Sustainable predictive storage management: on-line grouping for energy and latency reduction

  • Authors:
  • David Essary;Ahmed Amer

  • Affiliations:
  • University of Pittsburgh;Santa Clara University

  • Venue:
  • Proceedings of the 4th Annual International Conference on Systems and Storage
  • Year:
  • 2011

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Abstract

The divergence of processor and storage system speeds is one of the most intensely investigated problems in computing. Yet the performance disparity remains, and further, storage energy consumption is rapidly becoming a new critical problem. While smarter caching and predictive techniques do much to alleviate this disparity, the problem persists, and data storage remains a growing contributor to latency and energy consumption. We present an online, block-level, context-based predictive engine utilizing opportunistic replication. We test this predictive engine on real-world workloads, gathering all necessary predictive metadata on the fly without any warm-up period, and show reductions of total disk activity by up to 65%. This reduction in logical movement equates to a reduction in physical mechanical movement of the drive by 80%, a reduction of perceived latency by up to 63%, and a reduction in measured energy consumed by mechanical acticvity of 52--71% on live hardware.