David A. Bader
IEEE Fellow
AAAS Fellow
College of Computing
Georgia Tech
Atlanta, GA 30332



Energy-Efficient Scheduling for Best-Effort Interactive Services to Achieve High Response Quality

High response quality is critical for many best-effort interactive services, and at the same time, reducing energy consumption can directly reduce the operational cost of service providers. In this paper, we study the quality-energy tradeoff for such services by using a composite performance metric that captures their relative importance in practice: Service providers usually grant top priority to quality guarantee and explore energy saving secondly. We consider scheduling on multicore systems with core-level DVFS support and a power budget. Our solution consists of two steps. First, we employ an equal sharing principle for both job and power distribution. Specifically, we present a "Cumulative Round-Robin" policy to distribute the jobs onto the cores, and a "Water-Filling" policy to distribute the power dynamically among the cores. Second, we exploit the concave quality function of many best-effort applications, and develop Online-QE, a myopic optimal online algorithm for scheduling jobs on a single-core system. Combining the two steps together, we present a heuristic online algorithm, called DES (Dynamic Equal Sharing), for scheduling best-effort interactive services on multicore systems. The simulation results based on a web search engine application show that DES takes advantage of the core-level DVFS architecture and exploits the concave quality function of best-effort applications to achieve high service quality with low energy consumption.

Publication History

Versions of this paper appeared as:
  1. Z. Du, H. Sun, Y. He, Y. He, D.A. Bader, and H. Zhang, ``Energy-Efficient Scheduling for Best-Effort Interactive Services to Achieve High Response Quality,'' 27th IEEE International Parallel and Distributed Processing Symposium (IPDPS), Boston, MA, May 20-24, 2013.

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Last updated: May 24, 2013


Computational Biology

Parallel Computing