Parallel and Distributed Processing Symposium, International (2013)
Cambridge, MA, USA USA
May 20, 2013 to May 24, 2013
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IPDPS.2013.26
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.
Multicore processing, Schedules, Heuristic algorithms, Optimal scheduling, Energy consumption, Dynamic scheduling, Multicore systems, Energy efficiency, Scheduling algorithm, Quality of service
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," 2013 IEEE 27th International Symposium on Parallel and Distributed Processing (IPDPS 2013)(IPDPS), Boston, MA, 2013, pp. 637-648.