Energy-efficient scheduling for best-effort interactive services to achieve high response quality

Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium, IPDPS 2013

Volumn , Issue , 2013, Pages 637-648

  Du, Zhihui ^a   Sun, Hongyang ^b   He, Yuxiong ^c   He, Yu ^a   Bader, David A ^d   Zhang, Huazhe ^e  

^a TSINGHUA UNIVERSITY   (China)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c MICROSOFT RESEARCH   (United States)

^d Georgia Institute of Technology   (United States)

^e BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Energy efficiency; Multicore systems; Quality of service; Scheduling algorithm

Indexed keywords

BEST-EFFORT APPLICATIONS; ENERGY-EFFICIENT SCHEDULING; INTERACTIVE SERVICES; LOW ENERGY CONSUMPTION; MULTI-CORE SYSTEMS; PERFORMANCE METRICES; POWER DISTRIBUTIONS; REDUCING ENERGY CONSUMPTION;

DISTRIBUTED COMPUTER SYSTEMS; DISTRIBUTED PARAMETER NETWORKS; ENERGY EFFICIENCY; ENERGY UTILIZATION; QUALITY OF SERVICE; SCHEDULING; SEARCH ENGINES;

SCHEDULING ALGORITHMS;

EID: 84884827373     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2013.26     Document Type: Conference Paper

References (27)

1. S. Albers, F. Müller, and S. Schmelzer. Speed scaling on parallel processors. In ACM Symposium on Parallelism in Algorithms and Architectures(SPAA), pages 289-298, San Diego, USA, 2007.
2. W. Baek and T. M. Chilimbi. Green: a framework for supporting energy-conscious programming using controlled approximation. In ACM SIGPLAN conference on Programming Language Design and Implementation (PLDI), pages 198-209, 2010.
3. N. Bansal, H. L. Chan, T. W. Lam, and L. K. Lee. Scheduling for speed bounded processors. In International Colloquium on Automata, Languages and Programming (ICALP), pages 409-420, 2008.
4. N. Bansal, T. Kimbrel, and K. Pruhs. Speed scaling to manage energy and temperature. Journal of the ACM, 54(1):3:1-3:39, 2007.
5. S. Baruah, G. Koren, D. Mao, B. Mishra, A. Raghunathan, L. Rosier, D. Shasha, and F. Wang. On the competitiveness of on-line real-time task scheduling. Real-Time Systems, 4(2):125-144, 1992. (Pubitemid 23566344)
6. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
7. H.-L. Chan, W.-T. Chan, T.-W. Lam, L.-K. Lee, K.-S. Mak, and P. W. H. Wong. Energy efficient online deadline scheduling. In ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 795-804, 2007.
8. E.-C. Chang and C. Yap. Competitive on-line scheduling with level of service. Journal of Scheduling, 6(3):251-267, 2003.
9. F. Y. L. Chin and S. P. Y. Fung. Online scheduling with partial job values: Does timesharing or randomization help. Algorithmica, 37:149-164, 2003.
10. F. Y. L. Chin and S. P. Y. Fung. Improved competitive algorithms for online scheduling with partial job values. Theoretical Computer Science, 325(3):467-478, 2004.
11. M. Chrobak, L. Epstein, J. Noga, J. Sgall, R. van Stee, T. Tichý, and N. Vakhania. Preemptive scheduling in overloaded systems. Journal of Computer and System Sciences, 67(1):183-197, 2003.
12. M. Ehrgott. A characterization of lexicographic max-ordering solutions. In Proceedings of the 6th Workshop of the DGOR Working-Group Multicriteria Optimization and Decision Theory, pages 193-202, 1997.
13. R. Ge, X. Feng, S. Song, H.-C. Chang, D. Li, and K.W. Cameron. Powerpack: Energy profiling and analysis of high-performance systems and applications. IEEE Transactions on Parallel and Distributed Systems, 21(5):658-671, 2010.
14. Y. He, S. Elnikety, J. Larus, and C. Yan. Zeta: scheduling interactive services with partial execution. In Proceedings of the Third ACM Symposium on Cloud Computing, 2012.
15. Y. He, S. Elnikety, and H. Sun. Tians scheduling: Using partial processing in best-effort applications. In International Conference on Distributed Computing Systems(ICDCS), pages 434-445, 2011.
16. C. Isci, A. Buyuktosunoglu, C.-Y. Cher, P. Bose, and M. Martonosi. An analysis of efficient multi-core global power management policies: Maximizing performance for a given power budget. In IEEE/ACM International Symposium on Microarchitecture, pages 347-358, 2006.
17. C.-Y. Koo, T.-W. Lam, T.-W. Ngan, K. Sadakane, and K.-K. To. On-line scheduling with tight deadlines. Mathematical Foundations of Computer Science (MFCS), 295(1-3):251-261, 2003.
18. G. Koren and D. Shasha. Dover: An optimal on-line scheduling algorithm for overloaded uniprocessor real-time systems. SIAM Journal on Computing, 24(2):318-339, 1995.
19. E. L. Lawler. A dynamic programming algorithm for preemptive scheduling of a single machine to minimize the number of late jobs. Annals of Operations Research, 26(1-4):125-133, 1991.
20. J. Li and J. F. Martínez. Power-performance considerations of parallel computing on chip multiprocessors. ACM Transactions on Architecture and Code Optimization, 2(4):397-422, 2005.
21. M. Li, A. Yao, and F. Yao. Discrete and continuous min-energy schedules for variable voltage processors. National Academy of Sciences, 103:3983-3987, 2006.
22. A. Naveh, E. Rotem, A. Mendelson, S. Gochman, R. Chabukswar, K. Krishnan, and A. Kumar. Power and thermal management in the intel core duo processor. Intel Technology Journal, 10(2):109-122, 2006.
23. Z. Shi, C. Beard, and K. Mitchell. Competition, cooperation, and optimization in multi-hop CSMA networks. In ACM symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks (PE-WASUN), 2011.
24. J. Stankovic, M. Spuri, K. Ramamritham, and G. C. Buttazzo. Deadline Scheduling for Real-Time systems - EDF and related algorithms. Academic Publishers, 1998.
25. F. Yao, A. Demers, and S. Shenker. A scheduling model for reduced CPU energy. In IEEE Symposium on Foundations of Computer Science (FOCS), pages 374-382, 1995.
26. X. Zhang, K. Shen, S. Dwarkadas, and R. Zhong. An evaluation of per-chip nonuniform frequency scaling on multicores. In USENIX Annual Technical Conference (USENIX ATC), pages 19-19, 2010.
27. X. Zhao and N. Jamali. Fine-grained per-core frequency scheduling for power efficient-multicore execution. In International Green Computing Conference (IGCC), pages 1-8, 2011.