Pepperdata Introduces Capacity Optimizer to Intelligently Augment Autoscaling
The latest solution to managed autoscaling in the cloud provides full-stack visibility for troubleshooting, debugging, and planning.
FREMONT, CA: Pepperdata, the pioneer in Analytics Stack Performance (ASP), announces managed autoscaling in the cloud with Pepperdata Capacity Optimizer version 6.3. While autoscaling offers the elasticity customers require for their big data workloads, it can lead to hefty costs. Capacity Optimizer intelligently augments autoscaling to make sure that all nodes are fully utilized before additional nodes are generated, avoiding waste, and reducing costs.
Cloud service providers give infrastructure based on the peak demands of workloads. This ensures the maximums are met, but there's a lot of waste inherent in the present method of provisioning. Capacity Optimizer makes several decisions per second, analyzing the resource usage of each node in real-time to streamline the utilization of CPU, memory, and I/O resources on big data clusters. The total effect is that horizontal scaling is harmonized, and waste is eliminated.
Pepperdata also provides automated deployment options for customers that can effortlessly be added to EMR, Dataproc, and Qubole deployments. Apart from automatically tuning the cloud deployment for optimal performance, Pepperdata helps reduce troubleshooting time by leveraging targeted performance insights, tunes application resources for peak efficiency, and automatically detects and alerts risks that impact SLAs.
Even with the robust cloud migration strategy and dedicated methods to curb costs, the cloud makes managing resources more complex. By leveraging machine learning and handling infrastructure in real-time, IT operations teams can automatically recapture wasted capacity and reduce their costs significantly. Pepperdata Capacity Optimizer with managed autoscaling is available from July as a supported beta release for firms looking for early access, with free updates availed.
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