Kubernetes CPU Requests & Limits VS Autoscaling

In this blog post, Jason Umiker discusses the intricacies of managing CPU resources in Kubernetes environments, focusing on the roles and limitations of CPU requests and limits. The article explains how CPU requests ensure that a container has a guaranteed minimum amount of CPU resources, which helps with scheduling and resource allocation, whereas CPU limits cap the maximum CPU resources a container can use but can inadvertently lead to performance issues, especially in multithreaded applications. The post highlights that while CPU requests are optional, they are crucial for maintaining availability and performance, and that limits are often misunderstood and misconfigured. It suggests alternatives like relying on Kubernetes' Horizontal Pod Autoscaler and Cluster Autoscaler for dynamic scaling as more effective strategies in cloud environments due to their flexibility and cost efficiency. The article also discusses how different programming languages handle CPU resources and the importance of aligning thread counts with limits, with some languages providing automatic detection and adjustment capabilities. Finally, it introduces Sysdig Monitor as a tool to help optimize Kubernetes resource allocation and reduce costs.