Podcasts about vsphere

Recently I realized I hadn't had Bob Plankers on the show yet, so it was time to get him to join the show and talk all things security.Bob goes over all the various features we introduced related to security in VCF 9.1, and also gives a quick overview of what we've had available in VCF and vSphere for decades now. All the topics and links discussed can be found here:https://github.com/vmware/vcf-security-and-compliance-guidelineshttps://blogs.vmware.com/cloud-foundation/2025/08/06/confidential-computing-vmware-cloud-foundation-9-0/https://blogs.vmware.com/cloud-foundation/2025/08/05/security-vmware-cloud-foundation-9-0/https://blogs.vmware.com/cloud-foundation/2026/05/05/platform-security-vcf-9-1/

Broadcom continues to expand its role as a major contributor to cloud-native open source, particularly within the Cloud Native Computing Foundation (CNCF) ecosystem. Its recent donation of Velero—originally developed by VMware—to the CNCF Sandbox reflects a strategic move to foster broader community trust and collaboration. By shifting governance away from vendor control, Broadcom aims to position Velero as a truly community-driven data protection standard for Kubernetes environments, encouraging wider adoption and contribution.  At the same time, the company is reinforcing its position as a full-stack Kubernetes provider across both cloud-native and private cloud environments. Despite Kubernetes' dominance, many organizations still struggle with its complexity. Broadcom is addressing this by focusing on lifecycle management, long-term support, and deep integration with existing infrastructure like vSphere.  In a podcast recorded at KubeCon + CloudNativeCon Europe 2026, Dilpreet Bindra emphasized that open source success comes not just from code contributions, but also from relinquishing control to empower the broader ecosystem and drive sustainable innovation.  Learn more from The New Stack about the latest developments around Velero:  Broadcom donates Velero to CNCF — and it could reshape how Kubernetes users handle backup and disaster recovery  How AI Search Is Supporting Artistic Freedom  Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

This episode covers several major cybersecurity and tech news stories, including a sophisticated NPM supply chain attack that compromised the widely used Axios library through advanced social engineering, and the broader implications for software security. The hosts also discuss the accidental leak of Anthropic's Claude codebase, what it reveals about AI development practices, and the risks of misconfigurations exposing sensitive systems. Additional conversation touches on AI reliability, “vibe-coded” software, and the growing role of AI in both development and attack techniques.Join us LIVE on Mondays, 4:30pm EST.A weekly Podcast with BHIS and Friends. We discuss notable Infosec, and infosec-adjacent news stories gathered by our community news team.https://www.youtube.com/@BlackHillsInformationSecurityChat with us on Discord! - https://discord.gg/bhis

Recently, Frank published a series of blog posts on GPU resource management. I invited Frank to the show to explain why GPU resource management is different than CPU and memory management in vSphere. Frank goes over all the intricate details, and as always, dives deep into the various constructs involved. I highly recommend reading the articles which are part of the following series, and make sure to also check the tool Frank developed, as this helps visualizing the challenges you may face today, or potentially in the future!https://frankdenneman.ai/ai-infrastructure/https://frankdenneman.ai/understanding-ai-memory/https://frankdenneman.ai/tools/

Recently there was a big announcement about the adoption of vSAN Storage Clusters by Audi. All the reasons for me to invite Kalyan back to the show. During this episode, Kalyan not only discusses what was announced specifically with Audi, but also goes over some of the recently introduced functionality like vSAN traffic seperation, vSphere only clusters support for stretched with vSAN Storage Clusters, and much more.Some of the discussed papers and blogs:Audi case study: https://blogs.vmware.com/cloud-foundation/2025/03/27/audi-introduces-smart-manufacturing-with-vcf-edge/ Lower requirements for vSAN: https://blogs.vmware.com/cloud-foundation/2025/11/14/driving-down-storage-costs-with-lower-hardware-requirements-for-vsan/

Lee Caswell, senior vice president of product and solutions marketing at Nutanix Nutanix has published the 8th annual Enterprise Cloud Index, its flagship survey tracking how organizations are building and managing infrastructure. This year’s findings hit three themes that matter for the channel: the rapid spread of unmanaged AI tools, the growing weight of data sovereignty, and the accelerating shift toward containers. Lee Caswell, Nutanix’s senior vice president of product and solutions marketing, joins us to dig into the data. Lee spent years at VMware before joining Nutanix, giving him an unusual perspective on how the infrastructure market is reshaping itself – particularly as organizations navigate Broadcom’s changes to VMware alongside the push to build AI-ready environments. The numbers are striking: 79 per cent of respondents encounter AI tools deployed outside IT’s oversight, 80 per cent consider data sovereignty a top infrastructure priority, and 87 per cent expect containerization to increase. But Lee’s read goes beyond the headlines. On shadow AI, he argues most of this is rational behaviour by teams testing in the cloud before committing on-prem – the real challenge is providing a structured path, not clamping down. On sovereignty, he draws a memorable distinction between a “noisy neighbor” and a “nosy neighbor” in multi-tenant environments – a framing that matters for how MSPs position managed services around compliance. Lee, who recently wrote about what he calls the “sovereign edge”, goes deep on what sovereignty means in practice when AI workloads need to stay local. The conversation also explores the MSP opportunity. While 65 per cent of respondents say their AI runs via managed service providers, Lee candidly notes that figure includes SaaS-delivered AI. The bigger play, he argues, is MSPs becoming the “governed alternative” to shadow AI – a sanctioned service layer offering sovereignty compliance, optimal application placement, and predictable costs. His closing advice: be “AI smart,” not just “AI fast.” Read Full Transcript Robert Dutt: Hello and welcome to In The Channel from ChannelBuzz.ca, bringing news and information to Canadian IT solution providers for 16 years now. I’m Robert Dutt, editor of ChannelBuzz.ca, and as always your host for the show. If you’re an MSP, there’s a good chance your customers are already using AI tools that your team doesn’t know about. Nutanix recently released the 8th annual Enterprise Cloud Index, their big annual survey of how organizations are building and managing infrastructure. And this year, the data paints a picture that would be uncomfortable for anyone who thinks they’ve got a handle on where AI is running in their environment. Nearly 80% of respondents say they’ve encountered AI tools or agents deployed outside IT’s control. Data sovereignty has become a top priority, and containers are quietly becoming the default for new applications. My guest today is Lee Caswell, Nutanix’s senior vice president of product and solutions marketing. Lee came to Nutanix from VMware, so he’s been watching the infrastructure market reshape itself from a vantage point that very few people have. We dig into what the survey data actually says, where the contradictions are, and what it means for MSPs and solution providers. Here’s our conversation. Robert Dutt: Lee, thanks for taking the time. Lee Caswell: Well, Robert, thank you. Robert Dutt: You come to Nutanix from VMware, and your CEO now, Rajiv Ramaswami, he was the COO over there. Now you’re running this survey while the virtualization market is being reshaped by Broadcom’s changes. How does sitting where you sit now, having been kind of on both sides of that fence, shape how you look at this year’s data? Lee Caswell: Well, I think it’s fascinating that for years, maybe 20 years, people just assumed that the underlying virtualization layer was fixed. That vSphere was well established, super product, exciting. A lot of people built their careers, frankly, on learning the ins and outs of vSphere. And to a lesser extent, some of the later add-on products. But the idea that the underlying virtualization layer has changed has, for the first time in years, had people reconsidering how they will build out their IT infrastructure for the next 10 years. Robert Dutt: And we’ll circle back to that theme and that infrastructure theme a little later. But I wanted to dive in off the top into shadow AI, because it’s something that we’ve been talking about a fair bit on the podcast, and it’s something that a lot of partners are thinking about and trying to get their heads around how to deal with it. According to the survey, 79% of your respondents say they’re encountering AI tools or agents that are deployed outside the purview of IT. That’s a striking number. I’m curious, though, about the quality of the problem. Is this mostly folks who are using ChatGPT carelessly or without permission, or are we talking about the worst-case scenario of actual AI agents making business decisions willy-nilly without oversight? Lee Caswell: Well, we’ve certainly seen some of those later examples, but I think the majority of this is rational decision-making on IT and developer teams. Thinking about the fact that AI infrastructure itself can be relatively expensive. GPUs, new servers, new hardware. You’re generally bringing new hardware into the mix to start with. And what customers have been doing is before they go and make their investment strategy, and particularly in on-prem environments, they’ve been trying things out in the cloud where you can rent infrastructure, you can basically start something up, spin it down. That’s kind of a classic test-dev model, by the way, not different from what we’ve experienced in the past. And yet, when you look at how you’re going to deploy AI long-term with considerations around sovereignty and privacy, and particularly around predictable and lower costs, you start thinking about how you can take your on-prem infrastructure skills, which could include a data center but might also include the edge, and start thinking about how do you bring your already-strapped IT teams into this? And from a channel perspective, it’s how do you leverage some of the skills where people have been trained, particularly on virtualization. We’ll come back to this in just a minute. And basically apply this now into the new world of AI LLMs, AI hardware, and containerized infrastructure running on VMs. Robert Dutt: So if I’m an MSP supporting that kind of mid-market client, the 200 to 1,000 seat kind of space, what does a practical response to shadow AI look like at this moment in time? Because, you know, “implement an AI governance framework,” that’s great in concept, but that’s the kind of consulting engagement that’s a little hard for a lot of MSPs to deliver. Lee Caswell: Well, first off, you want to start thinking about what are the risks you’re trying to address. One is you want to look carefully at what LLMs your user base is actually using. One of the things that we’ve been able to do, for example, is have an audit trail, so you can look at who’s using DeepSeek, for example. Who’s using OpenAI? Who’s using some of the Llama 2, Llama 3 models, for example, or NVIDIA models? So the ability to go and look into the user base and get an assessment of that. Secondly, you’re looking at how do you make sure you don’t have a runaway cost model? This was one of the risks in the early cloud days, you remember. You had users getting shocked with the amount of unplanned, unmanaged cloud costs. And so you’ve got this opportunity now to look at how do you manage a brand new metric of consumption, by the way, called a token. I defy you to find somebody who knows exactly how tokens are created and the like. That’s a very difficult challenge. If you can provide a predictable way to manage, monitor, and control the usage of tokens, we do that as a way to basically protect against runaway costs. And then finally, the idea of sovereignty. So where is your data? Specifically, as you look at geopolitical considerations, we have, I think, a stunning finding that showed that 57% of our respondents said that they wanted their AI workloads to be within a sovereign country. Now, that doesn’t mean a single location necessarily, but it does show the concerns around where’s my data? Who can subpoena my data? Who’s got access to my data? And it may be, Robert, that the data model is more sensitive than the data itself, because the data model shows how you’re interpreting the data. And that’s actually a really interesting finding, I think, for a lot of folks, as AI takes hold so quickly. Robert Dutt: And data sovereignty is an area that we want to drill down on. It’s an area that’s of key interest to our audience, obviously. You touch on the 57% number in terms of how customers want infrastructure in a single country. 80% say it’s a high priority. You wrote recently about what you called the “sovereign edge,” the idea that AI is forcing compute closer to data within sovereign boundaries. For a Canadian audience that’s been navigating this between different regulation at different levels, the US hyperscalers and the CLOUD Act, for years, what’s new here? Is this kind of validation that what they’re seeing is real, or is the ground really shifting here? Lee Caswell: I think the sensitivity is a continuation of the trends that we’ve seen in the past. What’s changed is the understanding that in an AI world, data will be more distributed than it is today. And so imagine if you’re a hydro company, let’s say. And you’ve got different dams and facilities and hydro control points. These are distributed. They need to be able to run in a disconnected manner. You want to have AI applied locally. If you’re doing things around video processing, you don’t want to send all that data back to a central location. And so the ability to have a distributed model where your data and apps are more distributed and yet be connected so that you can do patching, for example, day-two operations, security updates, and push those out to a distributed environment. Now the realization is sovereignty has grown in importance, and at the same time, my data and applications will be more distributed. That’s a double stressor for IT teams looking at how to maintain that control and let the agility of distributed operations continue on. Robert Dutt: So are you seeing organizations redefine sovereignty in terms of operational control rather than just “the data lives here”? Because I think that distinction can matter pretty significantly for how MSPs ultimately architect their solutions and try to address this challenge. Lee Caswell: Yeah, I think for MSPs, there’s a few important areas to think through. One is that customers who were looking for, let’s say, an infrastructure link are now looking for an AI dial tone. They’re expecting to have AI available, always on, no matter where they are, accessing it for their users. Because AI is quickly, as you can see from the data here, becoming a top corporate priority. So that’s one thing. The second one is that the sovereignty means you need to make sure you’re controlling where is your data replicated to? Where does DR happen? How do you fail back within sovereign boundaries? Being able to establish that, something where the data services, something that you can establish or set as a differentiated capability, has been extremely important. And then lastly, you start thinking about what about within the MSP? There’s a noisy neighbor issue, but there’s a nosy neighbor issue, which is how do I make sure that someone inside can’t cross boundaries internally in an MSP and look at your data being hosted in a common location? This is an area that you’re going to want to look carefully at multi-tenancy and how the infrastructure protects your data even when some of the infrastructure is shared across users. Robert Dutt: So let’s shift and talk about containers, because I think that’s one of the areas that’s impactful but kind of hard for the audience to act on immediately. You have 87% increasing their containerization, 83% building new apps in containers. For MSPs who are still living in a virtual machine-centered world, which is probably a lot of them at this point in time, what’s the practical on-ramp? And honestly, how urgent is this? Do they have years to kind of figure this out and re-strategize, or is this a situation where if you’re not there, you’re already behind? Lee Caswell: I think for many customers who are running traditional applications and let’s say they move from an owned data center into a service provider model, the idea is that the applications may not be changing as fast as the container world might have them think. However, what you’re seeing is that new applications are built with containers because developers benefit from running in containers. What we’re finding though is most customers, the far majority of containers, are running in VMs. And they run in VMs because you’re able to now get the benefits of software agility – develop apps faster, eliminate testing dependencies, be able to run in distributed environments more quickly. Those benefits are married or matched with the resiliency of the underlying infrastructure so that individual components can fail. You can have day-two operations intact, and you’ve got integrated privacy and security and sovereignty. The idea that you’re going to run these both – it turns out we allow customers to run containers depending on their use case. If they were going to run on a bare metal instance, they can. If they want to run in the public cloud on EKS, for example, they can run our container Kubernetes stack, take advantage of our orchestration capabilities, but they don’t have to. For many customers, the fastest path to adopting containers will be to run containers in VMs, very familiar to our users and to the service provider base. What we’re encouraging them to get ready for is that even if they weren’t considering containers for traditional workloads, the fast adoption of AI workloads will bring a requirement for supporting containers. Think carefully around how do you leverage the training you already have, resilient infrastructure, all the things that our teams have been able to protect their downside, and still get access to the upside of new AI applications. We think running containers in VMs actually makes that the fastest path to container adoption. Robert Dutt: On AI agents, the survey shows a great deal of optimism around them. The productivity gains, the new revenue streams, all of that. But you also note that, as we talked about before, 79% of organizations can’t quite figure out how to manage the tools their employees are already using. Can you walk me through that disconnect? How do you go from “we can’t govern what we have” to “let’s deploy autonomous agents”? Lee Caswell: Yeah. Well, as you start thinking about what people have realized about AI, first, most customers have figured out that AI training will happen in the public cloud and that training requires huge investments, large power outlays that can only be taken on by the development of the models by the largest hyperscalers and some sovereign nations themselves. And so customers have been looking at, “I’m going to take models,” but then they quickly realize that the ability to have these models be useful in a particular company environment is dependent on having access to proprietary data. Think of support. If you want to support a product, it’s not interesting to have support in a general sense. You want to have support for your products, things that you may not want to expose, internal documents that are proprietary and private to your specific company. So now what you’re doing is basically taking these models, giving them access to your private data. And now the idea is, “I’m going to be able to take that inferencing model,” which is what this is called. Taking inferencing means you can take advantage of a software platform that abstracts the new hardware that’s required, GPUs, and abstracts the different types of models that you may choose over time. And so this is where you have these different LLMs. The ability to access those – we certify and validate the leading models so that they will run on the GPUs that are certified by our OEM partners. And so what we’re doing is taking out the risks. Effectively, what you do is leverage all the expertise you have for building an enterprise-level application today, and now be able to assimilate GPUs at the hardware layer and new LLMs at the software layer. And we’ll make it operate exactly the same as what you have today. Robert Dutt: 65% say that their AI applications are running today via managed service providers. That’s a pretty validating number for our audience, except for maybe the few who are going to say, “Well, what about the other 35%?” But, you know, can’t please everyone every time. I want to push though on what running AI via MSP actually means in practice. Are we talking about infrastructure hosting, model development and management, governance and compliance? What’s the service that MSPs are actually delivering today versus what they should be thinking about building towards for the future as this evolves? Lee Caswell: Yeah, I think the numbers overstate a little bit about how much training and skill building has actually happened already, because this would include things like SaaS-delivered services. And as you think of SaaS-delivered services like Copilot or ServiceNow or Salesforce, you’ll have AI-enhanced SaaS services that can be delivered by a service provider. What we’re anticipating and preparing service providers for is the idea that customers will, as they have private data to run their private models, be requiring dedicated equipment or provided services that give access to GPUs and LLMs that are beyond a SaaS-level model and now are actually specific applications for specific customer use case models. Robert Dutt: We talked about shadow AI a little earlier. I’m curious, speaking of future states for MSPs, is there a world where the MSP becomes kind of the governed alternative to shadow AI? Essentially the sanctioned AI service layer? Because that seems like a bigger play and a little bit harder to get your head around, but a bigger opportunity than just, “Hey, we host applications on GPUs now as well as CPUs.” Lee Caswell: I think so. And I think there’s a terrific both revenue and profit opportunity for service providers around this. First, there’s a services aspect of thinking about where do these applications run? Do they run in one location? Do they run across the hybrid cloud? So for anyone who’s working with cloud providers, how do I bridge this world out to this sovereign edge as we talked about? So that idea of how do I optimally locate applications, AI applications, and their associated data – that’s a very interesting workflow model to start with. And then next up, I think, is the idea of, well, where and how do I maintain sovereignty within this model? Service providers have a terrific opportunity to say, “Here are the limits within which your data and applications can move. And I’m going to provide that and give you some audit capabilities to manage any compliance risks that you have.” So terrific opportunity, I think, for service providers to become, as you mentioned, that governed alternative. And then finally, the idea that you would have a predictable cost model with tokens that allow you to share GPU resources means not just predictable, but lower cost than having an unpredictable model from the hyperscalers. We think this is actually a really compelling opportunity for service providers going forward. Robert Dutt: Can’t let you go without asking this one directly. A lot of our audience is in the middle of evaluating their virtualization platforms because of what’s happened with Broadcom and VMware. Within the survey data, is there anything about how those infrastructure decisions intersect with AI and sovereignty, the things we’ve been talking about, that you’d like to share? Are organizations treating this transition and the AI buildout as separate projects, or do things start to connect in an overall infrastructure refresh rethink? Lee Caswell: Well, I think some of the excitement from a service provider standpoint should be based on modeling or following what’s happening with the largest hyperscalers. I mean, you’re watching hyperscalers build out tens of billions of dollars of capital per month. We’ve never seen anything like this happen. And so that model, at a hyperscaler level, now what you’re thinking about is 82% from the survey of our respondents felt that their infrastructure was not fully ready for AI. And so building this out – I called this an AI dial tone earlier. The idea that similar to how you remember, Robert, how when you went to hotels, when Wi-Fi came along, all of a sudden Wi-Fi became de rigueur. You had to have it. If it wasn’t fast enough, people knew right away and responded very quickly. My view is we’re going to have exactly the same response to having fast, secure, and managed AI dial tones, if you will, for AI workloads, where you can apply your custom data or your private data and do that quickly using skills that you already have. For me, that means using a platform based on servers, based on certified GPUs, getting access to a changing set and world of LLMs. And being able to abstract both the hardware elements and the software elements means that you’re going to have customers be able to take all of the fast-changing AI world and bring it to their business problems more quickly. Robert Dutt: Before we wrap, a couple of lightning round questions, if you will. If a Canadian MSP is listening to this and thinking, “Okay, I need to do something differently,” what’s kind of the one thing based on what this data is showing that you’d tell them to prioritize in the next 12 months in terms of transforming their business? Lee Caswell: Yeah, I’d say number one is AI is coming. So prepare yourself. If you think you can get started nicely with small clusters, for example – one of the nice things about the Nutanix model is you can start small and grow from there. So start small, get a usable cluster ready for customers so they can try out how they can assimilate new GPU hardware, new AI LLMs. I think that’s essential. Also, in the process, what will happen is they’ll get experience with this new world of containers without giving up their virtualization expertise. That’s an extremely important step. If you try and do everything at once, it can be a lot. There are competitive solutions that force you to go to a Kubernetes-oriented management model. That’s a step too far for most service providers. If you think now what you could do instead is leverage your familiar virtualization skills, bring in the containerization, and allow customers to get started on shared infrastructure with a predictable cost. That’s a winning strategy for providing an on-ramp to AI with the lowest risk and a fast uptake. Robert Dutt: All right. And finally, so that the MSP audience can kind of keep an eye on what they need to keep in mind on the customer side, what’s the most dangerous assumption that you see IT leaders making right now about AI infrastructure? Lee Caswell: I think the most concerning thing I see is customers who are racing to be AI fast without being AI smart. And we saw some of this in the early days of the cloud. We remember “cloud first” versus “cloud smart.” And what happened was you had blown-up costs, you had programs that weren’t successful. But I’d say the most important thing actually has nothing to do with the infrastructure itself. It has to do with corporate management making sure that the application of AI is tied to a specific business problem. That’s the most important element. This is the thing I look for first. If you’re trying to solve an important business problem where you can ideally show that you can save money, generate more revenue, or do things more efficiently, those are the areas where you say AI is going to help here. Don’t just apply AI because it’s cool. Apply it because it’s going to solve a business problem, and you’ll find that you can actually move any infrastructure. We’ll bring that and make that work for you. Robert Dutt: Once again, it all kind of flows back to business outcomes. That’s great advice. I love that. Lee, thanks so much for taking the time. I appreciate it. Lee Caswell: Robert, I really appreciate it. Thank you. Robert Dutt: There you have it. Lee Caswell from Nutanix on their 8th annual Enterprise Cloud Index. A couple of things I’d like to flag from that conversation. Lee’s distinction between a noisy neighbor and a nosy neighbor when it comes to multi-tenant environments and data sovereignty – that’s a framing worth sitting with if you’re thinking about how to position managed services around compliance. And his point about organizations racing to be AI fast without being AI smart – that’s one you can take directly to client conversations. We’ll have a link to the full Enterprise Cloud Index report in the show notes, as well as a full transcript of the conversation. Tomorrow on the show, AWS Canada celebrates 20 years of the cloud. I sat down with Eric Gales to talk about what that milestone looks like from a Canadian perspective, and we’ll be back next Monday to catch you up on the headlines with In Case You Missed It. Between now and then, we’d invite you to subscribe to or follow the podcast in your podcast app of choice. And if it lets you, please do leave a review. Until next time, I’m Robert Dutt for ChannelBuzz.ca, and I’ll see you in the channel.

