Ajay Gulati
Mustafa Uysal
ABSTRACT
Virtualized datacenters strive to reduce costs through workload consolidation. Workloads exhibit a diverse set of IO behaviors and varying IO load that makes it difficult to estimate the IO performance on shared storage. As a result, system administrators often resort to gross overprovisioning or static partitioning of storage to meet application demands. In this paper, we introduce Pesto, a unified storage performance management system for heterogeneous virtualized datacenters. Pesto is the first system that completely automates storage performance management for virtualized datacenters, providing IO load balancing with cost-benefit analysis, per-device congestion management, and initial placement of new workloads.
At its core, Pesto constructs and adapts approximate black-box performance models of storage devices automatically, leveraging our analysis linking device throughput and latency to outstanding IOs.Experimental results for a wide range of devices and configurations validate the accuracy of these models. We implemented Pesto in a commercial product and tested its performance on tens of devices, running hundreds of test cases over the past year. End-to-end experiments demonstrate that Pesto is efficient, adapts to changes quickly and can improve workload performance by up to 19%, achieving our objective of lowering storage management costs through automation.
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Index Terms
- Pesto: online storage performance management in virtualized datacenters
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