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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ahmad, Irfan Waldspurger, Carl Uysal, Mustafa Gulati, Ajay Shanmuganathan, Ganesha |
| 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. |
| Starting Page | 1 |
| Ending Page | 14 |
| Page Count | 14 |
| File Format | |
| ISBN | 9781450309769 |
| DOI | 10.1145/2038916.2038935 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-10-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Storage Qos Device Vm Modeling Virtualization |
| Content Type | Text |
| Resource Type | Article |
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