Bryan Cantrill is the co-founder and CTO of Oxide Computer Company. We discuss why the biggest cloud providers don't use off the shelf hardware, how scaling data centers at samsung's scale exposed problems with hard drive firmware, how the values of NodeJS are in conflict with robust systems, choosing Rust, and the benefits of Oxide Computer's rack scale approach. This is an extended version of an interview posted on Software Engineering Radio. Related links Oxide Computer Oxide and Friends Illumos Platform as a Reflection of Values RFD 26 bhyve CockroachDB Heterogeneous Computing with Raja Koduri Transcript You can help correct transcripts on GitHub. Intro [00:00:00] Jeremy: Today I am talking to Bryan Cantrill. He's the co-founder and CTO of Oxide computer company, and he was previously the CTO of Joyent and he also co-authored the DTrace Tracing framework while he was at Sun Microsystems. [00:00:14] Jeremy: Bryan, welcome to Software Engineering radio. [00:00:17] Bryan: Uh, awesome. Thanks for having me. It's great to be here. [00:00:20] Jeremy: You're the CTO of a company that makes computers. But I think before we get into that, a lot of people who built software, now that the actual computer is abstracted away, they're using AWS or they're using some kind of cloud service. So I thought we could start by talking about, data centers. [00:00:41] Jeremy: 'cause you were. Previously working at Joyent, and I believe you got bought by Samsung and you've previously talked about how you had to figure out, how do I run things at Samsung's scale. So how, how, how was your experience with that? What, what were the challenges there? Samsung scale and migrating off the cloud [00:01:01] Bryan: Yeah, I mean, so at Joyent, and so Joyent was a cloud computing pioneer. Uh, we competed with the likes of AWS and then later GCP and Azure. Uh, and we, I mean, we were operating at a scale, right? We had a bunch of machines, a bunch of dcs, but ultimately we know we were a VC backed company and, you know, a small company by the standards of, certainly by Samsung standards. [00:01:25] Bryan: And so when, when Samsung bought the company, I mean, the reason by the way that Samsung bought Joyent is Samsung's. Cloud Bill was, uh, let's just say it was extremely large. They were spending an enormous amount of money every year on, on the public cloud. And they realized that in order to secure their fate economically, they had to be running on their own infrastructure. [00:01:51] Bryan: It did not make sense. And there's not, was not really a product that Samsung could go buy that would give them that on-prem cloud. Uh, I mean in that, in that regard, like the state of the market was really no different. And so they went looking for a company, uh, and bought, bought Joyent. And when we were on the inside of Samsung. [00:02:11] Bryan: That we learned about Samsung scale. And Samsung loves to talk about Samsung scale. And I gotta tell you, it is more than just chest thumping. Like Samsung Scale really is, I mean, just the, the sheer, the number of devices, the number of customers, just this absolute size. they really wanted to take us out to, to levels of scale, certainly that we had not seen. [00:02:31] Bryan: The reason for buying Joyent was to be able to stand up on their own infrastructure so that we were gonna go buy, we did go buy a bunch of hardware. Problems with server hardware at scale [00:02:40] Bryan: And I remember just thinking, God, I hope Dell is somehow magically better. I hope the problems that we have seen in the small, we just. You know, I just remember hoping and hope is hope. It was of course, a terrible strategy and it was a terrible strategy here too. Uh, and the we that the problems that we saw at the large were, and when you scale out the problems that you see kind of once or twice, you now see all the time and they become absolutely debilitating. [00:03:12] Bryan: And we saw a whole series of really debilitating problems. I mean, many ways, like comically debilitating, uh, in terms of, of showing just how bad the state-of-the-art. Yes. And we had, I mean, it should be said, we had great software and great software expertise, um, and we were controlling our own system software. [00:03:35] Bryan: But even controlling your own system software, your own host OS, your own control plane, which is what we had at Joyent, ultimately, you're pretty limited. You go, I mean, you got the problems that you can obviously solve, the ones that are in your own software, but the problems that are beneath you, the, the problems that are in the hardware platform, the problems that are in the componentry beneath you become the problems that are in the firmware. IO latency due to hard drive firmware [00:04:00] Bryan: Those problems become unresolvable and they are deeply, deeply frustrating. Um, and we just saw a bunch of 'em again, they were. Comical in retrospect, and I'll give you like a, a couple of concrete examples just to give, give you an idea of what kinda what you're looking at. one of the, our data centers had really pathological IO latency. [00:04:23] Bryan: we had a very, uh, database heavy workload. And this was kind of right at the period where you were still deploying on rotating media on hard drives. So this is like, so. An all flash buy did not make economic sense when we did this in, in 2016. This probably, it'd be interesting to know like when was the, the kind of the last time that that actual hard drives made sense? [00:04:50] Bryan: 'cause I feel this was close to it. So we had a, a bunch of, of a pathological IO problems, but we had one data center in which the outliers were actually quite a bit worse and there was so much going on in that system. It took us a long time to figure out like why. And because when, when you, when you're io when you're seeing worse io I mean you're naturally, you wanna understand like what's the workload doing? [00:05:14] Bryan: You're trying to take a first principles approach. What's the workload doing? So this is a very intensive database workload to support the, the object storage system that we had built called Manta. And that the, the metadata tier was stored and uh, was we were using Postgres for that. And that was just getting absolutely slaughtered. [00:05:34] Bryan: Um, and ultimately very IO bound with these kind of pathological IO latencies. Uh, and as we, you know, trying to like peel away the layers to figure out what was going on. And I finally had this thing. So it's like, okay, we are seeing at the, at the device layer, at the at, at the disc layer, we are seeing pathological outliers in this data center that we're not seeing anywhere else. [00:06:00] Bryan: And that does not make any sense. And the thought occurred to me. I'm like, well, maybe we are. Do we have like different. Different rev of firmware on our HGST drives, HGST. Now part of WD Western Digital were the drives that we had everywhere. And, um, so maybe we had a different, maybe I had a firmware bug. [00:06:20] Bryan: I, this would not be the first time in my life at all that I would have a drive firmware issue. Uh, and I went to go pull the firmware, rev, and I'm like, Toshiba makes hard drives? So we had, I mean. I had no idea that Toshiba even made hard drives, let alone that they were our, they were in our data center. [00:06:38] Bryan: I'm like, what is this? And as it turns out, and this is, you know, part of the, the challenge when you don't have an integrated system, which not to pick on them, but Dell doesn't, and what Dell would routinely put just sub make substitutes, and they make substitutes that they, you know, it's kind of like you're going to like, I don't know, Instacart or whatever, and they're out of the thing that you want. [00:07:03] Bryan: So, you know, you're, someone makes a substitute and like sometimes that's okay, but it's really not okay in a data center. And you really want to develop and validate a, an end-to-end integrated system. And in this case, like Toshiba doesn't, I mean, Toshiba does make hard drives, but they are a, or the data they did, uh, they basically were, uh, not competitive and they were not competitive in part for the reasons that we were discovering. [00:07:29] Bryan: They had really serious firmware issues. So the, these were drives that would just simply stop a, a stop acknowledging any reads from the order of 2,700 milliseconds. Long time, 2.7 seconds. Um. And that was a, it was a drive firmware issue, but it was highlighted like a much deeper issue, which was the simple lack of control that we had over our own destiny. [00:07:53] Bryan: Um, and it's an, it's, it's an example among many where Dell is making a decision. That lowers the cost of what they are providing you marginally, but it is then giving you a system that they shouldn't have any confidence in because it's not one that they've actually designed and they leave it to the customer, the end user, to make these discoveries. [00:08:18] Bryan: And these things happen up and down the stack. And for every, for whether it's, and, and not just to pick on Dell because it's, it's true for HPE, it's true for super micro, uh, it's true for your switch vendors. It's, it's true for storage vendors where the, the, the, the one that is left actually integrating these things and trying to make the the whole thing work is the end user sitting in their data center. AWS / Google are not buying off the shelf hardware but you can't use it [00:08:42] Bryan: There's not a product that they can buy that gives them elastic infrastructure, a cloud in their own DC The, the product that you buy is the public cloud. Like when you go in the public cloud, you don't worry about the stuff because that it's, it's AWS's issue or it's GCP's issue. And they are the ones that get this to ground. [00:09:02] Bryan: And they, and this was kind of, you know, the eye-opening moment. Not a surprise. Uh, they are not Dell customers. They're not HPE customers. They're not super micro customers. They have designed their own machines. And to varying degrees, depending on which one you're looking at. But they've taken the clean sheet of paper and the frustration that we had kind of at Joyent and beginning to wonder and then Samsung and kind of wondering what was next, uh, is that, that what they built was not available for purchase in the data center. [00:09:35] Bryan: You could only rent it in the public cloud. And our big belief is that public cloud computing is a really important revolution in infrastructure. Doesn't feel like a different, a deep thought, but cloud computing is a really important revolution. It shouldn't only be available to rent. You should be able to actually buy it. [00:09:53] Bryan: And there are a bunch of reasons for doing that. Uh, one in the one we we saw at Samsung is economics, which I think is still the dominant reason where it just does not make sense to rent all of your compute in perpetuity. But there are other reasons too. There's security, there's risk management, there's latency. [00:10:07] Bryan: There are a bunch of reasons why one might wanna to own one's own infrastructure. But, uh, that was very much the, the, so the, the genesis for oxide was coming out of this very painful experience and a painful experience that, because, I mean, a long answer to your question about like what was it like to be at Samsung scale? [00:10:27] Bryan: Those are the kinds of things that we, I mean, in our other data centers, we didn't have Toshiba drives. We only had the HDSC drives, but it's only when you get to this larger scale that you begin to see some of these pathologies. But these pathologies then are really debilitating in terms of those who are trying to develop a service on top of them. [00:10:45] Bryan: So it was, it was very educational in, in that regard. And you're very grateful for the experience at Samsung in terms of opening our eyes to the challenge of running at that kind of scale. [00:10:57] Jeremy: Yeah, because I, I think as software engineers, a lot of times we, we treat the hardware as a, as a given where, [00:11:08] Bryan: Yeah. [00:11:08] Bryan: Yeah. There's software in chard drives [00:11:09] Jeremy: It sounds like in, in this case, I mean, maybe the issue is not so much that. Dell or HP as a company doesn't own every single piece that they're providing you, but rather the fact that they're swapping pieces in and out without advertising them, and then when it becomes a problem, they're not necessarily willing to, to deal with the, the consequences of that. [00:11:34] Bryan: They just don't know. I mean, I think they just genuinely don't know. I mean, I think that they, it's not like they're making a deliberate decision to kind of ship garbage. It's just that they are making, I mean, I think it's exactly what you said about like, not thinking about the hardware. It's like, what's a hard drive? [00:11:47] Bryan: Like what's it, I mean, it's a hard drive. It's got the same specs as this other hard drive and Intel. You know, it's a little bit cheaper, so why not? It's like, well, like there's some reasons why not, and one of the reasons why not is like, uh, even a hard drive, whether it's rotating media or, or flash, like that's not just hardware. [00:12:05] Bryan: There's software in there. And that the software's like not the same. I mean, there are components where it's like, there's actually, whether, you know, if, if you're looking at like a resistor or a capacitor or something like this Yeah. If you've got two, two parts that are within the same tolerance. Yeah. [00:12:19] Bryan: Like sure. Maybe, although even the EEs I think would be, would be, uh, objecting that a little bit. But the, the, the more complicated you get, and certainly once you get to the, the, the, the kind of the hardware that we think of like a, a, a microprocessor, a a network interface card, a a, a hard driver, an NVME drive. [00:12:38] Bryan: Those things are super complicated and there's a whole bunch of software inside of those things, the firmware, and that's the stuff that, that you can't, I mean, you say that software engineers don't think about that. It's like you, no one can really think about that because it's proprietary that's kinda welded shut and you've got this abstraction into it. [00:12:55] Bryan: But the, the way that thing operates is very core to how the thing in aggregate will behave. And I think that you, the, the kind of, the, the fundamental difference between Oxide's approach and the approach that you get at a Dell HP Supermicro, wherever, is really thinking holistically in terms of hardware and software together in a system that, that ultimately delivers cloud computing to a user. [00:13:22] Bryan: And there's a lot of software at many, many, many, many different layers. And it's very important to think about, about that software and that hardware holistically as a single system. [00:13:34] Jeremy: And during that time at Joyent, when you experienced some of these issues, was it more of a case of you didn't have enough servers experiencing this? So if it would happen, you might say like, well, this one's not working, so maybe we'll just replace the hardware. What, what was the thought process when you were working at that smaller scale and, and how did these issues affect you? UEFI / Baseboard Management Controller [00:13:58] Bryan: Yeah, at the smaller scale, you, uh, you see fewer of them, right? You just see it's like, okay, we, you know, what you might see is like, that's weird. We kinda saw this in one machine versus seeing it in a hundred or a thousand or 10,000. Um, so you just, you just see them, uh, less frequently as a result, they are less debilitating. [00:14:16] Bryan: Um, I, I think that it's, when you go to that larger scale, those things that become, that were unusual now become routine and they become debilitating. Um, so it, it really is in many regards a function of scale. Uh, and then I think it was also, you know, it was a little bit dispiriting that kind of the substrate we were building on really had not improved. [00:14:39] Bryan: Um, and if you look at, you know, the, if you buy a computer server, buy an x86 server. There is a very low layer of firmware, the BIOS, the basic input output system, the UEFI BIOS, and this is like an abstraction layer that has, has existed since the eighties and hasn't really meaningfully improved. Um, the, the kind of the transition to UEFI happened with, I mean, I, I ironically with Itanium, um, you know, two decades ago. [00:15:08] Bryan: but beyond that, like this low layer, this lowest layer of platform enablement software is really only impeding the operability of the system. Um, you look at the baseboard management controller, which is the kind of the computer within the computer, there is a, uh, there is an element in the machine that needs to handle environmentals, that needs to handle, uh, operate the fans and so on. [00:15:31] Bryan: Uh, and that traditionally has this, the space board management controller, and that architecturally just hasn't improved in the last two decades. And, you know, that's, it's a proprietary piece of silicon. Generally from a company that no one's ever heard of called a Speed, uh, which has to be, is written all on caps, so I guess it needs to be screamed. [00:15:50] Bryan: Um, a speed has a proprietary part that has a, there is a root password infamously there, is there, the root password is encoded effectively in silicon. So, uh, which is just, and for, um, anyone who kind of goes deep into these things, like, oh my God, are you kidding me? Um, when we first started oxide, the wifi password was a fraction of the a speed root password for the bmc. [00:16:16] Bryan: It's kinda like a little, little BMC humor. Um, but those things, it was just dispiriting that, that the, the state-of-the-art was still basically personal computers running in the data center. Um, and that's part of what, what was the motivation for doing something new? [00:16:32] Jeremy: And for the people using these systems, whether it's the baseboard management controller or it's the The BIOS or UF UEFI component, what are the actual problems that people are seeing seen? Security vulnerabilities and poor practices in the BMC [00:16:51] Bryan: Oh man, I, the, you are going to have like some fraction of your listeners, maybe a big fraction where like, yeah, like what are the problems? That's a good question. And then you're gonna have the people that actually deal with these things who are, did like their heads already hit the desk being like, what are the problems? [00:17:06] Bryan: Like what are the non problems? Like what, what works? Actually, that's like a shorter answer. Um, I mean, there are so many problems and a lot of it is just like, I mean, there are problems just architecturally these things are just so, I mean, and you could, they're the problems spread to the horizon, so you can kind of start wherever you want. [00:17:24] Bryan: But I mean, as like, as a really concrete example. Okay, so the, the BMCs that, that the computer within the computer that needs to be on its own network. So you now have like not one network, you got two networks that, and that network, by the way, it, that's the network that you're gonna log into to like reset the machine when it's otherwise unresponsive. [00:17:44] Bryan: So that going into the BMC, you can are, you're able to control the entire machine. Well it's like, alright, so now I've got a second net network that I need to manage. What is running on the BMC? Well, it's running some. Ancient, ancient version of Linux it that you got. It's like, well how do I, how do I patch that? [00:18:02] Bryan: How do I like manage the vulnerabilities with that? Because if someone is able to root your BMC, they control the system. So it's like, this is not you've, and now you've gotta go deal with all of the operational hair around that. How do you upgrade that system updating the BMC? I mean, it's like you've got this like second shadow bad infrastructure that you have to go manage. [00:18:23] Bryan: Generally not open source. There's something called open BMC, um, which, um, you people use to varying degrees, but you're generally stuck with the proprietary BMC, so you're generally stuck with, with iLO from HPE or iDRAC from Dell or, or, uh, the, uh, su super micros, BMC, that H-P-B-M-C, and you are, uh, it is just excruciating pain. [00:18:49] Bryan: Um, and that this is assuming that by the way, that everything is behaving correctly. The, the problem is that these things often don't behave correctly, and then the consequence of them not behaving correctly. It's really dire because it's at that lowest layer of the system. So, I mean, I'll give you a concrete example. [00:19:07] Bryan: a customer of theirs reported to me, so I won't disclose the vendor, but let's just say that a well-known vendor had an issue with their, their temperature sensors were broken. Um, and the thing would always read basically the wrong value. So it was the BMC that had to like, invent its own ki a different kind of thermal control loop. [00:19:28] Bryan: And it would index on the, on the, the, the, the actual inrush current. It would, they would look at that at the current that's going into the CPU to adjust the fan speed. That's a great example of something like that's a, that's an interesting idea. That doesn't work. 'cause that's actually not the temperature. [00:19:45] Bryan: So like that software would crank the fans whenever you had an inrush of current and this customer had a workload that would spike the current and by it, when it would spike the current, the, the, the fans would kick up and then they would slowly degrade over time. Well, this workload was spiking the current faster than the fans would degrade, but not fast enough to actually heat up the part. [00:20:08] Bryan: And ultimately over a very long time, in a very painful investigation, it's customer determined that like my fans are cranked in my data center for no reason. We're blowing cold air. And it's like that, this is on the order of like a hundred watts, a server of, of energy that you shouldn't be spending and like that ultimately what that go comes down to this kind of broken software hardware interface at the lowest layer that has real meaningful consequence, uh, in terms of hundreds of kilowatts, um, across a data center. So this stuff has, has very, very, very real consequence and it's such a shadowy world. Part of the reason that, that your listeners that have dealt with this, that our heads will hit the desk is because it is really aggravating to deal with problems with this layer. [00:21:01] Bryan: You, you feel powerless. You don't control or really see the software that's on them. It's generally proprietary. You are relying on your vendor. Your vendor is telling you that like, boy, I don't know. You're the only customer seeing this. I mean, the number of times I have heard that for, and I, I have pledged that we're, we're not gonna say that at oxide because it's such an unaskable thing to say like, you're the only customer saying this. [00:21:25] Bryan: It's like, it feels like, are you blaming me for my problem? Feels like you're blaming me for my problem? Um, and what you begin to realize is that to a degree, these folks are speaking their own truth because the, the folks that are running at real scale at Hyperscale, those folks aren't Dell, HP super micro customers. [00:21:46] Bryan: They're actually, they've done their own thing. So it's like, yeah, Dell's not seeing that problem, um, because they're not running at the same scale. Um, but when you do run, you only have to run at modest scale before these things just become. Overwhelming in terms of the, the headwind that they present to people that wanna deploy infrastructure. The problem is felt with just a few racks [00:22:05] Jeremy: Yeah, so maybe to help people get some perspective at, at what point do you think that people start noticing or start feeling these problems? Because I imagine that if you're just have a few racks or [00:22:22] Bryan: do you have a couple racks or the, or do you wonder or just wondering because No, no, no. I would think, I think anyone who deploys any number of servers, especially now, especially if your experience is only in the cloud, you're gonna be like, what the hell is this? I mean, just again, just to get this thing working at all. [00:22:39] Bryan: It is so it, it's so hairy and so congealed, right? It's not designed. Um, and it, it, it, it's accreted it and it's so obviously accreted that you are, I mean, nobody who is setting up a rack of servers is gonna think to themselves like, yes, this is the right way to go do it. This all makes sense because it's, it's just not, it, I, it feels like the kit, I mean, kit car's almost too generous because it implies that there's like a set of plans to work to in the end. [00:23:08] Bryan: Uh, I mean, it, it, it's a bag of bolts. It's a bunch of parts that you're putting together. And so even at the smallest scales, that stuff is painful. Just architecturally, it's painful at the small scale then, but at least you can get it working. I think the stuff that then becomes debilitating at larger scale are the things that are, are worse than just like, I can't, like this thing is a mess to get working. [00:23:31] Bryan: It's like the, the, the fan issue that, um, where you are now seeing this over, you know, hundreds of machines or thousands of machines. Um, so I, it is painful at more or less all levels of scale. There's, there is no level at which the, the, the pc, which is really what this is, this is a, the, the personal computer architecture from the 1980s and there is really no level of scale where that's the right unit. Running elastic infrastructure is the hardware but also, hypervisor, distributed database, api, etc [00:23:57] Bryan: I mean, where that's the right thing to go deploy, especially if what you are trying to run. Is elastic infrastructure, a cloud. Because the other thing is like we, we've kinda been talking a lot about that hardware layer. Like hardware is, is just the start. Like you actually gotta go put software on that and actually run that as elastic infrastructure. [00:24:16] Bryan: So you need a hypervisor. Yes. But you need a lot more than that. You, you need to actually, you, you need a distributed database, you need web endpoints. You need, you need a CLI, you need all the stuff that you need to actually go run an actual service of compute or networking or storage. I mean, and for, for compute, even for compute, there's a ton of work to be done. [00:24:39] Bryan: And compute is by far, I would say the simplest of the, of the three. When you look at like networks, network services, storage services, there's a whole bunch of stuff that you need to go build in terms of distributed systems to actually offer that as a cloud. So it, I mean, it is painful at more or less every LE level if you are trying to deploy cloud computing on. What's a control plane? [00:25:00] Jeremy: And for someone who doesn't have experience building or working with this type of infrastructure, when you talk about a control plane, what, what does that do in the context of this system? [00:25:16] Bryan: So control plane is the thing that is, that is everything between your API request and that infrastructure actually being acted upon. So you go say, Hey, I, I want a provision, a vm. Okay, great. We've got a whole bunch of things we're gonna provision with that. We're gonna provision a vm, we're gonna get some storage that's gonna go along with that, that's got a network storage service that's gonna come out of, uh, we've got a virtual network that we're gonna either create or attach to. [00:25:39] Bryan: We've got a, a whole bunch of things we need to go do for that. For all of these things, there are metadata components that need, we need to keep track of this thing that, beyond the actual infrastructure that we create. And then we need to go actually, like act on the actual compute elements, the hostos, what have you, the switches, what have you, and actually go. [00:25:56] Bryan: Create these underlying things and then connect them. And there's of course, the challenge of just getting that working is a big challenge. Um, but getting that working robustly, getting that working is, you know, when you go to provision of vm, um, the, all the, the, the steps that need to happen and what happens if one of those steps fails along the way? [00:26:17] Bryan: What happens if, you know, one thing we're very mindful of is these kind of, you get these long tails of like, why, you know, generally our VM provisioning happened within this time, but we get these long tails where it takes much longer. What's going on? What, where in this process are we, are we actually spending time? [00:26:33] Bryan: Uh, and there's a whole lot of complexity that you need to go deal with that. There's a lot of complexity that you need to go deal with this effectively, this workflow that's gonna go create these things and manage them. Um, we use a, a pattern that we call, that are called sagas, actually is a, is a database pattern from the eighties. [00:26:51] Bryan: Uh, Katie McCaffrey is a, is a database reCrcher who, who, uh, I, I think, uh, reintroduce the idea of, of sagas, um, in the last kind of decade. Um, and this is something that we picked up, um, and I've done a lot of really interesting things with, um, to allow for, to this kind of, these workflows to be, to be managed and done so robustly in a way that you can restart them and so on. [00:27:16] Bryan: Uh, and then you guys, you get this whole distributed system that can do all this. That whole distributed system, that itself needs to be reliable and available. So if you, you know, you need to be able to, what happens if you, if you pull a sled or if a sled fails, how does the system deal with that? [00:27:33] Bryan: How does the system deal with getting an another sled added to the system? Like how do you actually grow this distributed system? And then how do you update it? How do you actually go from one version to the next? And all of that has to happen across an air gap where this is gonna run as part of the computer. [00:27:49] Bryan: So there are, it, it is fractally complicated. There, there is a lot of complexity here in, in software, in the software system and all of that. We kind of, we call the control plane. Um, and it, this is the what exists at AWS at GCP, at Azure. When you are hitting an endpoint that's provisioning an EC2 instance for you. [00:28:10] Bryan: There is an AWS control plane that is, is doing all of this and has, uh, some of these similar aspects and certainly some of these similar challenges. Are vSphere / Proxmox / Hyper-V in the same category? [00:28:20] Jeremy: And for people who have run their own servers with something like say VMware or Hyper V or Proxmox, are those in the same category? [00:28:32] Bryan: Yeah, I mean a little bit. I mean, it kind of like vSphere Yes. Via VMware. No. So it's like you, uh, VMware ESX is, is kind of a key building block upon which you can build something that is a more meaningful distributed system. When it's just like a machine that you're provisioning VMs on, it's like, okay, well that's actually, you as the human might be the control plane. [00:28:52] Bryan: Like, that's, that, that's, that's a much easier problem. Um, but when you've got, you know, tens, hundreds, thousands of machines, you need to do it robustly. You need something to coordinate that activity and you know, you need to pick which sled you land on. You need to be able to move these things. You need to be able to update that whole system. [00:29:06] Bryan: That's when you're getting into a control plane. So, you know, some of these things have kind of edged into a control plane, certainly VMware. Um, now Broadcom, um, has delivered something that's kind of cloudish. Um, I think that for folks that are truly born on the cloud, it, it still feels somewhat, uh, like you're going backwards in time when you, when you look at these kind of on-prem offerings. [00:29:29] Bryan: Um, but, but it, it, it's got these aspects to it for sure. Um, and I think that we're, um, some of these other things when you're just looking at KVM or just looks looking at Proxmox you kind of need to, to connect it to other broader things to turn it into something that really looks like manageable infrastructure. [00:29:47] Bryan: And then many of those projects are really, they're