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

  • Authors:

  • Nohhyun Park;Irfan Ahmad;David J. Lilja

  • Affiliations:

  • University of Minnesota;CloudPhysics, Inc.;University of Minnesota

  • Venue:
  • Proceedings of the Third ACM Symposium on Cloud Computing
  • Year:
  • 2012

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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.