Towards continuous policy-driven demand response in data centers

  • Authors:

  • David Irwin;Navin Sharma;Prashant Shenoy

  • Affiliations:

  • University of Massachusetts, Amherst, Amherst, USA;University of Massachusetts, Amherst, Amherst, USA;University of Massachusetts, Amherst, Amherst, USA

  • Venue:
  • Proceedings of the 2nd ACM SIGCOMM workshop on Green networking
  • Year:
  • 2011

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Abstract

Demand response (DR) is a technique for balancing electricity supply and demand by regulating power consumption instead of generation. DR is a key technology for emerging smart electric grids that aim to increase grid efficiency, while incorporating significant amounts of clean renewable energy sources. In today's grid, DR is a rare event that only occurs when actual peak demands exceed the expected peak. In contrast, smart electric grids incentivize consumers to engage in continuous policy-driven DR to 1) optimize power consumption for time-of-use pricing and 2) deal with power variations from non-dispatchable renewable energy sources. While data centers are well-positioned to exploit DR, applications must cope with significant, frequent, and unpredictable changes in available power by regulating their energy footprint. The problem is challenging since data centers often use distributed storage systems that co-locate computation and storage, and serve as a foundation for a variety of stateful distributed applications. As a result, existing approaches that deactivate servers as power decreases do not translate well to DR, since important application-level state may become completely unavailable. In this paper, we propose a DR-compatible storage system that uses staggered node blinking patterns combined with a balanced data layout and popularity-based replication to optimize I/O throughput, data availability, and energy-efficiency as power varies. Initial simulation results show the promise of our approach, which increases I/O throughput by at least 25% compared to an activation approach when adjusting to real-world wind and price fluctuations.