either proprietary projects, uh, proprietary products like vSphere, um, or you are really dealing with open source projects that are. Not necessarily aimed at the same level of scale. Um, you know, you look at a, again, Proxmox or, uh, um, you'll get an OpenStack. [00:30:05] Bryan: Um, and you know, OpenStack is just a lot of things, right? I mean, OpenStack has got so many, the OpenStack was kind of a, a free for all, for every infrastructure vendor. Um, and I, you know, there was a time people were like, don't you, aren't you worried about all these companies together that, you know, are coming together for OpenStack? [00:30:24] Bryan: I'm like, haven't you ever worked for like a company? Like, companies don't get along. By the way, it's like having multiple companies work together on a thing that's bad news, not good news. And I think, you know, one of the things that OpenStack has definitely struggled with, kind of with what, actually the, the, there's so many different kind of vendor elements in there that it's, it's very much not a product, it's a project that you're trying to run. [00:30:47] Bryan: But that's, but that very much is in, I mean, that's, that's similar certainly in spirit. [00:30:53] Jeremy: And so I think this is kind of like you're alluding to earlier, the piece that allows you to allocate, compute, storage, manage networking, gives you that experience of I can go to a web console or I can use an API and I can spin up machines, get them all connected. At the end of the day, the control plane. Is allowing you to do that in hopefully a user-friendly way. [00:31:21] Bryan: That's right. Yep. And in the, I mean, in order to do that in a modern way, it's not just like a user-friendly way. You really need to have a CLI and a web UI and an API. Those all need to be drawn from the same kind of single ground truth. Like you don't wanna have any of those be an afterthought for the other. [00:31:39] Bryan: You wanna have the same way of generating all of those different endpoints and, and entries into the system. Building a control plane now has better tools (Rust, CockroachDB) [00:31:46] Jeremy: And if you take your time at Joyent as an example. What kind of tools existed for that versus how much did you have to build in-house for as far as the hypervisor and managing the compute and all that? [00:32:02] Bryan: Yeah, so we built more or less everything in house. I mean, what you have is, um, and I think, you know, over time we've gotten slightly better tools. Um, I think, and, and maybe it's a little bit easier to talk about the, kind of the tools we started at Oxide because we kind of started with a, with a clean sheet of paper at oxide. [00:32:16] Bryan: We wanted to, knew we wanted to go build a control plane, but we were able to kind of go revisit some of the components. So actually, and maybe I'll, I'll talk about some of those changes. So when we, at, For example, at Joyent, when we were building a cloud at Joyent, there wasn't really a good distributed database. [00:32:34] Bryan: Um, so we were using Postgres as our database for metadata and there were a lot of challenges. And Postgres is not a distributed database. It's running. With a primary secondary architecture, and there's a bunch of issues there, many of which we discovered the hard way. Um, when we were coming to oxide, you have much better options to pick from in terms of distributed databases. [00:32:57] Bryan: You know, we, there was a period that now seems maybe potentially brief in hindsight, but of a really high quality open source distributed databases. So there were really some good ones to, to pick from. Um, we, we built on CockroachDB on CRDB. Um, so that was a really important component. That we had at oxide that we didn't have at Joyent. [00:33:19] Bryan: Um, so we were, I wouldn't say we were rolling our own distributed database, we were just using Postgres and uh, and, and dealing with an enormous amount of pain there in terms of the surround. Um, on top of that, and, and, you know, a, a control plane is much more than a database, obviously. Uh, and you've gotta deal with, uh, there's a whole bunch of software that you need to go, right. [00:33:40] Bryan: Um, to be able to, to transform these kind of API requests into something that is reliable infrastructure, right? And there, there's a lot to that. Uh, especially when networking gets in the mix, when storage gets in the mix, uh, there are a whole bunch of like complicated steps that need to be done, um, at Joyent. [00:33:59] Bryan: Um, we, in part because of the history of the company and like, look. This, this just is not gonna sound good, but it just is what it is and I'm just gonna own it. We did it all in Node, um, at Joyent, which I, I, I know it sounds really right now, just sounds like, well, you, you built it with Tinker Toys. You Okay. [00:34:18] Bryan: Uh, did, did you think it was, you built the skyscraper with Tinker Toys? Uh, it's like, well, okay. We actually, we had greater aspirations for the Tinker Toys once upon a time, and it was better than, you know, than Twisted Python and Event Machine from Ruby, and we weren't gonna do it in Java. All right. [00:34:32] Bryan: So, but let's just say that that experiment, uh, that experiment did ultimately end in a predictable fashion. Um, and, uh, we, we decided that maybe Node was not gonna be the best decision long term. Um, Joyent was the company behind node js. Uh, back in the day, Ryan Dahl worked for Joyent. Uh, and then, uh, then we, we, we. [00:34:53] Bryan: Uh, landed that in a foundation in about, uh, what, 2015, something like that. Um, and began to consider our world beyond, uh, beyond Node. Rust at Oxide [00:35:04] Bryan: A big tool that we had in the arsenal when we started Oxide is Rust. Um, and so indeed the name of the company is, is a tip of the hat to the language that we were pretty sure we were gonna be building a lot of stuff in. [00:35:16] Bryan: Namely Rust. And, uh, rust is, uh, has been huge for us, a very important revolution in programming languages. you know, there, there, there have been different people kind of coming in at different times and I kinda came to Rust in what I, I think is like this big kind of second expansion of rust in 2018 when a lot of technologists were think, uh, sick of Node and also sick of Go. [00:35:43] Bryan: And, uh, also sick of C++. And wondering is there gonna be something that gives me the, the, the performance, of that I get outta C. The, the robustness that I can get out of a C program but is is often difficult to achieve. but can I get that with kind of some, some of the velocity of development, although I hate that term, some of the speed of development that you get out of a more interpreted language. [00:36:08] Bryan: Um, and then by the way, can I actually have types, I think types would be a good idea? Uh, and rust obviously hits the sweet spot of all of that. Um, it has been absolutely huge for us. I mean, we knew when we started the company again, oxide, uh, we were gonna be using rust in, in quite a, quite a. Few places, but we weren't doing it by fiat. [00:36:27] Bryan: Um, we wanted to actually make sure we're making the right decision, um, at, at every different, at every layer. Uh, I think what has been surprising is the sheer number of layers at which we use rust in terms of, we've done our own embedded firmware in rust. We've done, um, in, in the host operating system, which is still largely in C, but very big components are in rust. [00:36:47] Bryan: The hypervisor Propolis is all in rust. Uh, and then of course the control plane, that distributed system on that is all in rust. So that was a very important thing that we very much did not need to build ourselves. We were able to really leverage, uh, a terrific community. Um. We were able to use, uh, and we've done this at Joyent as well, but at Oxide, we've used Illumos as a hostos component, which, uh, our variant is called Helios. [00:37:11] Bryan: Um, we've used, uh, bhyve um, as a, as as that kind of internal hypervisor component. we've made use of a bunch of different open source components to build this thing, um, which has been really, really important for us. Uh, and open source components that didn't exist even like five years prior. [00:37:28] Bryan: That's part of why we felt that 2019 was the right time to start the company. And so we started Oxide. The problems building a control plane in Node [00:37:34] Jeremy: You had mentioned that at Joyent, you had tried to build this in, in Node. What were the, what were the, the issues or the, the challenges that you had doing that? [00:37:46] Bryan: Oh boy. Yeah. again, we, I kind of had higher hopes in 2010, I would say. When we, we set on this, um, the, the, the problem that we had just writ large, um. JavaScript is really designed to allow as many people on earth to write a program as possible, which is good. I mean, I, I, that's a, that's a laudable goal. [00:38:09] Bryan: That is the goal ultimately of such as it is of JavaScript. It's actually hard to know what the goal of JavaScript is, unfortunately, because Brendan Ike never actually wrote a book. so that there is not a canonical, you've got kind of Doug Crockford and other people who've written things on JavaScript, but it's hard to know kind of what the original intent of JavaScript is. [00:38:27] Bryan: The name doesn't even express original intent, right? It was called Live Script, and it was kind of renamed to JavaScript during the Java Frenzy of the late nineties. A name that makes no sense. There is no Java in JavaScript. that is kind of, I think, revealing to kind of the, uh, the unprincipled mess that is JavaScript. [00:38:47] Bryan: It, it, it's very pragmatic at some level, um, and allows anyone to, it makes it very easy to write software. The problem is it's much more difficult to write really rigorous software. So, uh, and this is what I should differentiate JavaScript from TypeScript. This is really what TypeScript is trying to solve. [00:39:07] Bryan: TypeScript is like. How can, I think TypeScript is a, is a great step forward because TypeScript is like, how can we bring some rigor to this? Like, yes, it's great that it's easy to write JavaScript, but that's not, we, we don't wanna do that for Absolutely. I mean that, that's not the only problem we solve. [00:39:23] Bryan: We actually wanna be able to write rigorous software and it's actually okay if it's a little harder to write rigorous software that's actually okay if it gets leads to, to more rigorous artifacts. Um, but in JavaScript, I mean, just a concrete example. You know, there's nothing to prevent you from referencing a property that doesn't actually exist in JavaScript. [00:39:43] Bryan: So if you fat finger a property name, you are relying on something to tell you. By the way, I think you've misspelled this because there is no type definition for this thing. And I don't know that you've got one that's spelled correctly, one that's spelled incorrectly, that's often undefined. And then the, when you actually go, you say you've got this typo that is lurking in your what you want to be rigorous software. [00:40:07] Bryan: And if you don't execute that code, like you won't know that's there. And then you do execute that code. And now you've got a, you've got an undefined object. And now that's either gonna be an exception or it can, again, depends on how that's handled. It can be really difficult to determine the origin of that, of, of that error, of that programming. [00:40:26] Bryan: And that is a programmer error. And one of the big challenges that we had with Node is that programmer errors and operational errors, like, you know, I'm out of disk space as an operational error. Those get conflated and it becomes really hard. And in fact, I think the, the language wanted to make it easier to just kind of, uh, drive on in the event of all errors. [00:40:53] Bryan: And it's like, actually not what you wanna do if you're trying to build a reliable, robust system. So we had. No end of issues. [00:41:01] Bryan: We've got a lot of experience developing rigorous systems, um, again coming out of operating systems development and so on. And we want, we brought some of that rigor, if strangely, to JavaScript. So one of the things that we did is we brought a lot of postmortem, diagnos ability and observability to node. [00:41:18] Bryan: And so if, if one of our node processes. Died in production, we would actually get a core dump from that process, a core dump that we could actually meaningfully process. So we did a bunch of kind of wild stuff. I mean, actually wild stuff where we could actually make sense of the JavaScript objects in a binary core dump. JavaScript values ease of getting started over robustness [00:41:41] Bryan: Um, and things that we thought were really important, and this is the, the rest of the world just looks at this being like, what the hell is this? I mean, it's so out of step with it. The problem is that we were trying to bridge two disconnected cultures of one developing really. Rigorous software and really designing it for production, diagnosability and the other, really designing it to software to run in the browser and for anyone to be able to like, you know, kind of liven up a webpage, right? [00:42:10] Bryan: Is kinda the origin of, of live script and then JavaScript. And we were kind of the only ones sitting at the intersection of that. And you begin when you are the only ones sitting at that kind of intersection. You just are, you're, you're kind of fighting a community all the time. And we just realized that we are, there were so many things that the community wanted to do that we felt are like, no, no, this is gonna make software less diagnosable. It's gonna make it less robust. The NodeJS split and why people left [00:42:36] Bryan: And then you realize like, I'm, we're the only voice in the room because we have got, we have got desires for this language that it doesn't have for itself. And this is when you realize you're in a bad relationship with software. It's time to actually move on. And in fact, actually several years after, we'd already kind of broken up with node. [00:42:55] Bryan: Um, and it was like, it was a bit of an acrimonious breakup. there was a, uh, famous slash infamous fork of node called IoJS Um, and this was viewed because people, the community, thought that Joyent was being what was not being an appropriate steward of node js and was, uh, not allowing more things to come into to, to node. [00:43:19] Bryan: And of course, the reason that we of course, felt that we were being a careful steward and we were actively resisting those things that would cut against its fitness for a production system. But it's some way the community saw it and they, and forked, um, and, and I think the, we knew before the fork that's like, this is not working and we need to get this thing out of our hands. Platform is a reflection of values node summit talk [00:43:43] Bryan: And we're are the wrong hands for this? This needs to be in a foundation. Uh, and so we kind of gone through that breakup, uh, and maybe it was two years after that. That, uh, friend of mine who was um, was running the, uh, the node summit was actually, it's unfortunately now passed away. Charles er, um, but Charles' venture capitalist great guy, and Charles was running Node Summit and came to me in 2017. [00:44:07] Bryan: He is like, I really want you to keynote Node Summit. And I'm like, Charles, I'm not gonna do that. I've got nothing nice to say. Like, this is the, the, you don't want, I'm the last person you wanna keynote. He's like, oh, if you have nothing nice to say, you should definitely keynote. You're like, oh God, okay, here we go. [00:44:22] Bryan: He's like, no, I really want you to talk about, like, you should talk about the Joyent breakup with NodeJS. I'm like, oh man. [00:44:29] Bryan: And that led to a talk that I'm really happy that I gave, 'cause it was a very important talk for me personally. Uh, called Platform is a reflection of values and really looking at the values that we had for Node and the values that Node had for itself. And they didn't line up. [00:44:49] Bryan: And the problem is that the values that Node had for itself and the values that we had for Node are all kind of positives, right? Like there's nobody in the node community who's like, I don't want rigor, I hate rigor. It's just that if they had the choose between rigor and making the language approachable. [00:45:09] Bryan: They would choose approachability every single time. They would never choose rigor. And, you know, that was a, that was a big eye-opener. I do, I would say, if you watch this talk. [00:45:20] Bryan: because I knew that there's, like, the audience was gonna be filled with, with people who, had been a part of the fork in 2014, I think was the, the, the, the fork, the IOJS fork. And I knew that there, there were, there were some, you know, some people that were, um, had been there for the fork and. [00:45:41] Bryan: I said a little bit of a trap for the audience. But the, and the trap, I said, you know what, I, I kind of talked about the values that we had and the aspirations we had for Node, the aspirations that Node had for itself and how they were different. [00:45:53] Bryan: And, you know, and I'm like, look in, in, in hindsight, like a fracture was inevitable. And in 2014 there was finally a fracture. And do people know what happened in 2014? And if you, if you, you could listen to that talk, everyone almost says in unison, like IOJS. I'm like, oh right. IOJS. Right. That's actually not what I was thinking of. [00:46:19] Bryan: And I go to the next slide and is a tweet from a guy named TJ Holloway, Chuck, who was the most prolific contributor to Node. And it was his tweet also in 2014 before the fork, before the IOJS fork explaining that he was leaving Node and that he was going to go. And you, if you turn the volume all the way up, you can hear the audience gasp. [00:46:41] Bryan: And it's just delicious because the community had never really come, had never really confronted why TJ left. Um, there. And I went through a couple folks, Felix, bunch of other folks, early Node folks. That were there in 2010, were leaving in 2014, and they were going to go primarily, and they were going to go because they were sick of the same things that we were sick of. [00:47:09] Bryan: They, they, they had hit the same things that we had hit and they were frustrated. I I really do believe this, that platforms do reflect their own values. And when you are making a software decision, you are selecting value. [00:47:26] Bryan: You should select values that align with the values that you have for that software. That is, those are, that's way more important than other things that people look at. I think people look at, for example, quote unquote community size way too frequently, community size is like. Eh, maybe it can be fine. [00:47:44] Bryan: I've been in very large communities, node. I've been in super small open source communities like AUMs and RAs, a bunch of others. there are strengths and weaknesses to both approaches just as like there's a strength to being in a big city versus a small town. Me personally, I'll take the small community more or less every time because the small community is almost always self-selecting based on values and just for the same reason that I like working at small companies or small teams. [00:48:11] Bryan: There's a lot of value to be had in a small community. It's not to say that large communities are valueless, but again, long answer to your question of kind of where did things go south with Joyent and node. They went south because the, the values that we had and the values the community had didn't line up and that was a very educational experience, as you might imagine. [00:48:33] Jeremy: Yeah. And, and given that you mentioned how, because of those values, some people moved from Node to go, and in the end for much of what oxide is building. You ended up using rust. What, what would you say are the, the values of go and and rust, and how did you end up choosing Rust given that. Go's decisions regarding generics, versioning, compilation speed priority [00:48:56] Bryan: Yeah, I mean, well, so the value for, yeah. And so go, I mean, I understand why people move from Node to Go, go to me was kind of a lateral move. Um, there were a bunch of things that I, uh, go was still garbage collected, um, which I didn't like. Um, go also is very strange in terms of there are these kind of like. [00:49:17] Bryan: These autocratic kind of decisions that are very bizarre. Um, there, I mean, generics is kind of a famous one, right? Where go kind of as a point of principle didn't have generics, even though go itself actually the innards of go did have generics. It's just that you a go user weren't allowed to have them. [00:49:35] Bryan: And you know, it's kind of, there was, there was an old cartoon years and years ago about like when a, when a technologist is telling you that something is technically impossible, that actually means I don't feel like it. Uh, and there was a certain degree of like, generics are technically impossible and go, it's like, Hey, actually there are. [00:49:51] Bryan: And so there was, and I just think that the arguments against generics were kind of disingenuous. Um, and indeed, like they ended up adopting generics and then there's like some super weird stuff around like, they're very anti-assertion, which is like, what, how are you? Why are you, how is someone against assertions, it doesn't even make any sense, but it's like, oh, nope. [00:50:10] Bryan: Okay. There's a whole scree on it. Nope, we're against assertions and the, you know, against versioning. There was another thing like, you know, the Rob Pike has kind of famously been like, you should always just run on the way to commit. And you're like, does that, is that, does that make sense? I mean this, we actually built it. [00:50:26] Bryan: And so there are a bunch of things like that. You're just like, okay, this is just exhausting and. I mean, there's some things about Go that are great and, uh, plenty of other things that I just, I'm not a fan of. Um, I think that the, in the end, like Go cares a lot about like compile time. It's super important for Go Right? [00:50:44] Bryan: Is very quick, compile time. I'm like, okay. But that's like compile time is not like, it's not unimportant, it's doesn't have zero importance. But I've got other things that are like lots more important than that. Um, what I really care about is I want a high performing artifact. I wanted garbage collection outta my life. Don't think garbage collection has good trade offs [00:51:00] Bryan: I, I gotta tell you, I, I like garbage collection to me is an embodiment of this like, larger problem of where do you put cognitive load in the software development process. And what garbage collection is saying to me it is right for plenty of other people and the software that they wanna develop. [00:51:21] Bryan: But for me and the software that I wanna develop, infrastructure software, I don't want garbage collection because I can solve the memory allocation problem. I know when I'm like, done with something or not. I mean, it's like I, whether that's in, in C with, I mean it's actually like, it's really not that hard to not leak memory in, in a C base system. [00:51:44] Bryan: And you can. give yourself a lot of tooling that allows you to diagnose where memory leaks are coming from. So it's like that is a solvable problem. There are other challenges with that, but like, when you are developing a really sophisticated system that has garbage collection is using garbage collection. [00:51:59] Bryan: You spend as much time trying to dork with the garbage collector to convince it to collect the thing that you know is garbage. You are like, I've got this thing. I know it's garbage. Now I need to use these like tips and tricks to get the garbage collector. I mean, it's like, it feels like every Java performance issue goes to like minus xx call and use the other garbage collector, whatever one you're using, use a different one and using a different, a different approach. [00:52:23] Bryan: It's like, so you're, you're in this, to me, it's like you're in the worst of all worlds where. the reason that garbage collection is helpful is because the programmer doesn't have to think at all about this problem. But now you're actually dealing with these long pauses in production. [00:52:38] Bryan: You're dealing with all these other issues where actually you need to think a lot about it. And it's kind of, it, it it's witchcraft. It, it, it's this black box that you can't see into. So it's like, what problem have we solved exactly? And I mean, so the fact that go had garbage collection, it's like, eh, no, I, I do not want, like, and then you get all the other like weird fatwahs and you know, everything else. [00:52:57] Bryan: I'm like, no, thank you. Go is a no thank you for me, I, I get it why people like it or use it, but it's, it's just, that was not gonna be it. Choosing Rust [00:53:04] Bryan: I'm like, I want C. but I, there are things I didn't like about C too. I was looking for something that was gonna give me the deterministic kind of artifact that I got outta C. But I wanted library support and C is tough because there's, it's all convention. you know, there's just a bunch of other things that are just thorny. And I remember thinking vividly in 2018, I'm like, well, it's rust or bust. Ownership model, algebraic types, error handling [00:53:28] Bryan: I'm gonna go into rust. And, uh, I hope I like it because if it's not this, it's gonna like, I'm gonna go back to C I'm like literally trying to figure out what the language is for the back half of my career. Um, and when I, you know, did what a lot of people were doing at that time and people have been doing since of, you know, really getting into rust and really learning it, appreciating the difference in the, the model for sure, the ownership model people talk about. [00:53:54] Bryan: That's also obviously very important. It was the error handling that blew me away. And the idea of like algebraic types, I never really had algebraic types. Um, and the ability to, to have. And for error handling is one of these really, uh, you, you really appreciate these things where it's like, how do you deal with a, with a function that can either succeed and return something or it can fail, and the way c deals with that is bad with these kind of sentinels for errors. [00:54:27] Bryan: And, you know, does negative one mean success? Does negative one mean failure? Does zero mean failure? Some C functions, zero means failure. Traditionally in Unix, zero means success. And like, what if you wanna return a file descriptor, you know, it's like, oh. And then it's like, okay, then it'll be like zero through positive N will be a valid result. [00:54:44] Bryan: Negative numbers will be, and like, was it negative one and I said airo, or is it a negative number that did not, I mean, it's like, and that's all convention, right? People do all, all those different things and it's all convention and it's easy to get wrong, easy to have bugs, can't be statically checked and so on. Um, and then what Go says is like, well, you're gonna have like two return values and then you're gonna have to like, just like constantly check all of these all the time. Um, which is also kind of gross. Um, JavaScript is like, Hey, let's toss an exception. If, if we don't like something, if we see an error, we'll, we'll throw an exception. [00:55:15] Bryan: There are a bunch of reasons I don't like that. Um, and you look, you'll get what Rust does, where it's like, no, no, no. We're gonna have these algebra types, which is to say this thing can be a this thing or that thing, but it, but it has to be one of these. And by the way, you don't get to process this thing until you conditionally match on one of these things. [00:55:35] Bryan: You're gonna have to have a, a pattern match on this thing to determine if it's a this or a that, and if it in, in the result type that you, the result is a generic where it's like, it's gonna be either the thing that you wanna return. It's gonna be an okay that contains the thing you wanna return, or it's gonna be an error that contains your error and it forces your code to deal with that. [00:55:57] Bryan: And what that does is it shifts the cognitive load from the person that is operating this thing in production to the, the actual developer that is in development. And I think that that, that to me is like, I, I love that shift. Um, and that shift to me is really important. Um, and that's what I was missing, that that's what Rust gives you. [00:56:23] Bryan: Rust forces you to think about your code as you write it, but as a result, you have an artifact that is much more supportable, much more sustainable, and much faster. Prefer to frontload cognitive load during development instead of at runtime [00:56:34] Jeremy: Yeah, it sounds like you would rather take the time during the development to think about these issues because whether it's garbage collection or it's error handling at runtime when you're trying to solve a problem, then it's much more difficult than having dealt with it to start with. [00:56:57] Bryan: Yeah, absolutely. I, and I just think that like, why also, like if it's software, if it's, again, if it's infrastructure software, I mean the kinda the question that you, you should have when you're writing software is how long is this software gonna live? How many people are gonna use this software? Uh, and if you are writing an operating system, the answer for this thing that you're gonna write, it's gonna live for a long time. [00:57:18] Bryan: Like, if we just look at plenty of aspects of the system that have been around for a, for decades, it's gonna live for a long time and many, many, many people are gonna use it. Why would we not expect people writing that software to have more cognitive load when they're writing it to give us something that's gonna be a better artifact? [00:57:38] Bryan: Now conversely, you're like, Hey, I kind of don't care about this. And like, I don't know, I'm just like, I wanna see if this whole thing works. I've got, I like, I'm just stringing this together. I don't like, no, the software like will be lucky if it survives until tonight, but then like, who cares? Yeah. Yeah. [00:57:52] Bryan: Gar garbage clock. You know, if you're prototyping something, whatever. And this is why you really do get like, you know, different choices, different technology choices, depending on the way that you wanna solve the problem at hand. And for the software that I wanna write, I do like that cognitive load that is upfront. With LLMs maybe you can get the benefit of the robust artifact with less cognitive load [00:58:10] Bryan: Um, and although I think, I think the thing that is really wild that is the twist that I don't think anyone really saw coming is that in a, in an LLM age. That like the cognitive load upfront almost needs an asterisk on it because so much of that can be assisted by an LLM. And now, I mean, I would like to believe, and maybe this is me being optimistic, that the the, in the LLM age, we will see, I mean, rust is a great fit for the LLMH because the LLM itself can get a lot of feedback about whether the software that's written is correct or not. [00:58:44] Bryan: Much more so than you can for other environments. [00:58:48] Jeremy: Yeah, that is a interesting point in that I think when people first started trying out the LLMs to code, it was really good at these maybe looser languages like Python or JavaScript, and initially wasn't so good at something like Rust. But it sounds like as that improves, if. It can write it then because of the rigor or the memory management or the error handling that the language is forcing you to do, it might actually end up being a better choice for people using LLMs. [00:59:27] Bryan: absolutely. I, it, it gives you more certainty in the artifact that you've delivered. I mean, you know a lot about a Rust program that compiles correctly. I mean, th there are certain classes of errors that you don't have, um, that you actually don't know on a C program or a GO program or a, a JavaScript program. [00:59:46] Bryan: I think that's gonna be really important. I think we are on the cusp. Maybe we've already seen it, this kind of great bifurcation in the software that we writ

