Podcasts about little's law

Like most things, there's probably more myths than truths about Little's Law out there on the interwebs. How many myths do you know about? Join Dan and Prateek on the next episode of #DrunkAgile to find out!

Dan and Prateek take a deep-ish dive into Little's Law. They cover the theorem and the assumptions behind it and why is it important to understand them. They explain how paying attention to Little's Law and its assumptions helps create a stable, predictable system.

Little's law is an astounding equation that's dead simple, yet it can bring an amazing insight into what your distributed system is capable of. Read more: https://256.nurkiewicz.com/6 Newsletter: https://256.nurkiewicz.com/newsletter More resources: * Little's law: https://en.wikipedia.org/wiki/Little%27s_law * John Little: https://en.wikipedia.org/wiki/John_Little_(academic) * Node.js and CPU intensive requests: https://stackoverflow.com/questions/3491811/node-js-and-cpu-intensive-requests * My talk where I mention Little's law (from 23:03: https://www.youtube.com/watch?v=5TJiTSWktLU&t=23m03s)

You can't make an hourglass flow faster by yelling at the sand. Discussed: queuing, constraints, Little's Law.

Learn about why it’s a good thing when you have to wait in a long line; new research that says an ancient supernova may have triggered a mass extinction on Earth; the delicious origin story of Key lime pie; and how and why we forget pain.   In this podcast, Cody Gough and Ashley Hamer discuss the following stories from Curiosity.com to help you get smarter and learn something new in just a few minutes:   Why Is This Line So Long? — https://curiosity.im/2LNuYJk An Ancient Supernova May Have Triggered a Mass Extinction on Earth — https://curiosity.im/2LWKbb6 Key Lime Pie Is a Modern Delicacy With Humble Beginnings — https://curiosity.im/2s7bPZO   If you love our show and you're interested in hearing full-length interviews, then please consider supporting us on Patreon. You'll get exclusive episodes and access to our archives as soon as you become a Patron! https://www.patreon.com/curiositydotcom Download the FREE 5-star Curiosity app for Android and iOS at https://curiosity.im/podcast-app. And Amazon smart speaker users: you can listen to our podcast as part of your Amazon Alexa Flash Briefing — just click “enable” here: https://curiosity.im/podcast-flash-briefing.

This week starts with Amos describing the new features elixir 1.7 and Chris explaining some queueing theory. Afterwards Anna describes her experience solving a bug in her crypto-currency exchange. This leads to a discussion on how to find bugs in distributed systems, the dangers of generating data based on types and how to guide generators so they find more bugs.

In today's episode, Chuck talks about Little's Law from queuing theory, which plays a prominent role in lean and Kanban. Queuing theory comes up a lot in our society, so you may learn something about shopping and driving as well. This episode is sponsored by our friends and generous backers on Patreon. Sponsors are needed to help the podcast grow and thrive. Sign up today!Support the show (https://www.patreon.com/agilechuckwagon)

Timing is everything. If a system does not respond in a timely manner then: at best, its value is greatly diminished; and at worst, it is effectively unavailable. Reactive systems need to meet predictable response time guarantees regardless of load or datasets size, even in the presence of burst traffic and partial failure conditions. In this talk we will explore what is means to be responsive and the fundamental design patterns required to meet predictable response time guarantees. Queueing theory, Little's Law, Amdahl's Law, Universal Scalability Law - we'll cover the good bits. Then we'll explore algorithms that work with these laws to deliver timely responses from our applications no matter what gets thrown at them. Martin Thompson - High-performance and low-latency specialist Martin is a high-performance and low-latency specialist, with experience gained over two decades working on the bleeding edge of large transactional and big-data systems. He believes in Mechanical Sympathy, i.e. applying an understanding of the hardware to the creation of software as being fundamental to delivering elegant high-performance solutions. The Disruptor framework is just one example of what his mechanical sympathy has created. Martin was the co-founder and CTO of LMAX. He blogs at mechanical-sympathy.blogspot.com, and can be found giving training courses on performance and concurrency when he is not cutting code to make systems better.

One of the fundamental problems of I/O is a core problem of communication: How do I know that the recipient is ready to receive my information? In a pure push model, without taking into consideration Little's Law, it is very easy to produce data at a faster pace than it can be consumed, leading to loss of information, contention on the medium, or even full system failure. Reactive Streams provide an asynchronous, concurrent, back-pressured/demand-driven solution for that and other problems. We will discuss this and more in this session. To view the video visit www.parleys.com.

This lecture continues with an analysis of sliding window protocol and how it handles packet loss. Little's Law is introduced to relate the average number of packets to the average service rate and average delay of a stable system.