Kubernetes is ruling the container market. We’ll go over 9 methods for Kubernetes cost optimization that can be used for a variety of Kubernetes use cases.
Join the DZone community and get the full member experience. Kubernetes is ruling the container market. According to a CNCF survey, the use of Kubernetes in production in 2020 was 93%, up from 78% in 2019. Moreover, the survey reveals that the use of containers in production in 2020 was 92%. This figure is up 300% from CNCF’s first survey in 2016. Due to the adoption of Kubernetes by DevOps teams and the open source community’s encouragement, this figure could grow more. And if it stays at present prices, this market share still is a significant portion. This means that even though Kubernetes makes a lot of things easier, challenges will always appear, as the survey confirms. Namely, the problems listed include networking, storage, tracking, surveillance, a lack of preparation, and, of course, cost management. Running Kubernetes can be very costly, especially if done inefficiently. When businesses first try to incorporate Kubernetes in their organizations, they usually use the same architecture and setup that performed well with initial research experiments. However, this setup is often unoptimized, and companies don’t think about expenses right away. This could save a lot of unnecessary costs and encourage the implementation of good habits from the beginning. In this article, we’ll go over several methods for controlling and lowering Kubernetes costs. Moreover, as Amazon EKS is the most common container management approach after self-managed Kubernetes, we’ll offer more actionable advice on Kubernetes cost optimization on AWS. This is the most logical step towards starting to manage your Kubernetes costs more efficiently. Monitoring should show you how you’re spending your money when it comes to Kubernetes. More importantly, you should identify saving opportunities. Cloud vendors offer billing summary that provides information about what you’re paying for. However, they will usually only include a simple overview that is only slightly useful for multi-tenant Kubernetes clusters. This is inaccessible in private clouds. As a consequence, it’s popular to use external software to monitor Kubernetes consumption. Prometheus, Kubecost, Microtica, and Replex are some useful tools in this field. Choose the tools you’ll use and how you’re going to monitor your Kubernetes costs. Then, start implementing more concrete actions for Kubernetes cost optimization. Resource constraints that are effective guarantee that no program or operator of the Kubernetes system uses too much processing power. As a result, they protect you from unwelcome shocks such as unexpected billing changes. A container can’t use more than the resource limit you set. The kubelet (and container runtime) implement the memory cap if you set it to, for example,4GB for a particular container. The container’s runtime prohibits it from exceeding the configured resource cap. When a process in the container attempts to use more memory than is permitted, the system kernel aborts the process with an out-of-memory (OOM) error. Developers can enforce limits in two ways. First, reactively, when the system detects a violation. The second way is by enforcement, which means that the system never allows the container to go over the limit. They can implement the same constraints in various ways for different runtimes. Limiting resources is crucial, especially if many of your developers have direct access to Kubernetes. They ensure that available resources are shared fairly, reducing the overall cluster size. Without limitations, one person could use all energy. This would prevent others from working, resulting in a need for more computational resources overall.