Thomas has written a python script available on his github.com account that you can run against any vSphere server to check hardware compatibility. It uses API's from the VCF Operations which has the current compatibility matrix in it's grasp. It now also can tell you future compatibility risks. A cool small powerful bit of Code. Thomas joins Bob and Eric and Tony to talk about it and other side topics on Compatibility.

Join us as Marian explains what AI governance means for vSphere administrators and why it matters now. Marian walks through practical governance frameworks that vSphere admins need to understand, from IEEE 7000 series standards to mapping governance controls onto infrastructure you already manage. You'll learn what your CISO will ask for, how to respond using your existing VMware stack, and why governance isn't about slowing innovation� it's about enabling it safely. This episode covers real-world scenarios from data lineage and model transparency to integrating governance tools with existing infrastructure, and addresses the gap between compliance requirements and practical implementation for virtualized environments. Timestamps 0:00 Welcome & Introduction 5:16 Marian's Background in Tech & Governance 6:37 What is Governance? 12:45 IEEE 7000 Series Standards Overview 18:22 AI Governance for vSphere Admins 24:16 Data Lineage & Model Transparency 30:41 Risk Assessment Frameworks 36:52 Practical Implementation Strategies 42:18 Integration with Existing Tools 47:35 Common Governance Challenges 51:12 Vendor Landscape Discussion 54:27 Missing Innovation in the Space 58:09 Wrap-up & Resources How to find Marian: https://www.linkedin.com/in/mariannewsome/ Links from the show: https://ethicaltechmatters.com/

