Optimizing Kubernetes Resource Allocation with Robusta KRR

Kubernetes has revolutionised the way we deploy and manage applications, offering unparalleled scalability and flexibility. However, with great power comes great responsibility — particularly when it comes to resource management. Overprovisioning leads to wasted costs, while underprovisioning can result in performance bottlenecks and downtime.

As engineers, we constantly seek ways to optimize our infrastructure for both performance and cost. This is where Robusta KRR (Kubernetes Resource Recommender) comes into play.

KRR’s GitHub

In this article, I’ll share how I leveraged KRR with a centralized metrics system to optimize resource allocation across multiple Kubernetes clusters, and how this approach can benefit anyone looking to streamline their Kubernetes operations.

Source: KubeCost

Managing resources across distributed Kubernetes clusters is complex, especially without a centralized system to collect and analyze metrics. Running Robusta KRR on lightweight clusters is effective for generating resource recommendations but becomes cumbersome when full monitoring stacks like Prometheus need to be deployed on each cluster.

Using a centralized monitoring stack such as VictoriaMetrics or Prometheus solves this by allowing lightweight agents like OpenTelemetry, Grafana Agent (Alloy), or VMAgent to forward metrics to a central system. KRR runs locally on each cluster but uses the centralized stack for metric analysis, reducing overhead and simplifying resource management. This approach provides unified visibility, streamlines optimization, and makes Kubernetes environments more scalable and cost-efficient.

Setting Up Centralized Metrics Collection

To enable centralized monitoring for Robusta KRR, you can use tools like VictoriaMetrics, Prometheus, or Thanos. These systems allow metrics from multiple Kubernetes clusters to be aggregated into a single location for streamlined analysis.

• Deploy a Centralized Monitoring Stack
  Choose a high-performance, scalable monitoring solution such a VictoriaMetrics, Prometheus, or Thanos, and set it up in your environment. Ensure the system is accessible from all your Kubernetes clusters.
• Configure Clusters to Send Metrics
  On each cluster, install Prometheus Exporters (e.g., kube-state-metrics, node-exporter, cAdvisor) to collect metrics. Use a Remote Write configuration to forward these metrics to the centralized monitoring stack.
• Label Metrics by Cluster
  Add a unique label (e.g., cluster_name) in your configuration to differentiate metrics from each cluster. This allows Robusta KRR to filter metrics by cluster during analysis, enabling precise and actionable recommendations.

Preview of cluster labels from Grafana

This setup ensures a unified view of your Kubernetes infrastructure while reducing the complexity of maintaining separate Prometheus instances on each cluster.

Running Robusta KRR Against Centralized Metrics

With a centralized metrics system like VictoriaMetrics, running Robusta KRR becomes straightforward and efficient. Here’s a summarized process:

• Install Robusta KRR
  Install KRR CLI on a local machine (e.g. Mac) or a management server using :

brew tap robusta-dev/homebrew-krr
brew install krr

• Run KRR with Cluster Filters
  Execute KRR for a specific cluster by using the --prometheus-label and -l flags to query the metrics for that cluster. This ensures the analysis is scoped to a specific cluster's data.
• Explore Additional Possibilities
  Robusta KRR offers versatile features for fine-tuning recommendations, including filtering by namespaces or workload labels, customizing historical data durations, exporting results in formats like JSON, YAML, or CSV, and tweaking CPU or memory thresholds with custom strategies.

Real-World Application: How We Use KRR at HostSpace

At HostSpace, we strive to simplify cloud hosting by offering scalable and affordable solutions. Our platform, HostSpace Kubernetes Engine (HKE), provides fully managed Kubernetes clusters tailored for efficiency and ease of use.

https://ui.hostspaceng.com

Integrating KRR into HKE

We incorporated the centralized KRR approach into HKE to offer our users unparalleled resource optimization:

• Seamless Setup: HKE automatically configures clusters to send metrics to our centralized VictoriaMetrics instance.
• Automated Recommendations: Users receive regular resource allocation recommendations generated by KRR.
• Easy Implementation: Recommendations can be applied directly through HKE’s management interface.
• Cost Savings: By optimizing resource requests and limits, our users have seen significant reductions in cloud spend.

Running KRR locally

Why This Matters

For our users, this means:

• Less Waste: Optimized resource allocation reduces idle resources and associated costs.
• Improved Performance: Right-sized resources lead to better application performance and stability.
• Simplicity: Users don’t need to manage complex monitoring setups; HKE handles it for them.

Conclusion

Efficient resource management in Kubernetes doesn’t have to be a complex, cluster-by-cluster endeavor. By centralizing your metrics with tools like VictoriaMetrics or Prometheus and leveraging Robusta KRR, you can gain actionable insights that span your entire infrastructure.

Whether you’re managing a handful of clusters or scaling rapidly, this approach offers a scalable, low-overhead solution to optimize your Kubernetes resource allocation.

Resources:

• Try Robusta KRR: GitHub Repository
• Explore VictoriaMetrics: Official Website
• Simplify Your Kubernetes Management with HostSpace: Simplify Kubernetes management with HostSpace Kubernetes Engine (HKE) — a managed solution for effortless deployment, scaling, and optimal resource utilization.

Feel free to reach out if you have questions or want to share your own experiences with Kubernetes optimization.