Romano: Autonomous storage management using performance prediction in multi-tenant datacenters

Nohhyun Park, Irfan Ahmad, David J Lilja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

Workload consolidation is a key technique in reducing costs in virtualized datacenters. When considering storage consolidation, a key problem is the unpredictable performance behavior of consolidated workloads on a given storage system. In practice, this often forces system administrators to grossly overprovision storage to meet application demands. In this paper, we show that existing modeling techniques are inaccurate and ineffective in the face of heterogenous devices. We introduce Romano, a storage performance management system designed to optimize truly heterogeneous virtualized datacenters. At its core, Romano constructs and adapts approximate workload-specific performance models of storage devices automatically, along with prediction intervals. It then applies these models to allow highly efficient IO load balancing. End-to-end experiments demonstrate that Romano reduces prediction error by 80% on average compared with existing techniques. The result is improved load balancing with lowered variance by 82% and reduced average and maximum latency observed across the storage systems by 52% and 78%, respectively.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd ACM Symposium on Cloud Computing, SoCC 2012
DOIs
StatePublished - 2012
Event3rd ACM Symposium on Cloud Computing, SoCC 2012 - San Jose, CA, United States
Duration: Oct 14 2012Oct 17 2012

Publication series

NameProceedings of the 3rd ACM Symposium on Cloud Computing, SoCC 2012

Other

Other3rd ACM Symposium on Cloud Computing, SoCC 2012
CountryUnited States
CitySan Jose, CA
Period10/14/1210/17/12

Keywords

  • Device
  • Modeling
  • QoS
  • Storage
  • VM
  • Virtualization

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