The Cloud Native Computing Foundation has introduced the Certified Kubernetes AI Conformance Program to bring consistency to an increasingly fragmented AI ecosystem. Announced at KubeCon + CloudNativeCon North America 2025, the program establishes open, community-driven standards to ensure AI applications run reliably and portably across different Kubernetes platforms. VMware by Broadcom's vSphere Kubernetes Service (VKS) is among the first platforms to achieve certification.In an interview with The New Stack, Broadcom leaders Dilpreet Bindra and Himanshu Singh explained that the program applies lessons from Kubernetes' early evolution, aiming to reduce the “muddiness” in AI tooling and improve cross-platform interoperability. They emphasized portability as a core value: organizations should be able to move AI workloads between public and private clouds with minimal friction.VKS integrates tightly with vSphere, using Kubernetes APIs directly to manage infrastructure components declaratively. This approach, along with new add-on management capabilities, reflects Kubernetes' growing maturity. According to Bindra and Singh, this stability now enables enterprises to trust Kubernetes as a foundation for production-grade AI. Learn more from The New Stack about Broadcom's latest updates with Kubernetes: Has VMware Finally Caught Up with Kubernetes?VMware VCF 9.0 Finally Unifies Container and VM ManagementJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The new VMUG Advantage VCP portal is going live over the next two weeks. Eric, Bob and Corey discuss the release and how to get your VCF 9 tokens. If you need to refresh your license, that's support too. Great show and good community participation.

Dive into KubeVirt with the vBrownBag crew and guest Eric Shanks!

Stuart Carrera (Senior Consultant, Mandiant Consulting) joins host Luke McNamara to discuss how threat actors are increasingly targeting the VMware vSphere estate, and leveraging in this environment to conduct extortion and data theft. Stuart details why this has become an attractive target, and ways organizations can better engineer detections to respond to this activity. https://cloud.google.com/blog/topics/threat-intelligence/defending-vsphere-from-unc3944https://cloud.google.com/blog/topics/threat-intelligence/vsphere-active-directory-integration-risks

Recently a new white paper was released on the topic of latency-sensitive workloads. I invited Mark Achtemichuck (X, LinkedIn) to the show to go over the various recommendations and best practices. Mark highlight many important configuration settings, and also recommends everyone to not only read the white paper, but also the vSphere 8 performance documentation. Also, his VMware Explore session comes highly recommended, make sure to watch it!Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

For episode 087 of the podcast, I invited Deji Akomolafe to the show to discuss Microsoft on VMware VCF and vSphere. Deji shares his experiences virtualizing and running Microsoft apps on top of VCF and vSphere and discusses some of the best practices and constraints. Whitepapers and tools discussed in the episode: Protecting mission critical workloads with Live Site Recovery Microsoft SQL on vSphere best practices Diskspd for bench Explore session - Improving Workload Availability Using vMotion Application Notification Explore session - Architecting Your Microsoft SQL Server Workloads on VMware Cloud Foundation Explore session - Improving Workloads Availability on VCF with vMotion Apps Notification Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

Brandon Frost shares his experience with memory tiering and the use of Intel Optane in his large private cloud environment. He explains how memory tiering helped optimize memory usage and reduce costs by offloading cold memory to a non-active tier. He also discusses the challenges of upgrading memory and the benefits of using NVMe-based tiering. Brandon expresses his interest in Project Peaberry, a memory tiering offloading card, and its potential use cases. He highlights the importance of his team in developing capacity data gathering and analytics solutions.Takeaways:Memory tiering helps optimize memory usage and reduce costs by offloading cold memory to a non-active tier.Using NVMe-based tiering provides flexibility and scalability in memory allocation.Project Peaberry, a memory tiering offloading card, offers potential benefits in terms of performance and cost-effectiveness.Effective capacity planning requires robust data gathering and analytics solutions.Brandon Frost emphasizes the importance of his team in developing and implementing memory optimization strategies.Links:VMware Explore session by Brandon and Arvind on memory tieringMemory Tiering blog on vmware.comDisclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

Arvind Jagannath, a platform product manager at Broadcom, discusses memory tiering and its benefits for customers with Duncan. He explains that enterprise applications are becoming more memory-bound and require better performance. Memory tiering helps address the CPU to memory imbalance that many customers face, allowing for better CPU utilization and reducing costs. Arvind also mentions the challenges of expanding memory and the need for a solution like memory tiering. He introduces Project Capitola, which has evolved into NVMe-based memory tiering, and discusses the benefits and recommendations for using NVMe devices. Arvind also touches on the integration of memory tiering with advanced vSphere features like DRS and HA, and hints at future developments with Project Peaberry and CXL. TakeawaysEnterprise applications are becoming more memory-bound and require better performance.Memory tiering helps address the CPU to memory imbalance and improves CPU utilization.NVMe-based memory tiering provides additional memory capacity and reduces costs.Integration with advanced vSphere features like DRS and HA is being developed.Future developments include Project Peaberry and the use of CXL for intelligent devices.LinksExplore Session - Memory Tiering: Power Your Server Infrastructure With Memory Innovations [VCFB1198LV]Yellow-Bricks.com - Memory Tiering BlogSNIA Recording on Memory Tiering and CXL accelerators Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

In this conversation, Katarina Brookfield discusses her career trajectory and her current role at Broadcom. She shares defining moments in her career, including her experience working on the Black Sea Maritime Archaeology project. The conversation then shifts to the newly announced vSphere IaaS control plane and its benefits. Katarina explains that the control plane provides a comprehensive solution for deploying workloads, including additional services like storage provisioning and load balancing. The conversation also covers the self-service nature of the control plane, the different interfaces for consumers and admins, and the integration of HashiCorp Packer for building and customizing VM images. The TKG service, which allows for the deployment of managed Kubernetes clusters, is also discussed, highlighting its ease of use and integration with vSphere. The conversation concludes with a discussion of the new features in the latest version of the TKG service, including cluster auto-scaling and the decoupling of TKG from vCenter.TakeawaysThe vSphere IaaS control plane provides a comprehensive solution for deploying workloads, including additional services like storage provisioning and load balancing.The control plane offers a self-service experience for consumers, allowing them to easily deploy the services they need.Different interfaces, including APIs, CLI, and UI, cater to the preferences of different users, making it accessible to both admins and consumers.The integration of HashiCorp Packer allows for the building and customization of VM images, providing flexibility and automation.The TKG service simplifies the deployment of managed Kubernetes clusters, making it accessible to users with little Kubernetes experience.The latest version of the TKG service decouples it from vCenter, allowing for faster delivery of new Kubernetes versions.New features in the TKG service include cluster auto-scaling and the integration of HashiCorp Packer for building and customizing VM images.Chapters00:00 - Kat's Career Trajectory and the Role of Defining Moments09:20 - The Comprehensive Solution of the vSphere IaaS Control Plane11:02 - Enabling Self-Service and Catering to Different User Preferences18:14 - Flexibility and Automation with HashiCorp Packer Integration22:47 - Simplifying Kubernetes Deployment with the TKG Service29:14 - Decoupling TKG from vCenter for Faster Delivery of Kubernetes Versions38:36 - New Features in the Latest Version of the TKG ServiceDisclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

Summary:Ala Dewberry introduces Project Marigold, a plugin that aims to make GPUs a first-class citizen on vSphere by providing visibility and observability of GPU metrics at the cluster and data center level. The plugin helps infrastructure administrators manage GPUs more strategically and enables data scientists and application developers to have a frictionless experience. Ala also discusses the use cases for private AI and encourages users to give feedback on the plugin.TakeawaysDefining moments in our careers can shape our trajectory and open up new opportunities for growth and learning.Project Marigold aims to make GPUs a first-class citizen on vSphere by providing visibility and observability of GPU metrics at the cluster and data center level.The plugin helps infrastructure administrators manage GPUs more strategically and enables data scientists and application developers to have a frictionless experience.Private AI allows organizations to leverage their data centers for AI workloads that require data privacy, security, and compliance.Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

Recently Broadcom announced the latest updates to vSphere 8. This new release includes updates that will help enhance operational efficiency for IT admins, supercharge the performance of demanding workloads, and accelerate the pace of innovation for DevOps engineers, developers, and anyone else that can benefit from self-service access to infrastructure services, in a secure and compliant manner.  On this episode of the Virtually Speaking Podcast we welcome Féidhlim O'Leary to walk us through the details of this latest release.  Links Mentioned: VCF Landing Page ​​What's New in vSphere 8 Update 3? ​​Announcing VMware vSphere Foundation 5.2 VMware vCenter Server 8.0 Update 3 Release Notes Embracing Change with VMware vSphere Foundation ​​VMware vSphere Foundation (VVF) Licensing Virtually Speaking YouTube Page Virtually Speaking Podcast vSAN on TechZone The Virtually Speaking Podcast The Virtually Speaking Podcast is a technical podcast dedicated to discussing VMware topics related to private and hybrid cloud. Each week Pete Flecha and John Nicholson bring in various subject matter experts from within the industry to discuss their respective areas of expertise. If you're new to the Virtually Speaking Podcast check out all episodes on vspeakingpodcast.com and follow on TwitterX @VirtSpeaking

comfivision CEO Yves Sandfort joins us to talk of the customer journey moving from vSphere to the full VCF stack, acceptance and moving forward.

Yves Sanffort Comdivision CEO gives his perspective on progress as customers move to VCF, AI and our Cloud offerings.

In Episode 5 of the series "Exploring VMware Cloud Foundation" titled 'VCF Storage,' we dive into the dynamic world of storage solutions within VMware Cloud Foundation. Our guest, Rakesh Radhakrishnan, Senior Director of Product Management, expertly navigates through the latest storage offerings and their benefits within the new Go-to-Market strategy for VCF. Rakesh begins by dissecting the array of storage offerings integrated into the revamped VCF platform, shedding light on their diverse functionalities and advantages. He elaborates on how these innovations elevate storage capabilities within VCF, enhancing performance, scalability, and agility for users. Throughout the episode, Rakesh guides us through a compelling customer journey, illustrating the seamless transition from vSphere to VCF from a storage perspective. By highlighting real-world scenarios and best practices, he illuminates the path for customers looking to leverage VCF's storage capabilities to their fullest potential. Furthermore, Rakesh unveils exciting new innovations in storage within VCF, offering a glimpse into the future of data management and storage infrastructure. He shares insights into the strategic direction for storage within VCF, outlining VMware's vision for evolving storage solutions to meet the ever-expanding needs of modern cloud environments. Join us on this Virtually Speaking Podcast series as we unravel the intricacies of VCF Storage with Rakesh Radhakrishnan, exploring the transformative potential of storage innovations within VMware Cloud Foundation. Links Mentioned: VCF Landing Page Announcing General Availability of VMware Cloud Foundation 5.1.1 VCF Webinars VCF YouTube Page Virtually Speaking YouTube Page Virtually Speaking Podcast vSAN on TechZone Watch the Entire Series Ep 01: Inside the Private Cloud Ep 02: What's Inside Ep 03: The Cloud Admin Journey Ep 04: VCF Compute Ep 05: VCF Storage Ep 06: VCF Networking Ep 07: A Cloud Management Experience Ep 08: VMware Private AI Ep 09: Data Services Manager  Ep 10: VMware vDefend  The Virtually Speaking Podcast The Virtually Speaking Podcast is a technical podcast dedicated to discussing VMware topics related to private and hybrid cloud. Each week Pete Flecha and John Nicholson bring in various subject matter experts from VMware and from within the industry to discuss their respective areas of expertise. If you're new to the Virtually Speaking Podcast check out all episodes on vspeakingpodcast.com and follow on TwitterX @VirtSpeaking

Continuing our special 10-part series on the Virtually Speaking Podcast: "Exploring VMware Cloud Foundation" in Episode 4,titled “VCF Compute”, Himanshu Singh, Director of vSphere Product Marketing, navigates us through the spectrum of vSphere editions, highlighting their adaptability for diverse customer needs. He then showcases the enhanced value proposition of vSphere within VMware Cloud Foundation, harnessing the synergy with NSX and Aria Automation to elevate private cloud infrastructures. Drawing from the essence of VMware vSphere, Himanshu emphasizes its role as the enterprise workload engine, integrating cutting-edge cloud infrastructure technology with DPU and GPU-based acceleration to amplify workload performance. vSphere optimizes IT environments, bolstering availability, simplifying lifecycle management, and streamlining maintenance for heightened operational efficiency. Moreover, it establishes an intrinsically secure infrastructure engine, fortified out-of-the-box and complemented by straightforward hardening guidance for compliance adherence. Links Mentioned: VCF Landing Page Announcing General Availability of VMware Cloud Foundation 5.1.1 VCF Webinars VCF YouTube Page Virtually Speaking YouTube Page Virtually Speaking Podcast Watch the Entire Series Ep 01: Inside the Private Cloud Ep 02: What's Inside Ep 03: The Cloud Admin Journey Ep 04: VCF Compute Ep 05: VCF Storage Ep 06: VCF Networking Ep 07: A Cloud Management Experience Ep 08: VMware Private AI Ep 09: Data Services Manager  Ep 10: VMware vDefend  The Virtually Speaking Podcast The Virtually Speaking Podcast is a technical podcast dedicated to discussing VMware topics related to private and hybrid cloud. Each week Pete Flecha and John Nicholson bring in various subject matter experts from VMware and from within the industry to discuss their respective areas of expertise. If you're new to the Virtually Speaking Podcast check out all episodes on vspeakingpodcast.com and follow on TwitterX @VirtSpeaking

