Abstract
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.
Original language | English (US) |
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Pages | 637-648 |
Number of pages | 12 |
DOIs | |
State | Published - 2013 |
Externally published | Yes |
Event | 27th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2013 - Boston, MA, United States Duration: May 20 2013 → May 24 2013 |
Conference
Conference | 27th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2013 |
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Country/Territory | United States |
City | Boston, MA |
Period | 5/20/13 → 5/24/13 |
All Science Journal Classification (ASJC) codes
- Software
Keywords
- Energy efficiency
- Multicore systems
- Quality of service
- Scheduling algorithm