Embracing failures and cutting infrastructure costs: Spot instances in Kubernetes

Embracing failures and cutting infrastructure costs: Spot instances in Kubernetes

  • November 9, 2018
Table of Contents

Embracing failures and cutting infrastructure costs: Spot instances in Kubernetes

Spot Instances are unused servers that are available for less than the regular price. Therefore, you can significantly save on your infrastructure costs. It does come with a price, though.

Your cloud provider can take away your spot instance at any time, and give to another client who has requested it at a standard cost. How can you save money, but work around disappearing servers? Learn how you can leverage Kubernetes to self-heal your infrastructure and cut costs with Spot Instances.

The last decades have seen a global shift from on-premise data centres to the provisioning of Virtual Machines (VMs) from mainstream cloud providers such as Amazon Web Services, Azure, Google Cloud Platform. Running and managing your own physical machines is hard and costly; chances are you’ll never be as successful and efficient as any of the top cloud providers. And what’s not to love when you can leverage a mature platform and features such as: In this article, we will explore the different pricing models of a typical cloud provider.

We will focus on one strategy and see how it could cut your bill by up to 80% if you are willing to trade in reliability. Finally, we will see how Kubernetes makes that lack of reliability irrelevant and allows you to run a cheap yet highly available cluster. The typical pricing model for cloud providers is based on a pay-as-you-go scheme.

Compute resources come in different sizes (i.e. memory, CPU, disk etc..) and an hourly cost. You get billed for the amount of time the instance is running.

Source: learnk8s.io

Share :
comments powered by Disqus

Related Posts

Tensorflow 2.0: models migration and new design

Tensorflow 2.0: models migration and new design

Tensorflow 2.0 will be a major milestone for the most popular machine learning framework: lots of changes are coming, and all with the aim of making ML accessible to everyone. These changes, however, requires for the old users to completely re-learn how to use the framework: this article describes all the (known) differences between the 1.x and 2.x version, focusing on the change of mindset required and highlighting the pros and cons of the new and implementations. This article can be a good starting point also for the novice: start thinking in the Tensorflow 2.0 way right now, so you don’t have to re-learn a new framework (unless until Tensorflow 3.0 will be released).

Read More
Horizon: An open-source reinforcement learning platform

Horizon: An open-source reinforcement learning platform

Horizon is the first open source end-to-end platform that uses applied reinforcement learning (RL) to optimize systems in large-scale production environments. The workflows and algorithms included in this release were built on open frameworks — PyTorch 1.0, Caffe2, and Spark — making Horizon accessible to anyone using RL at scale. We’ve put Horizon to work internally over the past year in a wide range of applications, including helping to personalize M suggestions, delivering more meaningful notifications, and optimizing streaming video quality.

Read More
Modernizing your build pipelines

Modernizing your build pipelines

Doing Continuous Integration is a lot easier if you have the right tools. In our project at a german car manufacturer, we were tasked with developing new services and bringing them to the cloud. We had a centralized Jenkins instance, shared by all the teams in the department.

Read More