VMware recently released vSphere and vSAN 8.0 Update 3, and of course, we had to invite Feidhlim, Jason, and Pete back on the show to discuss what's new in these releases. There's awesome new functionality released and some great enhancements, so make sure to listen to the full episode. Key Takeaways:vSphere 8.0 update 3 introduces the vSphere Live Patch Update Path, which allows for patching ESXi hosts without evacuating VMs or entering full maintenance mode.Improvements in GPU functionality include the ability to use two DPUs in an ESXi host for availability, better support for VGPUs with different profiles and memory sizes, and simplified activation of GPU mobility with DRS.The vSphere cluster service (VCLS) has been re-architected to reduce resource consumption and improve rollback mechanisms.The 8.0 update 3 introduces stretched VVols, which customers have been asking for, and support for stretched fault tolerance.There are enhancements in VVols, including unmapped support for NVMe over fabrics.The updates in NVMe over Fabrics provide faster data migration and cloning.NFS enhancements include VMK port binding and support for NFS version 4.1.vSAN 8.0 U3 introduces new features and enhancements in flexible topologies, agile data protection, and enhanced management.The support for stretch cluster arrangement in VCF allows customers to take full advantage of ESA and improve performance, storage efficiency, and resilience.The full support of vSAN Max as principal storage within a workload domain enables customers to maintain a centralized shared storage model while leveraging the capabilities of vSAN.vSAN data protection allows users to create snapshots based on groups of VMs, set snapshotting schedules, and easily recover VMs without them being part of the inventory.Enhancements in alerting capabilities for NVMe storage devices and proactive hardware management provide better visibility and intelligence about the health and wellbeing of storage devices.Follow us on Twitter for updates and news about upcoming episodes: https://twitter.com/UnexploredPod.Last but not least, make sure to hit that subscribe button, rate wherever possible, and share the episode with your friends and colleagues!Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

We interview Rutger and Luis covering their trending SDDC Lab Version 6, a blog article that can be found at https://https://rutgerblom.com/2024/01/26/sddc-lab-v6-released/. The SDDC.Lab project is a series of Ansible Playbooks that preform fully automated doployments of nested VMware Software Defined Data Center Environments called pods. Each Pod consists of solutions like vSphere, VSAN, NSX, Tanzu and NSX Advanced Load Balancer, Aria Operations for Logs and VyOS Router.

In this conversation, Duncan and Cormac Hogan discuss VMware's Data Services Manager (DSM) and its role in offering data services in a full-stack private cloud. They cover topics such as the use cases for DSM, the integration with Kubernetes, the support for different databases, the automation capabilities, and the licensing model. Cormac highlights the features of DSM, including lifecycle management, backups, scaling, monitoring, and advanced settings. He also mentions the upcoming release of new features and additional data services. TakeawaysData Services Manager (DSM) is a VMware product that offers data services in a full-stack private cloud.DSM integrates with Kubernetes and allows VI administrators to maintain control of vSphere resources while offering data services.DSM supports databases such as Postgres and MySQL, with support for other data services like AlloyDB in tech preview.DSM provides features such as lifecycle management, backups, scaling, monitoring, and advanced settings.DSM is included in VMware Cloud Foundation (VCF) and support can be added through the Private AI Foundation add-on.Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

In this episode, Suresh Madhu, Lead Architect for Pure Storage, walks us through new innovations in the Pure Storage Disaster Recovery portfolio & gives us a live demo of the Pure Protect DRaaS platform! __________ Chapters 00:00 - Introduction 01:33 - The landscape today 07:46 - Data Resiliency Strategies 12:21 - What is Pure Protect // DRaaS? 23:03 - Solution Overview // DRaaS for vSphere to AWS Native 50:24 - Live Demo! __________ Resources https://www.purestorage.com/ https://www.linkedin.com/in/suresh-madhu-9544181/

Welcome back, long-awaiting listeners, as your favorite IT podcast with a healthy dose of empathy, Data Center Therapy, returns after a seven-month hiatus.  It's been too long, we know, but we're back and we're recharged so we can bring you the first of many new episodes with exciting, topical and relevant content.  Thanks for joining us!On this episode, your intrepid hosts, Mr. Matt “been through the desert on a horse with no name” Cozzolino and Mr. Matt “Call me Heisenberg” Yette share Cozzo's adventures hiking from Supai to Havasupai Falls in the great state of Arizona, and talk all things Broadcom, given the big news of the acquisition of VMware completed since the last episode aired.While sharing their thoughts on the whole VMware ecosystem and the changes, the DCT crew muse about:The new tiers of subscription licensing that replace many of the old VMware SKUs,The changes in licensing from sockets to cores,The aborted licensing change back in the VMware days regarding vRAM,The “second day” about face on ROBO licensingViable alternatives to vSphereThe spinning off of the Horizon technology to the new firm named Omnissa,and much, much more.As a reminder, IVOXY are hosting a vSphere 8 Advanced Class in just two weeks, and we're developing classes and workshops for Disaster Recovery and Aria Operations (formerly VMware vRealize Operations.)  If you're interested in Matt Cozzolino's Networking for Server admins Ask Me Anything on June 20th at 11AM Pacific, register at: https://ivoxy.com/ama-networkingforserveradmins.   If you're looking for the DirtFish 2024 event registration, it may be found at: https://ivoxy.com/dirtfish2024.  As always, if you enjoy Data Center Therapy, please tell three friends and be sure to like, share and subscribe wherever you get your podcasts.  Thanks for your patience, your attention, and we eagerly look forward to sharing more in 2024 with you all on the next episode of Data Center Therapy!

In this episode of Virtually Speaking, Pete and John dive deep into the transformative impact of Data Processing Units (DPUs) on virtual infrastructure with special guest Dave Morera from vSphere Technical Marketing. They explore how vSphere Distributed Services Engine leverages DPUs to modernize infrastructure by offloading critical functions from the CPU. This advancement enables significant resource savings, accelerated networking, and enhanced workload security. Dave explains how the integration of DPU lifecycle management into vSphere simplifies operations, reduces the need for third-party tools, and strengthens security with agentless controls. Tune in to learn how DPUs are enhancing performance, improving workload consolidation, and reducing total cost of ownership while simplifying infrastructure management and boosting security.

SummaryFor this special edition of the podcast Duncan invited Michael Roy to discuss the latest VMware Workstation and VMware Fusion announcements. VMware Workstation and Fusion are desktop hypervisor products that allow users to run virtual machines on their PC or Mac. Starting today, Workstation and Fusion commercial licenses will only be available through annual subscriptions. The price for both products is now $199 per year. The free versions of Fusion Player and Workstation Player are being discontinued, but the Pro versions will be available for free for personal use. Support for personal use products will be community-based, while commercial users will have support included in their subscription. The focus of future innovation will be on the integration between vSphere and Workstation/Fusion, providing a local virtual sandbox for learning, development, and testing.TakeawaysVMware Workstation and Fusion are desktop hypervisor products for running virtual machines on PC and Mac.Commercial use of Workstation and Fusion is shifting from perpetual licenses to annual subscriptions.The free versions of Fusion Player and Workstation Player are being discontinued, but the Pro versions will be available for free for personal use.Support for personal use products will be community-based, while commercial users will have support included in their subscription.Future innovation will focus on integrating vSphere with Workstation and Fusion to provide a local virtual sandbox for learning, development, and testing.LinksAnnouncement BlogThe Register articleDisclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

In episode 073 Duncan and Pete discuss various updates and changes related to vSAN, including ReadyNode configurations, licensing, vSAN Max, capacity reporting, and compression ratios. They highlight the improvements in compression ratios with vSAN ESA, which can result in significant space efficiency gains. They also discuss the use cases for vSAN Max and vSAN HCI, as well as the flexibility in making changes to ReadyNode configurations. Overall, they emphasize the ongoing development and exciting future of vSAN and VMware Cloud Foundation.TakeawaysvSAN ESA offers improved compression ratios, with an average of 1.5x and some customers achieving 1.7x or better.vSAN Max is a centralized shared storage solution for vSphere clusters, providing storage services to multiple vSphere clusters.Customers can choose between vSAN Max and vSAN HCI based on their needs, such as independent scaling of storage and compute, separate lifecycle management, extending the life of existing vSphere clusters, or specific application requirements.Changes in ReadyNode configurations for vSAN Max have reduced the minimum number of hosts required and lowered the hardware requirements, making it more accessible for smaller enterprises.Capacity reporting in vSAN has been improved with the introduction of L0FS overhead, providing more accurate information on capacity usage.vSAN ESA's improved compression ratios, combined with RAID 5 or RAID 6 erasure coding, can result in significant space efficiency gains compared to the original storage architecture.Ongoing development and updates are expected in vSAN and VMware Cloud Foundation, with exciting new capabilities on the horizon.Linkshttps://core.vmware.com/blog/smaller-vsan-esa-readynodes-accommodate-vmware-vsphere-foundations-trial-capacity-capabilityhttps://docs.vmware.com/en/VMware-vSphere/8.0/rn/vsphere-esxi-80u2b-release-notes/index.htmlhttps://core.vmware.com/blog/improved-capacity-reporting-vmware-cloud-foundation-51-and-vsan-8-u2 https://core.vmware.com/blog/greater-flexibility-vsan-max-through-lower-hardware-and-cluster-requirements Follow us on X for updates and news about upcoming episodes: https://x.com/UnexploredPod.Last but not least, make sure to hit that subscribe button and share the episode with your friends and colleagues!Disclaimer: The thoughts and opinions shared in this podcast are our own/guest(s), and not necessarily those of Broadcom or VMware by Broadcom.

For some strange reason, “maintenance” has been in the news quite a bit lately. Is there ever a time when maintenance is enjoyable, or appreciated? SHOW: 814SHOW TRANSCRIPT: The Cloudcast #814SHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST - "CLOUDCAST BASICS"SHOW NOTES:AWS increased the price of longer-running EKS clusters by 6xBroadcom changes VMware licensing from perpetual to subscriptionBroadcom offers security patches to perpetual license customersIncreasing the Kubernetes support window to 1 yearDiscovering the XZ backdoor (Oxide and Friends, podcast)IS MAINTENANCE EVER APPRECIATED OR ENJOYABLE?Spent the day surrounded by maintenance activities (oil, AC, power-wash)The costs of maintenance are real and opportunityMaintenance often goes unappreciated and unseenNaming: Release Notes, Technical Debt, Chaos EngineeringTECHNICAL DEBT VS. MAINTENANCEShould we encourage a lack of maintenance vs. innovation as a priority?Should we encourage active maintenance with lower hard costs?Is there a way to put respect on maintenance? (e.g. OSS maintainers)Do we undervalue maintenance (e.g. Backup/Recovery, DisasterRecovery, etc.)?What maintenance best practices do you use? What are the good and bad of them?FEEDBACK?Email: show at the cloudcast dot netTwitter: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

In this episode of the Virtually Speaking Podcast, Pete and John sat down with Chen Wei, the creator of HCIBench, from VMware by Broadcom. HCIBench streamlines HCI benchmarking, simplifying a once-complex process. Chen Wei shared HCIBench's journey, emphasizing its user-friendly interface, which caters to both novices and experts. We explored its technical architecture and discussed key metrics like throughput and latency crucial for assessing HCI performance accurately. Our conversation also touched on HCIBench's collaborative development model, driven by user feedback and community contributions. Looking ahead, Chen discussed upcoming features and the tool's evolving role in the HCI industry. Tune in to gain insights into HCI benchmarking and discover how HCIBench is shaping the future of infrastructure optimization. Links Mentioned: Download HCIBench - https://via.vmware.com/hcibench Blog: How do I Identify if I have bad performance Reference Architectures - https://core.vmware.com/reference-architectures The Virtually Speaking Podcast The Virtually Speaking Podcast is a technical podcast dedicated to discussing VMware topics related to storage and availability. In each episode, Pete Flecha and John Nicholson bring in various subject matter experts from VMware and within the industry to discuss their respective areas of expertise. If you're new to the Virtually Speaking Podcast check out all episodes on vSpeakingPodcast.com and follow on Twitter @VirtSpeaking

Welcome to episode 249 of the CloudPod Podcast – where the forecast is always cloudy! This week, Justin and Ryan put on their scuba suits and dive into the latest cloud news, from Google Gemini’s “woke” woes, to Azure VMware Solution innovations, and some humorous takes on Reddit and Google’s unexpected collaboration. Join the conversation on AI, storage solutions, and more this week in the Cloud! Titles we almost went with this week: Gemini Has Gone Woke? Uhhh…ok.  A big thanks to this week's sponsor: We're sponsorless this week! Interested in sponsoring us and having access to a specialized and targeted market? We'd love to talk to you. Send us an email or hit us up on our Slack Channel.  General News  01:48 DigitalOcean beats expectations under the helm of new CEO Paddy Srinivasan Quick earnings chat. Digital Ocean, under their new CEO Paddy Srinivasan reported earnings of 44 centers per share, well ahead of Wall Street’s target of 37 cents per share.  Revenue growth was a little sluggish at 11% more than a year earlier, but the companies 181 million in reported sales still beat analysts expectations.  Full year revenue was 693M for the year.  We’re really glad to see the business is still going, and instead of going back on-premise, we think it’s a viable option for many workloads so don't sleep on them. 02:46  Ryan – “I like that, you know, while they are very focused on, you know, traditional compute workloads, you can still see them. Dip in their toes into managed services and, and, um, their interaction with the community and documentation of how to do things. I think it’s really impactful.” 03:34 VMware moves to quell concern over rapid series of recent license changes   As we have reported multiple times on the VMWARE shellacking they are doing to the customers, Vmware has released a blog post trying to convince you that they’re **not** screwing you.  Broadcom has realigned operations around VMWare Cloud Foundation private cloud portfolio and data center-focused VMWare Vsphere suite, and no longer sells discrete products such as vSphere hypervisor, vSAN virtual storage and NSX network storage virtualization software.   They also are eliminating perpetual licensing in favor of subscription-only pricing, with VCF users getting vSAN, NSX and the Aria Management and orchestration components bundled whether you want them or not.  Broadcom says this is about focusing on best-of-breed silos, and not disparate products without an integrated experience. 

Will Broadcom's bold move with VMware's licensing leave your budget on cloud nine or bring it crashing back down to earth? This episode is a whirlwind tour through the cost conundrum shaking the foundations of VMware Cloud Foundation's license portability, and we do it all with the cheeky banter you've come to love. Plus, we're not shy about calling out the elephant in the room: the industry's skeptical eye on the promised TCO reductions. So buckle up, tech enthusiasts, as we dissect just how the Broadcom-VMware alliance is reshaping the game for everyone from fledgling startups to tech goliaths.The virtualization space is at a crossroads, and VMware's path is looking as rugged as the surface of Mars. Say farewell to the free ride with vSphere hypervisor ESXi and hello to potential new horizons with contenders like Proxmox and Nutanix. As we explore the ripples of VMware's licensing labyrinth, we also cast a spotlight on the startling layoffs at Cisco—no easy feat for a company that's been a bedrock in the tech landscape. In this cheeky chat, we're spilling the tea on Cisco's strategic shuffle and musing about how Nvidia's astronomical growth could be rewriting the rulebook for tech titans.Who needs a crystal ball when you've got the inside scoop on the cloud market's future? In the final stretch, we're breaking down the Aviatrix report's revelations on cloud cost optimization and why the big CSPs might be keeping those purse strings a bit too tight. Get ready for a lively debate on Microsoft's potential to outpace AWS by 2026 thanks to its ecosystem integration strategy. With our usual mix of sass and savvy, we promise you won't look at the cloud—or your cloud budget—the same way again after tuning into this episode of Cables to Clouds.Check out the Fortnightly Cloud Networking NewsVisit our website and subscribe: https://www.cables2clouds.com/Follow us on Twitter: https://twitter.com/cables2cloudsFollow us on YouTube: https://www.youtube.com/@cables2clouds/Follow us on TikTok: https://www.tiktok.com/@cables2cloudsMerch Store: https://store.cables2clouds.com/Join the Discord Study group: https://artofneteng.com/iaatjArt of Network Engineering (AONE): https://artofnetworkengineering.com

There are two new tools to help understand and calculate the required subscription capacity for the new VMware Cloud Foundation (VCF) and VMware vSphere Foundation (VVF) offerings, which are licensed based on physical CPU Cores for compute and total raw physical storage (TiBs) for vSAN. On this episode of the Virtually Speaking Podcast Pete and John welcome William Lam to discuss the details of these new scripts and how demonstrates with a live demo.  Links Mentioned  WilliamLam.com VMware KB 95927 - Counting Cores for VMware Cloud Foundation and vSphere Foundation and TiBs for vSAN VMware KB 96426 - License Calculator for VMware Cloud Foundation, VMware vSphere Foundation and VMware vSAN

On this week's episode I do a roundup of this month's Windows Updates, I get into the recent VMware announcement of the end of free vSphere hypervisors and much more! Reference Links: https://www.rorymon.com/blog/vmware-pulls-free-hypervisor-new-cvad-ltsr-patch-tuesday-news/

