Pepperdata announced managed autoscaling in the cloud with Pepperdata Capacity Optimizer version 6.3.
While autoscaling provides the elasticity customers demand for their big data workloads, it can lead to runaway costs. Capacity Optimizer intelligently augments autoscaling to ensure all nodes are fully utilized before additional nodes are created, eliminating waste and reducing costs.
Cloud providers provision infrastructure based on the peak needs of workloads. This guarantees the maximums are met, but there’s a lot of waste inherent in the current method of provisioning. Capacity Optimizer makes thousands of decisions per second, analyzing the resource usage of each node in real time to optimize the utilization of CPU, memory and I/O resources on big data clusters. The net effect is that horizontal scaling is optimized and waste is eliminated.
Pepperdata provides automated deployment options for customers that can seamlessly be added to EMR, Dataproc and Qubole deployments.
In addition to automatically tuning your cloud deployment for optimal performance, Pepperdata helps:
- Reduce troubleshooting time by 90% by leveraging targeted performance insights
- Tune application resources for peak efficiency with prescriptive recommendations
- Automatically detect and alert on bottlenecks that impact SLAs
Even with the best cloud migration strategy and dedicated attempts to curb costs, the cloud makes managing resources more difficult,” says Ash Munshi, CEO Pepperdata. “But, by leveraging machine learning and managing infrastructure in real time, IT operations teams automatically recapture wasted capacity and significantly reduce their costs.”
Pepperdata Capacity Optimizer with managed autoscaling is available in July as a supported beta release for companies looking for early access, with free updates provided. The general availability release is due in September 2020.
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