Choosing accurate CPU and memory request values for Kubernetes workloads is a difficult endeavor. This difficulty results in application developers overprovisioning their workloads to ensure that application performance will not be affected. This can lead to increasing cloud costs and inefficient resource usage. In addition, it is also possible that workloads can be underprovisioned inadvertently. This can negatively affect application performance and potentially even lead to service disruptions.
In this blog, we describe how Kubernetes Vertical Pod Autoscaler (VPA) can be leveraged in conjunction with Luna, a powerful cluster autoscaler - to ensure that Kubernetes workloads are right-sized by VPA and the Kubernetes cluster as well as nodes are right-sized by Luna - resulting in cost-effective and performant operations. |
RSS Feed