Cohesity is set to acquire the data protection assets of Veritas in a huge deal. Cohesity will be picking up the NetBackup portfolio as well as SaaS offering Alta. The move is seen as a huge bolster to Cohesity both in their cloud offerings as well as traditional on-prem enterrpise back and data protection. The deal will merge the two companies into a $7 billion. The remaining parts of Veritas that aren't purchased will be rebranded as DataCo. Time Stamps: 0:00 - Welcome to the Rundown 0:50 - NetApp and SpectraLogic Team Up for On-Prem Archival Storage 4:33 - Flipper Zero is Canada's Number One Enemy 9:18 - AI Continues to Shape Google Gemini 13:42 - Free VMware vSphere Hypervisor Ended by Broadcom 17:56 - Alcion Announces New MSP Partner Program 20:36 - Cohesity to Acquire Veritas Technologies 32:17 - The Weeks Ahead 34:10 - Thanks for Watching Follow our Hosts on Social Media Tom Hollingsworth: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.twitter.com/NetworkingNerd⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Stephen Foskett: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.twitter.com/SFoskett⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  Follow Gestalt IT Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.GestaltIT.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.twitter.com/GestaltIT⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/company/Gestalt-IT⁠ #Rundown, #Storage, #AI, #Data, #vSphere, #Gemini, #Security, #AIFD4, #NFD34, #XFD11, @Cohesity, @VeritasTechLLC, @AlcionHQ, @Broadcom, @VMware, @Google, @GoogleCloud, @Flipper_Zero, @NetApp, @SpectraLogic, @TheFuturumGroup, @GestaltIT, @SFoskett, @NetworkingNerd,

In this episode, join us as we delve into the intricate world of Platform Engineering with Aparna Subramanian, Director of Production Engineering at Shopify. Discover how Shopify, a powerhouse in e-commerce, masters the art of scaling platform engineering. Gain invaluable insights into their strategies, innovations, and lessons learned while navigating the complexities of sustaining and evolving a robust infrastructure to support millions, even through special peak events like Black Friday and Cyber Monday. If you're keen on understanding the backbone of a thriving online platform, don't miss out on this episode. Aparna started her career as a Software Engineer and has spent most part of her almost two decades of technology experience specializing in Infrastructure and Data Platforms. In her current role she leads Shopify's Cloud Native Production Platform. Previously, she was Director of Engineering at VMware where she was a founding member of Tanzu on vSphere, a Kubernetes Platform for the hybrid cloud. She also serves as co-chair of the “CNCF End User Developer Experience” SIG and as member of the CNCF End user technical advisory board. The episode was live-streamed on 11 January 2024 and the video is available at https://www.youtube.com/watch?v=6ShtsTTUizI OpenObservability Talks episodes are released monthly, on the last Thursday of each month and are available for listening on your favorite podcast app and on YouTube. We live-stream the episodes on Twitch and YouTube Live - tune in to see us live, and chime in with your comments and questions on the live chat. https://www.youtube.com/@openobservabilitytalks   ⁠https://www.twitch.tv/openobservability Show Notes: 00:00 - Show intro & 2023 stats 01:49 - Episode and guest intro 04:15 - Shopify's scale 06:09 - Shopify's journey to Platform Engineering 08:56 - Shopify's platform structure 11:49 - division of responsibility 13:51 - golden path vs flexibility 17:58 - balancing flexibility and abstraction 19:56 - platform group structure 23:28 - handling load spikes 28:55 - FinOps in Platform Engineering 38:38 - avoiding silos and the cultural aspect 41:13 - CNCF end-user SIG and community challenges 49:24 - KubeCon Paris and guest contact  51:03 - OpenTofu reached GA 53:33 - Isovalent acquired by Cisco 55:00 - year-end summary articles 57:07 - .NET Aspire released preview2 58:58 - Episode and show outro Resources: Shopify Engineering Blog https://shopify.engineering/ Performance wins at Shopify: https://www.shopify.com/news/performance%F0%9F%91%86-complexity%F0%9F%91%87-killer-updates-from-shopify-engineering CNCF End User SIG https://github.com/cncf/enduser-public OpenTofu has reached GA https://logz.io/blog/terraform-is-no-longer-open-source-is-opentofu-opentf-the-successor/?utm_source=devrel&utm_medium=devrel Observability in 2024: https://thenewstack.io/observability-in-2024-more-opentelemetry-less-confusion/ OpenTelemetry in 2024: https://www.apmdigest.com/2024-application-performance-management-apm-predictions-4 .NET Aspire preview2: https://devblogs.microsoft.com/dotnet/announcing-dotnet-aspire-preview-2/  Socials: Twitter:⁠ https://twitter.com/OpenObserv⁠ YouTube: ⁠https://www.youtube.com/@openobservabilitytalks⁠ Dotan Horovits ============ Twitter: @horovits LinkedIn: in/horovits Mastodon: @horovits@fosstodon Aparna Subramanian ================= Twitter: @aparnastweets LinkedIn: https://www.linkedin.com/in/subramanianaparna/ 

Welcome to episode 240! It's a doozy this week! Justin, Ryan, Jonathan and Matthew are your hosts in this supersized episode. Today we talk about Google Gemini, the GCP sales force (you won't believe the numbers) and Google feudalism. (There's some lovely filth over here!) Plus we discuss the latest happenings over at HashiCorp, Broadcom, and the Code family of software. So put away your ugly sweaters and settle in for episode 240 of The Cloud Pod podcast - where the forecast is always cloudy!  Titles we almost went with this week:

vSphere 8 Update 2 introduces several significant announcements to vVols such as new VASA specs, better performance and resilience, enhanced certificate management, and support for NVMeoF. On this episode of the Virtually Speaking Podcast Pete and John welcome Jason Massae and Sudhir Balasubramanian to discuss the details of this release.

This week VMware announced the latest release of VMware Data Services Manager (DSM), version 2.0. DSM enables the provisioning of data services (e.g., databases, object stores) on vSphere infrastructure. This release builds on earlier versions of VMware Data Services Manager 1.x, but also extends the product. On this episode of the Virtually Speaking Podcast Cormac Hogan shares the details of this new release.

