Pepperdata Helps Fortune 100 Financial Services Giant

Pepperdata Helps Fortune 100 Financial Services Giant

Improve Price/Performance up to 3X with Autonomous Optimization

Pepperdata Capacity Optimizer automatically optimizes your cluster resources, recapturing wasted capacity so you can run more applications and get the most out of your infrastructure investment. Capacity Optimizer enables you to:

  • Get up to 3X price-performance improvement on top of AWS autoscaling.
  • Recapture wasted capacity, run more applications, and get the best ROI from your infrastructure investment.
  • Optimize each node’s ability to run an optimal number of containers and to run the same number of workloads on fewer instances.

Immediately Improve Big Data Cluster Throughput

On a typical cluster, Capacity Optimizer uses machine learning (ML) to make thousands of decisions per second, analyzing the resource usage of each node in real time. The result: CPU, memory, and I/O resources are automatically optimized to increase utilization, and waste is eliminated in both Kubernetes and traditional big data environments. Capacity Optimizer rapidly identifies where more work can be done and adds tasks to nodes with available resources. Even the most experienced operator dedicated to resource management can’t make manual configuration changes with that precision and speed.

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Boost Autoscaling Performance and Reduce Your Cloud Costs

In cloud environments, autoscaling provides the elasticity you need for your big data workloads, but it often leads to uncontrolled costs. Cloud providers provision infrastructure based on the peak needs of workloads. This guarantees that maximums are met but can create a lot of provisioning waste—the very waste that Capacity Optimizer identifies and returns to you in the form of optimized, available resources to run more jobs. 

Whatever your cloud platform, Capacity Optimizer uses autonomous optimization to intelligently augment autoscaling and ensure that all nodes are fully utilized before additional nodes are created. The net effect is that horizontal scaling is optimized and waste is eliminated.

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