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Energy-efficient task allocation techniques for asymmetric multiprocessor embedded systems

Published:27 January 2014Publication History
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Abstract

Asymmetric multiprocessor systems are considered power-efficient multiprocessor architectures. Furthermore, efficient task allocation (partitioning) can achieve more energy efficiency at these asymmetric multiprocessor platforms. This article addresses the problem of energy-aware static partitioning of periodic real-time tasks on asymmetric multiprocessor (multicore) embedded systems. The article formulates the problem according to the Dynamic Voltage and Frequency Scaling (DVFS) model supported by the platform and shows that it is an NP-hard problem. Then, the article outlines optimal reference partitioning techniques for each case of DVFS model with suitable assumptions. Finally, the article proposes modifications to the traditional bin-packing techniques and designs novel techniques taking into account the DVFS model supported by the platform. All algorithms and techniques are simulated and compared. The simulation shows promising results, where the proposed techniques reduced the energy consumption by 75% compared to traditional methods when DVFS is not supported and by 50% when per-core DVFS is supported by the platform.

References

  1. B. Andersson and E. Tovar. 2007. Competitive analysis of partitioned scheduling on uniform multiprocessors. In Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS). 1--8.Google ScholarGoogle Scholar
  2. ARM. 2012. ARM11#8482; MPCore#8482; multicore processor. http://www.arm.com/products/processors/classic/arm11/arm11-mpcore.php. (Last accessed 11/12).Google ScholarGoogle Scholar
  3. H. Aydin and Q. Yang. 2003. Energy-aware partitioning for multiprocessor real-time systems. In Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS). 1--9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Baruah and J. Goossens. 2003. Rate-monotonic scheduling on uniform multiprocessors. IEEE Trans. Comput. 52, 7, 966--970. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. S. Baruah. 2004a. Task partitioning upon heterogeneous multiprocessor platforms. In Proceedings of the Real-Time and Embedded Technology and Applications Symposium (RTAS). 536--543. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S. Baruah. 2004b. Partitioning real-time tasks among heterogeneous multiprocessors. In Proceedings of the International Conference on Parallel Processing. 467--474. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. T. Braun, H. Siegel, N. Beck, L. Boloni, M. Maheswaran, A. Reuther, J. Robertson, M. Theys, B. Yao, D. Hensgen, and R. Freund. 2001. A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distribut. Comput. 61, 810--837. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. Calandrino, D. Baumberger, T. Li, S. S. Hahn, and J. Anderson. 2007. Soft real-time scheduling on performance asymmetric multicore platforms. In Proceedings of the Real Time and Embedded Technology and Applications Symposium (RTAS). 101--112. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. H. Chen and A. Cheng. 2005. Applying ant colony optimization to the partitioned scheduling problem for heterogeneous multiprocessors. ACM SIGBED Rev. 2, 2, 11--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. Chen and C. Kuo. 2007. Energy-efficient scheduling for real-time systems on dynamic voltage scaling (DVS) platforms. In Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). 28--38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. S. Funk, J. Goossens, and S. Baruah. 2001. On-line scheduling on uniform multiprocessors. In Proceedings of the Real-Time Systems Symposium (RTSS). 183--192. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. S. Funk and S. Baruah. 2005. Task assignment on uniform heterogeneous multiprocessors. In Proceedings of the Euromicro Conference on Real-Time Systems (ECRTS). 219--226. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. M. Haouari and M. Serairi. 2009. Heuristics for the variable sized bin-packing problem. J. Comput. Oper. Res. 36, 2877--2884. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. F. Kong, W. Yi, and Q. Deng. 2011. Energy-efficient scheduling of real-time tasks on cluster-based multicores. In Proceedings of the Design, Automation & Test in Europe Conference & Exhibition (DATE). 1--6.Google ScholarGoogle Scholar
  15. D. Koufaty, D. Reddy, and S. Hahn. 2010. Bias scheduling in heterogeneous multicore architectures. In Proceedings of the 5th ACM European Conference on Computer Systems (EuroSys). 125--138. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. R. Kumar, K. Farkas, N. Jouppi, P. Ranganathan, and D. Tullsen. 2003. Single-ISA heterogeneous multi-core architectures: The potential for processor power reduction. In Proceedings of the 36th Annual IEEE/ACM International Symposium on Microarchitecture. 81--92. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. N. Lakshminarayana, S. Rao, and H. Kim. 2008. Asymmetry aware scheduling algorithms for asymmetric multiprocessors. In Proceedings of the Workshop on the Interaction between Operating Systems and Computer Architecture (WIOSCA). 1--7.Google ScholarGoogle Scholar
  18. N. Lakshminarayana and H. Kim 2008. Understanding performance, power and energy behavior in asymmetric multiprocessors. In Proceedings of the International Conference on Computer Design (ICCD). 471--477.Google ScholarGoogle ScholarCross RefCross Ref
  19. T. Li, P. Brett, B. Hohlt, R. Knauerhase, S. Mcelderry, and S. Hahn. 2008. Operating system support for shared-ISA asymmetric multi-core architectures. In Proceedings of the Workshop on the Interaction between Operating Systems and Computer Architecture (WIOSCA). 19--26.Google ScholarGoogle Scholar
  20. T. Li, D. Baumberger, D. Koufaty, and S. Hahn. 2007. Efficient operating system scheduling for performance-asymmetric multi-core architectures. In Proceedings of the IEEE/ACM Conference on Supercomputing (SC'07). 1--11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. A. Omidi and A. Rahmani. 2009. Multiprocessor independent tasks scheduling using a novel heuristic PSO algorithm. In Proceedings of the 2nd IEEE International Conference on Computer Science and Information Technology (ICCSIT). 369--373.Google ScholarGoogle Scholar
  22. E. Saad, M. Awadalla, M. Shalan, and A. Elewi. 2012. Energy-aware task partitioning on heterogeneous multiprocessor platforms. Int. J. Comput. Sci. Issues 9, 2, 1, 176--183.Google ScholarGoogle Scholar
  23. Texas Instruments. 2013. OMAP#8482; Application Processors. http://www.ti.com/lsds/ti/omap-applications-processors/features.page. (Last accessed 4/13).Google ScholarGoogle Scholar
  24. V. Venkatachalam and M. Franz. 2005. Power reduction techniques for microprocessor systems. ACM Comput. Surv. 37, 3, 195--237. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. P. Visalakshi and S. Sivanandam. 2009. Dynamic task scheduling with load balancing using hybrid particle swarm optimization. Int. J. Open Problems Compt. Math 2, 3, 475--488.Google ScholarGoogle Scholar
  26. O. Zapata and P. Alvarez. 2005. EDF and RM multiprocessor scheduling algorithms: Survey and performance evaluation. Tech. rep., CINVESTAV-IPN, Secci'on de Computaci'on, Mexico, 1--24.Google ScholarGoogle Scholar
  27. S. Zhuravlev, J. Saez, S. Blagodurov, A. Fedorova, and M. Prieto. 2012. Survey of energy-cognizant scheduling techniques. IEEE Trans. Parallel Distribut. Syst. 24, 7, 1447--1464. Google ScholarGoogle ScholarDigital LibraryDigital Library

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