Plants by nature are designed to interact with light. Satellites can measure the light reflected by plants to detect grapevine diseases before they are visible to the human eye. Katie Gold, Assistant Professor of Grape Pathology, Susan Eckert Lynch Faculty Fellow, School of Integrative Plant Science Plant Pathology and Plant-Microbe Biology Section of Cornell AgriTech is trailblazing remote disease detection with imaging spectroscopy also known as hyperspectral imaging. Imaging spectroscopy was developed by NASA to tell us what Mars was made out of. By turning satellites back on Earth, Katie and a team of scientists are learning how to use the light reflected back to manage grapevine viral and foliar diseases. Listen in to the end to get Katie's number one piece of advice on the importance of data management. Resources: Alyssa K. Whitcraft, University of Maryland Disease Triangle of Plant Pathology Gold Lab Katie Gold, Cornell University   Katie Gold - Twitter NASA AVIRIS (Airborne Visible and InfraRed Imaging Spectrometer) NASA Acres - applying satellite data solutions to the most pressing challenges facing U.S. agriculture NASA Emit Satellite NASA JPL (Jet Propulsion Laboratory) Planet Labs References: Vineyard Team Programs: Juan Nevarez Memorial Scholarship - Donate SIP Certified – Show your care for the people and planet   Sustainable Ag Expo – The premiere winegrowing event of the year - $50 OFF with code PODCAST23 Sustainable Winegrowing On-Demand (Western SARE) – Learn at your own pace Vineyard Team – Become a Member Get More Subscribe wherever you listen so you never miss an episode on the latest science and research with the Sustainable Winegrowing Podcast. Since 1994, Vineyard Team has been your resource for workshops and field demonstrations, research, and events dedicated to the stewardship of our natural resources. Learn more at www.vineyardteam.org.   Transcript Craig Macmillan  0:00  With us today is Katie Gold, Assistant Professor of Grape Pathology at Cornell AgraTech campus of the Cornell University. Thanks for being on the show.   Katie Gold  0:08  Well, thanks for having me.   Craig Macmillan  0:09  Today, we're going to talk about some really cool technology. I've been interested in it for a long time, and I can't wait to get an update on what all is happening. There's some really exciting work being done on using remote sensing for the detection of plant diseases. Can you tell us a little bit about what that research is about what's going on in that field?   Katie Gold  0:25  Sure, what isn't going on in this field, it's a really exciting time to be here. So I guess to put into context, we're really at this precipice of an unprecedented era of agricultural monitoring. And this comes from the intersection of you know, hardware becoming accessible, the data analytics becoming accessible, but also investment, you know, a lot of talk of ag tech being the next big thing. And with that comes this interest in using these cool and novel data streams for disease detection. So my group specializes in plant disease sensing, it's our bread and butter to what we entirely focus on. And we specialize in a technology called imaging spectroscopy for disease detection. So this is also known as hyperspectral imaging. Imaging spectroscopy is the technical term. And this is a type of remote sensing that it differs from, you know, radio wave remote sensing, and it focuses on light in the visible to shortwave infrared range.   Craig Macmillan  1:13  Talk a little bit more about that. So when we talk about hyperspectral, we're looking outside of the range of radiation, essentially, that's not just light.   Katie Gold  1:24  So yes, and no. So hyperspectral is a word that describes how the light is being measured, kind of colloquially, we assigned to it more meaning that it actually has. That's why I often like to differentiate between it for explanation sake, what hyperspectral imaging is, when we talk about using it in the full vSphere range, these are all types of light, you know, it's all aspects of the electromagnetic radiation scale. But this spectrum of light that ranges from the visible to the shortwave infrared, this spans a range of about 2100 wavelengths. So to put that into context, we see visible light only. And this spans a range of wavelengths, that's about 300 nanometers, and went from about 450 to 750. So if you think about all the richness of radiation, the subtlety in differences in color that you see in everyday light, all of that comes from those subtle interactions of, you know, specific wavelengths of light hitting that stuff and bouncing back into our eye. So now imagine having seven times more wavelengths than that, you know, we have 2100, different wavelengths that we measure. And those wavelengths that are beyond the range that we can see the reason why we don't see them as they're less abundant, they're less emitted by our sun, but they're still present, and they still interact with the world. In particular, they interact very strongly with chemistry, such as environmental chemistry. So imaging spectroscopy was developed by NASA to tell us what Mars was made out of, then one day, they're like, let's turn this baby around and pointed at the Earth. And we discovered that it's quite applicable for vegetative spectroscopy. So telling us what vegetation is made of what the composition of the Earth is. And because plant disease impacts chemistry, so dramatically, plant physiology, chemistry, morphology, such a dramatic chaotic impact. It's a really excellent technology to use for early detection. So those subtle little changes that occur within a plant before it becomes diseased to the human eye, but it's undergoing that process of disease.   Craig Macmillan  3:12  Can you expand on that point? Exactly how does this work in terms of the changes in the plant that are being picked up by viewing certain wavelengths? What's the connection there?   Katie Gold  3:23  Consider the leaf, right. So plants are an amazing thing to remotely sense because they're designed by nature to interact with light. Now that's in contrast to skin right that's designed to keep light out plants are designed to have light go in and out, etcetera. So light will enter our atmosphere from the sun, and it will do one of three things when it encounters a plant, it'll be reflected back, it will be absorbed for photosynthesis, or it will be transmitted through the plant. And the wealth of that light is actually reflected back. And that reflected light can be detected by something as distantly placed as a satellite in orbit. And how that light is reflecting off a plant is determined by the health status of a plant. So a healthy leaf, right? It's going to be photosynthesizing. This means that it's going to be absorbing red and blue light for photosynthesis, it's going to have a lot of chlorophyll, it's going to be nice, bright and green, it's going to reflect back a lot of green light. And then it's going to reflect back near infrared light, because that is the sort of light that corresponds really well to the cellular structure of a leaf, right, so a nice healthy leaf is going to bounce back near infrared light. Now an unhealthy plant, it's not going to be photosynthesizing properly. So it's going to be absorbing less red and blue light. Therefore, it will be reflecting more of that red light back, it's not going to have a lot of chlorophyll. So it's going to reflect back less green light, and it's not as healthy. It's not as robust, so it will reflect back less near infrared light. So by looking at those subtle differences, and this is where we get back to that idea of hyperspectral. Right. hyperspectral is a word about how a sensor is measuring light. And hyperspectral means that a sensor is measuring light at such narrow intervals, that it's a near continuous data product. And this is in contrast to a multispectral sensor something Like NDVI that measures light in big chunks. The power is when you have continuous data, right? You could do more complex analyses you just have more to work with. And when you have discrete data, this is what makes hyperspectral sensors more powerful. It's how they're measuring the light, and often, that they're measuring more light that our eyes can see. But that's not necessarily a given hyperspectral sensors do not need to measure beyond the visible range, they can solely be focused on the visual visible range. Because once again, hyperspectral is a word about how the light is being measured. But we oftentimes kind of colloquially, so assign more value to it. But let's take that in combination, right. So you have a hyperspectral sensor that's measuring light and very, very narrow intervals near continuous data product, you're measuring seven times more wavelengths than the eye can see, combined together. That's how this works, right? So those subtle differences and those wavebands how they're reflecting both direct interactions with plant chemistry, you know, some certain wavelengths of light will hit nitrogen bonds go wackadoo and bounce back, all crazy. Otherwise, we're making indirect inferences, right, you know, plant disease as a chaotic impact of plant health that impacts lots of areas of the spectrum. So we're not directly measuring the chemical impact, right? We're not saying okay, well, nitrogen is down two sugars are up three starch XYZ, we're measuring that indirect impact.   Craig Macmillan  6:19  That's pretty amazing. And so...   Katie Gold  6:21  I think it's cool, right? Yeah.   Craig Macmillan  6:24  The idea here is that there are changes in the leaf that can be picked up and these other wave lengths that we wouldn't see until it's too late.   Katie Gold  6:34  Exactly.   Craig Macmillan  6:35  Okay. So it's a warning sign. That gives us a chance to change management.   Katie Gold  6:40  Ideally, so. Right, so it depends on with the scale at which you're operating. So now here comes another level, right. So if you're considering just that one individual plant, it's different from when you're considering the whole scale of a vineyard, right, you want your sensing to be right size to the intervention that you're going to take. So my group works with two types of diseases primarily, we work with grape vine viral diseases, as well as grape vine foliar diseases, for example, a grape vine downy mildew, which is an Erysiphe caused by a Erysiphe pathogen, and grapevine powdery mildew, which is caused by a fungal pathogen. Now the sort of intervention that you would take for those two diseases is very different, right? With a viral disease, the only treatment that you have is removal, there's no cure for being infected with the virus. Now, with a fungal pathogen or an Erysiphe pathogen like grape downy mildew. If you detect that early, there are fungicides you can use with kickback action. Or otherwise, you might change the sort of what sort of choice you might make a fungicide right. If you know there's an actual risk in this location, you might put your most heavy hitting fungicides there than in areas where there is no disease detected, or the risk is incredibly low, you might feel more comfortable relying on a biological, thereby reducing the impact. So given the sort of intervention, you would take, we want to right size, our sensing approach for it. So with grapevine viral diseases, when the intervention is so has such a vast financial impact, right removal, we want to be incredibly sure of our data. So we focused on high spectral resolution data products for that ones, where we have lots of wavelengths being measured with the most precise accuracy so that we can have high confidence in that result, right? We want to give that to someone and say, Hey, we are very confident this is undergoing asymptomatic infection. Now, on the other hand, with these foliar diseases, they change at such a rapid timescale that you're more benefited by having an early warning that may be less accurate, right? So you're saying, hey, this area of your vineyard is undergoing rapid change it might be due to disease might be because your kid drove a golf cart through the vineyard, however, we're warning you regardless, to send someone out there and take a look and make a decision as to what you might do. Ideally, we would have a high spectral resolution regardless, right? Because more spectrum or better, but the realities of the physics and the actual logistics of doing the sensing is that we don't get to do that we have to do a trade off with spectral spatial and temporal resolution. So if we want rapid return, high degrees of monitoring, and we want that high spatial resolution suitable for a vineyard, we lose our spectral resolution, so we lose our confidence in that result. But our hope is that by saying, Hey, this is a high area of change, and giving you that information very quickly, you can still make an intervention that will be yield successful response, right? You'll go out there and you're like, Oh, yep, that's downy mildew. Otherwise, like, I'm going to take my kid keys like he's out here, my vineyard again. Right? So it's, it's kind of work balancing, right. So we have the logistics of the real world to contend with in terms of using sensing to make to inform management intervention.   Craig Macmillan  9:36  This technology can be used or applied at a variety of distances if I understand everything from proximal like driving through a vineyard to satellite.   Katie Gold  9:48  Oh, yeah. And we've worked with everything.   Craig Macmillan  9:50  Yeah, yeah. And everything in between. I mean, could you fly over is a lot of companies that do NDVIs with flyover.   Katie Gold  9:55  You can use robots like we do.   We can use robots, there's all kinds of things we can do. Or what is a what is NDVI for the audience, even though that's not what we're talking about. You and I keep using it.   So NDVI stands for Normalized Difference vegetative index. It's a normalized difference between near infrared light reflecting and red light. And it is probably the most accurate measurement we have of how green something is. And it's quite a powerful tool. As you you know, we've been using NDVI for well over 50 years to measure how green the earth is from space. That's powerful. But the power of NDVI is also its downside. And that because it is so effective at telling you how green something is, it cannot tell you why something is green. Or it cannot tell you why something is not green, it's going to pick up on a whole range of subtle things that impact plant health.   Craig Macmillan  10:40  And whereas the kind of work that you're doing differs from that in that it's looking at different frequencies, and a higher resolution of frequencies.   Katie Gold  10:51  Exactly. So for the most part, we do use NDVI. But we use it more as a stepping stone, a filtering step rather than the kind of end all be all. Additionally to we use an index that's a cousin to NDVI called EDI, that is adjusted for blue light reflectance, which is very helpful in the vineyard because it helps you deal with the shadow effects. Given the trellising system Iin the vineyard. But yes, exactly. We, for the most part are looking at more narrow intervals of light than NDVI and ranges beyond what NDVI is measuring.   Craig Macmillan  11:22  What's the resolution from space?   Katie Gold  11:24  That's a great question.   Craig Macmillan  11:25  What's the pixel size?   Katie Gold  11:27  One of the commercial satellite products we work with has half a meter resolution from space.   Craig Macmillan  11:32  Wow.   Katie Gold  11:33  Yeah, 50 centimeters, which is amazing. Yeah, that was exactly my reaction. When I heard about it, it was like I didn't get my hands on this. But as I mentioned before, right, you know, if that resolution, we trade off the spectral resolution. So actually, that imagery only has four bands, that effectively is quite similar to an NDVI sensor, that we do have a little more flexibility, we can calculate different indices with it. So we use that data product, 50 centimeters, we use three meter data products from commercial sources. And then we're also looking towards the future, a lot of my lab is funded by NASA, in support of a future satellite that's going to be launched at the end of the decade, called surface biology and geology. And this is going to put a full range Hyperspectral Imager into space that will yield global coverage for the first time. So this satellite will have 30 meter resolution. And it will have that amazing spectral resolution about 10 day return. And that 30 meter spatial size. So again, kind of mixing and matching, you don't get to optimize all three resolutions at once. Unfortunately, maybe sometime in my career, I'll get to the point where I get to optimize exactly what I want, but I'm not there yet.   Craig Macmillan  12:41  And I hadn't thought about that. So there's also a there's a time lag between when the data comes in and when it can be used.   Katie Gold  12:48  Yes.   Craig Macmillan  12:48  What are those lags like?   Katie Gold  12:50  It depends. So with some of the NASA data that we work with, it can be quite lagged, because it's not designed for rapid response. It's designed for research grade, right? So it's assuming that you have time, and it's going through a processing stage, it's going through corrections, etc. And this process is not designed to be rapid, because it's not for rapid response. Otherwise, sometimes when we're working with commercial imagery that can be available. If we task it, it can be available to us within 24 hours. So that's if I say, Hey, make me an acquisition. And they do and then within 24 hours, I get my imagery in hand. Otherwise to there's a there's delays up to seven days. But for the most part, you can access commercial satellite imagery of a scene of your choosing, generally within 24 hours of about three meter resolution to half a meter resolution. That is if you're willing to pay not available from the space agencies.   Craig Macmillan  13:42  I want to go back to that space agency thing first or in a second. What talk to me about satellite, we've got all kinds of satellites flying around out there.   Oh, we do.   All kinds of who's doing what and where and how and what are they? And how long are they up there. And...   Katie Gold  13:58  Well, I'll talk a little bit about the satellites that my program is most obsessed with. We'll call it that. I'll first start with the commercial satellite imagery that we use. This comes from Planet Labs. They're a commercial provider, they're quite committed to supporting research usages, but we've been using their data for three years now. Both they're tasked imagery, which is half a meter resolution, as well as their planet scope data, which is three meter resolution. And we've been looking at this for grapevine downy mildew. Planet Labs, their whole thing is that they have constellation architecture of cube sets. So one of the reasons why satellites are the big thing right now they are what everyone's talking about, is because we're at this point of accessibility to satellite data that's facilitated by these advances in hardware design. So one the design of satellites you know, we now have little satellites called CubeSats that are the size of footballs maybe a little bit bigger.   Craig Macmillan  14:48  Oh, really?   Katie Gold  14:48  Yeah, yeah, they're cool. They're cute. You can actually like kids science fair projects can design a CubeSat now, fancy kid school projects, at least not not where I was. As well as constellation architecture. So this is instead of having one big satellite, the size of a bus, you have something like 10, CubeSat, that are all talking to each other and working together to generate your imagery. So that's how you're able to have far more rapid returns, instead of one thing circling around the planet, you have 10 of them circling a little bit off. So you're able to get imagery far more frequently at higher spatial resolution. And this is now you know, trickled down to agriculture. Of course, you know, what did the Department of Defense have X years ago, they've, I'm excited to see what will finally be declassified eventually, right. But this is why satellite imagery is such a heyday. But anyway, that's, that's the whole Planet Labs stick, they use CubeSats and constellation design. And that's how they're able to offer such high spatial resolution imagery.   Craig Macmillan  15:44  Just real quick, I want to try understand this, you have x units, and they're spaced apart from each other in their orbit.   Katie Gold  15:52  That's my understanding. So remember, I'm the plant pathologist here I just usethis stuff. So that's my understanding is that the physicists, you know, and NASA speak, they classify us into three categories. They've got applications, like myself, I use data for something, you have algorithms, which is like I study how to make satellite, talk to the world, right, like, make useful data out of satellite. And then there's hardware people, right, they design the satellite, that's their whole life. And I'm on the other side of the pipeline. So this is my understanding of how this works. But yes, they have slightly different orbits, but they talk to each other very, very like intimately so that the data products are unified.   Craig Macmillan  16:33  Got it. But there's also other satellites that you're getting information from data from.   Katie Gold  16:37  Yes, yeah. So now kind of going on to the other side of things. So Planet Labs has lesser spectral resolution, they have four to eight, maybe 10 bands is the most that you can get from them. We're looking towards NASA surface biology and geology data. And we use NASA's Avaris instrument suite, the family suite, that includes next generation, as well as brand new Avaris three, and this stands for the Airborne, Visible and Infrared Imaging Spectrometer. Now, this is an aircraft mounted device, but this is the sort of sensor that we'll be going into space. Additionally, we're just starting to play around with data from the new NASA satellite called Emit. Emit is an imaging spectrometer that was initially designed to study dust emission. So like, tell us what the dust is made out of where it's coming from. But they've opened up the mask to allow its collection over other areas. And Emit has outstanding spectral resolution, and about 60 meter spatial resolution. It's based on the International Space.   Craig Macmillan  17:32  Station. It's located on the International Space Station?   Katie Gold  17:36  Yes, yeah. And that actually impacts how its imagery is collected. So if you take a look at a map of Emit collections, there are these stripes across the world. And that's because it's on the ISS. So it only collects imagery wherever the ISS goes. And that's a little bit different from this idea of constellation architecture, have these free living satellites floating through orbit and talking to each other.   Craig Macmillan  17:56  Are there other things like Landsat 7, Landsat 8?   Katie Gold  18:02  Oh, we're on Landsat 9 , baby!   Craig Macmillan  18:04  Oh, we're on Landsat 9 now. Cool.   Katie Gold  18:05  Yeah. Yeah, Landsat 9 was successfully launched. I'm really excited about its data.   Craig Macmillan  18:10  And it's coming in?   Katie Gold  18:11  Just to my understanding, yes, so we don't use Landsat and Sentinel data as much otherwise, our focus is on that spectral resolution, but Landsat 9 and its its partner from the European Space Agency's Sentinel 2, they're truly the workhorses of the agricultural monitoring industry. Without those two satellites, we would be in a very different place in this world.   Craig Macmillan  18:32  Right, exactly. Now, you said that your work is funded partially or all by NASA?   Katie Gold  18:37  Yes, partially.   Craig Macmillan  18:38  So partially, so what is the relationship there?   Katie Gold  18:40  So before I started with Cornell, I was hired by Cornell while I was still a graduate student, and as part of their support for my early career development, they sponsored a short postdoc for me a fellowship, they called it I got to stay with a faculty fellow feel better about myself at the Jet Propulsion Laboratory, where my graduate co advisor Phil Townsend had a relationship with so I spent nine months fully immersed in JPL. People think of JPL is like, you know, the rocket launchers, which they are, but they also study, you know, like some of those phase out and go out into the world. But some of the things they launched turn around and study the Earth, and they had the carbon and ecosystem cycling group there. So I was able to work with them, as well as the imaging spectroscopy group for nine months. And it completely changed my entire life just opened up the world to me about what was possible with NASA data, what was coming for potential use of NASA data. And it really changed the trajectory of my career. So I made connections, made friends got my first graduate student from JPL, that have truly defined my career path. So I work very closely with NASA, originating from that relationship, as well as I'm the pest and disease risk mitigation lead for the newly established domestic agriculture consortium called NASA Acres. So this is NASA's most recent investment in supporting domestic agriculture. Through this consortium we're funded to continue some of our research myself and my good colleague, Yu Jiang who's an engineer who builds me my robots. It's confounding our work continuously, as well as giving us the opportunity to try to expand our approach to other domains through interactions, one on one, collaborations with other researchers and importantly work with stakeholders. And this consortium, the Acres consortium is led by my colleague, Dr. Alyssa Woodcraft, based at the University of Maryland.   Craig Macmillan  20:20  Going back to some of the things that you mentioned earlier, and I think I just didn't ask the question at the time, how often does the satellite travel over any particular point on Earth?   Katie Gold  20:32  So it depends on the type of satellite design. Is it the big one satellite sort of design? Or is it constellation? Or the ISS, right? Like they think the ISS orbits every 90 minutes, something like that? So it really depends, but their satellites crossing us overhead every moment. I think at night, if you ever look up into the night sky, and you see a consistent light, just traveling across the world, not blinking. That's a satellite going overhead.   Craig Macmillan  20:59  Wow, that's amazing. Actually, are there applications for this technology on other crops?   Katie Gold  21:04  Oh, certainly. So yeah. Oh, absolutely. So the use of this technology for understanding vegetative chemistry was really trailblaze by the terrestrial ecologist, in particular, the forest ecologist because it's a, you know, it's how you study things at scale, unlike the vineyards would have nice paths between them for researchers like myself, and you know, us all to walk between forests are incredibly difficult to navigate, especially the ones in more remote locations. So for the past two decades, it really spear spearheaded and trailblaze this use, and then I work with vineyards for the most part, I'm a grape pathologist, I was hired to support the grape industry, they saw the research I was doing, they said, great, keep doing it in garpes. So I'm a reformed potato and vegetable pathologist, I like to say, but there's no reason at all why the work I'm doing isn't applicable to other crops. I just happened to be doing it in grape, and I happen to really adore working with the wine and grape industry.   Craig Macmillan  21:54  Yeah, yeah, absolutely. That, it totally makes sense. How is this translating are going to translate for growers into grower practices?   Katie Gold  22:02  That's a great question. So the idea is that by trailblazing these functionalities, eventually, we'll be able to partner with commercial industry to bring this to growers, right. We want these this utility to be adopted for management intervention. But there's only so much one academic lab alone can do and the my role in the world is to trailblaze the use cases and then to partner with private industry to bring it to the people at scale. But the hope is that, you know, I want every venue manager to be looking at aerial images of their vineyards. Every day, right? I have a vision of interactive dashboards, maps of informed risk. One day, I want to have live risk maps informed by remote sensing. And I want every vineyard manager to be as familiar with their aerial view of their vines as they are with that side view of their vines. Right. And I think we're getting there sooner than you realize we're really at the precipice of this unprecedented era of monitoring or monitoring ability, right? And I'm really excited about what it will hold for management.   Craig Macmillan  23:02  And so you must have cooperators I'm guessing.   Katie Gold  23:05  Oh, I do. Yes. I've wonderful cooperators.   Craig Macmillan  23:08  At this stage. It sounds like we're still kind of in a beta stage.   Katie Gold  23:13  Oh, yes, very much in the beta stage.   Craig Macmillan  23:15  So I'm guessing that you're looking at imagery and spotting areas that would suggest that there's some kind of a pathology problem, and then you're going on ground truthing it?   Katie Gold  23:27  So yes, and no, it's more of a testbed sort of case study. We have nine acres of pathology vineyards here at Cornell, Agrotech, and Geneva, New York. And then we do partner with cooperators. We have wonderful cooperators based out in California, as well as here in New York. But those are for more on testbed sort of thing. So we're not just monitoring vineyards, and like watching them and say, Ooh, the spot appears here. We're doing more of a case studies where we intentionally go out and ground truth, then build those links between the imagery because we're not quite there yet, in terms of having this whole thing automated, we're still building those algorithms building that functionality. Now we've established proof of concept. You know, we know this works. So we're working on the proof of practicality, right? Building robust pipelines, ones that are that are resilient to varying environmental geographic conditions, right, different crop varieties resilient to confounding abiotic stress, that one drives us nuts. So that's the stage that we're at, but our collaborators and our industry stakeholders who partner with us. Without them the sort of work I do just simply would not be possible. And I'm extremely grateful for their part.   Craig Macmillan  24:29  So what, what is next, what's next in the world of Katie Gold and in the world of hyperspectral plant pathology?   Katie Gold  24:34  What's next for me is in a week, I'm boarding an airplane to go to Europe for a jaunt. I'm giving two international keynotes at plant pathology conferences about methods but what I really see as next for me is I really want to see the tools that technologies the approach that my group is using, percolate through the domain of plant pathology. We're such a small discipline, there's only about 2000 of us Around the world, in plant pathology, and you know, there's not even 10, great pathologist in this country, I can name every single one of them if you wanted me to. And I think I've got their number and my phone, really, I strongly believe we're at the precipice of such an exciting era in plant pathology, due to the availability of these imagery, these data streams, just simply an unprecedented era. And it will be a paradigm shift in how we ask and answer questions about Plant Pathology, because for the first time, we have accessible, accurate imagery that we can use to study plant disease at the scale at which it occurs in the field in real time. So I want to see these ideas percolate through the skill sets adopted, taken up and embraced and it we're seeing that start, you know, we're seeing that start, there's really excitement in plant pathology, about the use of remote sensing about GIS and that skill set in its value to our discipline. But I'd really like to see that expand. I think I am the first ever plant pathologist to receive funding from NASA Earth Science Division. When I started at JPL, they would introduce me as a disease ecologist, because no one had ever heard of plant pathology. And my wonderful colleague at JPL, Brian Pavlik, who's a JPL technologist, when we started working together, he had never once been into a vineyard. He didn't know about Plant Pathology, he was the one that called me a disease ecologist. And recently, I heard him explain the disease triangle to someone, which is, of course, the fundamental theory of plant pathology. And I was just so proud. But it also really represented this real excitement for me this embrace this acknowledgement of the challenges we face in plant pathology in these domains that otherwise have not heard of us, right and beyond the USDA, funding from NASA, just awareness from these other organizations, excitement from engineers, AI experts about solving plant disease problems. It's truly invigorating and exciting to me. That's where I see you going next. And I'm really excited about the future.   Craig Macmillan  26:51  There was one thing that you could say to grape growers on this topic, what would it be?   Katie Gold  26:58  Oh, that's such a great question. There's so much that I want to say.   Craig Macmillan  27:01  One thing, Katie.   Katie Gold  27:04  I would say your data is valuable and to be aware of how you keep track of your data, that the keeping track of your data, keeping your data organized, keeping, just having reproducible organized workflows will enable you to make the most out of these forthcoming technologies. It will enable you to calibrate it will enable you to train these technologies to work better for you, but your data is valuable, don't give it away to just anyone and to be aware of it.   Craig Macmillan  27:33  I agree wholeheartedly. And I think that applies everything from how much time it takes to leaf an acre of ground. And how much wood you are removing when you prune to when and how much water you're applying. Data is gold.   Katie Gold  27:49   Data is gold.   Craig Macmillan  27:50  It takes time and energy.   Katie Gold  27:52  Institutional knowledge. For example, my field research manager Dave Combs has been doing this job for over 25 years, I inherited him from my predecessor, and he trained our robot how to see disease in its imagery. And the goal of our robots is not to replace the expertise like Dave, but to preserve them right to preserve that 25 years of knowledge into a format that will live beyond any of us. So I see keeping track of your data keeping track of that knowledge you have, you know, you know, in your vineyard where a disease is going to show up first, you know your problem areas, keeping track of that in an organized manner, annotating your datasets. I'm starting to adopt GIS in a way just simply like, here are my field boundaries, even simply just taking notes on your in your data sets that are timed and dated. I think it's incredibly important.   Craig Macmillan  28:38  Where can people find out more about you and your work?   Katie Gold  28:41  Well, so you can visit my Web website or I've got a public Twitter page where you can see me retweet cool things that I think are cool. I tweet a lot about NASA I tweet a lot about Greek disease. If you want to see pictures of dying grapes come to my Twitter page, as well as Cornell regularly publishes things about me.   Craig Macmillan  28:57  Fantastic.   Katie Gold  28:58  So be sure to Google Katie Gold Cornell.   Cornell that's the key. Yeah, Katie go to Cornell or you might get an unwelcome surprise.   Craig Macmillan  29:04  And we have lots of links and stuff on the show page. So listeners you can go there. I want to thank our guest today.   Unknown Speaker  29:13  Thank you so much for having me, Craig. This has been wonderful.   Craig Macmillan  29:16  Had Katie Gould, Assistant Professor of rape pathology at Cornell agritech campus of Cornell University.    Nearly Perfect Transcription by https://otter.ai

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At VMware Explore there were various announcements around vSphere+. Considering vSphere+ is still a relatively new offering we wanted to make sure that we would not only  cover the basics but also got to hear all of the intricate details from an expert like Dave Morera!Make sure to follow Dave on Twitter (https://twitter.com/GreatWhiteTec) to keep up to date with his adventures, and check out the VMware website for more documents, whitepapers, and announcements around vSphere+ here: https://core.vmware.com/vsphere#vsphereFollow us on Twitter for updates and news about upcoming episodes: https://twitter.com/UnexploredPod.Last but not least, make sure to hit that subscribe button, rate where ever possible, and share the episode with your friends and colleagues!

Today's Tech Bytes podcast, sponsored by VMware, dives into VMware's vSphere+. vSphere+ allows you to operate your on-prem workloads and infrastructure as if they were a public cloud. It supports VMs and Kubernetes, and provides admin, developer, and add-on services delivered via SaaS.

Today's Tech Bytes podcast, sponsored by VMware, dives into VMware's vSphere+. vSphere+ allows you to operate your on-prem workloads and infrastructure as if they were a public cloud. It supports VMs and Kubernetes, and provides admin, developer, and add-on services delivered via SaaS. The post Tech Bytes: Run On-Prem Infrastructure Like Public Cloud With vSphere+ (Sponsored) appeared first on Packet Pushers.