For any growing Kubernetes environment, managing resources effectively requires ensuring performance and scalability while balancing cost efficiency. StormForge Optimize Live automates and optimizes Kubernetes resource management using machine-learning-based rightsizing recommendations to maximize performance and minimize waste across your Kubernetes clusters.
Addressing Common Kubernetes Resource Management Pain Points #
Inefficient resource utilization, escalating cloud costs, and reliability issues are common challenges that emerge from improperly managing Kubernetes resources. Let's dive deeper into all of those and how StormForge can help.
Overprovisioning that Leads to Cloud Waste
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.
Underprovisioning that Causes Performance Issues
Underprovisioning CPU or memory resources in your Kubernetes cluster can lead to reliability issues such as CPU throttling and out of memory (OOM) kills.
By automatically adjusting pod resource requests and limits based on workload patterns, StormForge ensures that each individual pod has the available resources needed to run efficiently. Recommendations are forecast to account for spikes in resource needs across all resource types.
Unpredictable Workloads and Scaling Challenges
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.
Lack of Visibility into Kubernetes Resource Waste
Without clear insights into how pods are utilizing compute resources, it’s impossible to optimize resource consumption. Manually tracking resource utilization to identify inefficiencies in the Kubernetes environment is a constant struggle.
By ingesting metrics around resource usage and application performance, StormForge provides detailed insights into resource consumption. This enables you to monitor CPU usage, memory usage, requests and limits, and other key metrics across your Kubernetes environment. All views can be viewed by the cluster, namespace, or individual workload view.
Excessive Manual Toil from Adjusting Kubernetes Resources
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.
Automate Kubernetes Resource Management with StormForge #
ML-Powered Optimization: Our machine learning continuously learns from your Kubernetes environment and recommends optimal configurations that improve resource efficiency.
Automated Rightsizing: Recommendations automatically adjust CPU resources and memory resources, eliminating waste while ensuring workloads maintain optimal performance.
Integration with Existing Tools: StormForge leverages community standards to integrate with your existing Kubernetes tools, including HPA, KEDA, and Karpenter.
Cost Efficiency: StormForge customers typically see a 50%-70% reduction in cloud costs, due to optimized resource utilization and configurations that meet individual workload needs.
Enhanced Performance: By ensuring the right allocation of CPU and memory, StormForge optimizes application performance and reduces CPU throttling and OOM kills.
Flexible Configuration: With support for various limit configurations, users can set defaults that can be overridden to meet their organization’s unique requirements. StormForge was built on the understanding that there are many philosophies around setting limits, and we are flexible to work with whichever policy you have.
