Kubernetes Resource Management

Most teams add a large buffer to CPU and memory requests to ensure performance and reliability, but that leads to over-allocated resources in Kubernetes clusters, which results in wasteful spending on compute resources.

Using machine learning (ML) to analyze Kubernetes resource utilization, StormForge provides recommendations for appropriate resource allocation. This ensures that pod resource requests and limits match actual needs, reducing resource waste and cloud costs while maintaining optimal performance. 

Dynamic workloads make it difficult to predict the resource usage of workloads, resulting in inefficient resource utilization and overprovisioning.

Monitoring seasonal trending across compute resources using ML, StormForge learns the scaling patterns of the horizontal pod autoscaler (HPA) to dynamically adjust resources and account for these behaviors. Then, by recommending values for resource requests and resource limits, StormForge accounts for unpredictable scaling challenges to achieve efficient resource utilization.

Manually managing resource allocation can be time-consuming and error prone, especially as the number of workloads grows. While it may be simple to set resource requests and resource limits for a handful of workloads, doing this across thousands of workloads with changing traffic patterns is near impossible. DIY efforts eventually end in wasted engineering time and disappointment.

By automating optimization of Kubernetes resource requests and resource limits, StormForge eliminates the manual toil required to adjust configurations across environments. These recommendations can be deployed automatically as patches against the Kubernetes API or exported as a yaml file and integrated into GitOps workflows to ensure efficient resource utilization without human intervention.