Pepperdata Capacity Optimizer for Kubernetes
Pepperdata Capacity Optimizer autonomously optimizes your cluster resources, recapturing unused capacity so you can run more applications and get the most out of your infrastructure investment running Kubernetes—whether on prem or in the cloud.
- Increase node utilization and reduce the need for additional nodes, which translates directly to reduced costs.
- Autonomously remediate any inefficiencies in an application’s response to Amazon EKS autoscaling by reclaiming resources and scaling based on actual and not requested resources.
- Relax with the peace of mind that Pepperdata requires zero application changes and no manual intervention.
The State of Kubernetes
2023 Report
Enterprises looking to maximize performance in the cloud are increasingly turning to Kubernetes—most notably for its autoscaling functionality. We surveyed 800 Kubernetes experts about the growth of their Kubernetes clusters and the resulting management challenges.
Download the report to learn:
- The top workloads being deployed on Kubernetes.
- How companies are measuring the ROI of their deployments
- The most significant challenges with managing Kubernetes
Validated by Third-Party Benchmarking
30%
Improved Performance: Decrease query duration 30%.
13%
Improved Utilization: Decreased idle CPU by 13%.
35%
Increased Throughput: Increased workload capacity by 35%.
27%
Increased Available Resource Utilization: Decreased available memory by 27%.
*TPC-DS is the Decision Support framework from the Transaction Processing Performance Council. TPC-DS is an industry-standard big data analytics benchmark. Pepperdata’s work is not an official audited benchmark as defined by TPC. TPC-DS benchmark results (Amazon EKS), 1 TB dataset, 30 nodes, 225 executors, 103 jobs.