The DiRT on Chaos Engineering at Google • Jason Cahoon • GOTO 2021

COURTNEY NASH: Prerequisites for Chaos Engineering

Chaos Engineering is often characterized as “breaking things in production” which lends it an air of something only feasible for elite or sophisticated organizations. In practice, it’s been a key element in digital transformation from the ground up for a number of companies ranging from pre-streaming Netflix to those in highly regulated industries like healthcare and financial services. In this talk, you’ll learn the basic prerequisites for Chaos Engineering, including a couple pragmatic ways to get started.

JASON CAHOON: The DiRT on Chaos Engineering @ Google

A shallow dive into 15 years of Chaos Engineering at Google, the lessons we’ve learned performing many thousands of disaster tests on production systems, and some tips on how to approach getting started with Chaos Engineering at your own organization.

TIMECODES

• 00:00 Intro
• 01:02 DiRT: Disaster Resiliency Testing
• 02:53 Why?
• 04:38 What we test?
• 06:01 Testing themes
• 10:01 Practical vs theoretical
• 12:31 How?
• 15:12 Picking what to test
• 16:29 Steps for bootstrapping a disaster testing program
• 18:25 Testing production vs testin in production
• 20:16 Really, you’re breaking production though?!
• 23:00 Reporting on results
• 24:24 What have we learned?
• 26:55 Test example: Run at service level
• 28:51 Test example: Toggle the O-N / O-F-F discriminator
• 30:25 Test example: Run without dependencies
• 31:53 Test example: Hacked!

#chaos #chaos-engineering #developer

What is GEEK

Buddha Community

What Are Google Compute Engine ? - Explained

What Are Google Compute Engine ? - Explained

The Google computer engine exchanges a large number of scalable virtual machines to serve as clusters used for that purpose. GCE can be managed through a RESTful API, command line interface, or web console. The computing engine is serviced for a minimum of 10-minutes per use. There is no up or front fee or time commitment. GCE competes with Amazon’s Elastic Compute Cloud (EC2) and Microsoft Azure.

https://www.mrdeluofficial.com/2020/08/what-are-google-compute-engine-explained.html

#google compute engine #google compute engine tutorial #google app engine #google cloud console #google cloud storage #google compute engine documentation

The DiRT on Chaos Engineering at Google • Jason Cahoon • GOTO 2021

COURTNEY NASH: Prerequisites for Chaos Engineering

Chaos Engineering is often characterized as “breaking things in production” which lends it an air of something only feasible for elite or sophisticated organizations. In practice, it’s been a key element in digital transformation from the ground up for a number of companies ranging from pre-streaming Netflix to those in highly regulated industries like healthcare and financial services. In this talk, you’ll learn the basic prerequisites for Chaos Engineering, including a couple pragmatic ways to get started.

JASON CAHOON: The DiRT on Chaos Engineering @ Google

A shallow dive into 15 years of Chaos Engineering at Google, the lessons we’ve learned performing many thousands of disaster tests on production systems, and some tips on how to approach getting started with Chaos Engineering at your own organization.

TIMECODES

• 00:00 Intro
• 01:02 DiRT: Disaster Resiliency Testing
• 02:53 Why?
• 04:38 What we test?
• 06:01 Testing themes
• 10:01 Practical vs theoretical
• 12:31 How?
• 15:12 Picking what to test
• 16:29 Steps for bootstrapping a disaster testing program
• 18:25 Testing production vs testin in production
• 20:16 Really, you’re breaking production though?!
• 23:00 Reporting on results
• 24:24 What have we learned?
• 26:55 Test example: Run at service level
• 28:51 Test example: Toggle the O-N / O-F-F discriminator
• 30:25 Test example: Run without dependencies
• 31:53 Test example: Hacked!

#chaos #chaos-engineering #developer

Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

The Principles of Chaos Engineering

Resilience is something those who use Kubernetes to run apps and microservices in containers aim for. When a system is resilient, it can handle losing a portion of its microservices and components without the entire system becoming inaccessible.

Resilience is achieved by integrating loosely coupled microservices. When a system is resilient, microservices can be updated or taken down without having to bring the entire system down. Scaling becomes easier too, since you don’t have to scale the whole cloud environment at once.

That said, resilience is not without its challenges. Building microservices that are independent yet work well together is not easy.

What Is Chaos Engineering?

Chaos Engineering has been around for almost a decade now but it is still a relevent and useful concept to incorporate into improving your whole systems architecture. In essence, Chaos Engineering is the process of triggering and injecting faults into a system deliberately. Instead of waiting for errors to occur, engineers can take deliberate steps to cause (or simulate) errors in a controlled environment.

Chaos Engineering allows for better, more advanced resilience testing. Developers can now experiment in cloud-native distributed systems. Experiments involve testing both the physical infrastructure and the cloud ecosystem.

Chaos Engineering is not a new approach. In fact, companies like Netflix have been using resilience testing through Chaos Monkey, an in-house Chaos Engineering framework designed to improve the strength of cloud infrastructure for years now.

When dealing with a large-scale distributed system, Chaos Engineering provides an empirical way of building confidence by anticipating faults instead of reacting to them. The chaotic condition is triggered intentionally for this purpose.

There are a lot of analogies depicting how Chaos Engineering works, but the traffic light analogy represents the concept best. Conventional testing is similar to testing traffic lights individually to make sure that they work.

Chaos Engineering, on the other hand, means closing out a busy array of intersections to see how traffic reacts to the chaos of losing traffic lights. Since the test is run deliberately, more insights can be collected from the process.

#devops #chaos engineering #chaos monkey #chaos #chaos testing

Embedding your <image> in google colab <markdown>

This article is a quick guide to help you embed images in google colab markdown without mounting your google drive!

Just a quick intro to google colab

Google colab is a cloud service that offers FREE python notebook environments to developers and learners, along with FREE GPU and TPU. Users can write and execute Python code in the browser itself without any pre-configuration. It offers two types of cells: text and code. The ‘code’ cells act like code editor, coding and execution in done this block. The ‘text’ cells are used to embed textual description/explanation along with code, it is formatted using a simple markup language called ‘markdown’.

Embedding Images in markdown

If you are a regular colab user, like me, using markdown to add additional details to your code will be your habit too! While working on colab, I tried to embed images along with text in markdown, but it took me almost an hour to figure out the way to do it. So here is an easy guide that will help you.

STEP 1:

The first step is to get the image into your google drive. So upload all the images you want to embed in markdown in your google drive.

Step 2:

Google Drive gives you the option to share the image via a sharable link. Right-click your image and you will find an option to get a sharable link.

On selecting ‘Get shareable link’, Google will create and display sharable link for the particular image.

#google-cloud-platform #google-collaboratory #google-colaboratory #google-cloud #google-colab #cloud