Application Resource Optimizer (ARO) automates workload optimization and enhances resource allocation efficiency. ARO uses machine-learning algorithms and automation to analyze and optimize Kubernetes® workloads. By adjusting resource allocation, fine-tuning configurations, and considering reliability risks, ARO maximizes performance, improves resource utilization, reduces costs, and mitigates potential service disruptions. With its automated optimization workflow, ARO simplifies workload management, allowing IT teams to achieve optimal performance and efficiency without requiring extensive manual intervention.

ARO provides the following:

  • Cost Savings: ARO identifies opportunities to optimize resource allocation, leading to cost savings. Organizations can reduce their infrastructure costs by eliminating resource waste and ensuring optimal resource usage.
  • Improved Efficiency and Reliability: ARO improves efficiency and reliability by identifying optimal resource configurations based on user-defined performance objectives. It helps ensure that workloads are utilizing resources effectively, reducing inefficiencies and reliability risks.
  • Automated Workload Optimization: ARO automates the process of workload optimization. It can onboard and determine the optimal resource settings for the workload automatically, eliminating the need for manual trial-and-error tuning.

Optimization Tools

ARO provides you with several options to optimize Kubernetes workloads:

ARO ToolDescriptionBenefitsPrime Use Cases
ProfilerThe Profiler analyzes workloads, workload configuration settings, and operations metrics to identify performance and reliability risks and inefficient configurations.
  • Fast
  • Free
  • No additional agent or ingestion costs
  • Reliability risk score
  • Efficiency rate
  • Opportunities to optimize
  • Cautions

  • Identify common misconfigurations

You need a quick cost and performance analysis of your Kubernetes workload.

Instant Recommendation

Instant Recommendations are suggestions for fine-tuning the resource utilization of Kubernetes workloads based on past deployment data and considering historical usage.

  • Fast
  • Easy
  • Cost impact
  • Right sizing
  • Monthly savings
  • Easy to apply YAML resource configuration
  • You want to optimize non-production environments.
  • You need to optimize workloads with poor performance or high cost quickly.
Active Optimization

Active Optimization enables you to refine resource allocation based on the results of a battery of optimization tests and machine-language analysis.

  • Comprehensive
  • Precise
  • Based on tests and machine language analysis
  • Right sizing resources for specified target performance goals
  • Provides recommendations for up to three different objectives
  • Verified performance for the recommended configurations
  • Easy to apply YAML resource configuration
  • Insights such as performance and cost tradeoffs
  • You want to optimize your production workloads.
  • The areas of optimization are only apparent with thorough testing and analysis.
  • Your workloads are in flux and need frequent changes to resource configuration.
  • You want to verify the performance of the recommended configurations before applying them to business-critical workloads.

Supported Environments

AWS is the only supported environment for ARO at this time.


Kubernetes® is a trademark of The Linux Foundation®.