KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Cost-Aware Scheduling of Computation-Intensive Tasks on Multi-Core Server

  • Ding, Youwei (College of Information Technology, Nanjing University of Chinese Medicine) ;
  • Liu, Liang (College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics) ;
  • Hu, Kongfa (College of Information Technology, Nanjing University of Chinese Medicine) ;
  • Dai, Caiyan (College of Information Technology, Nanjing University of Chinese Medicine)
  • Received : 2017.10.14
  • Accepted : 2018.05.08
  • Published : 2018.11.30
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Abstract

Energy-efficient task scheduling on multi-core server is a fundamental issue in green cloud computing. Multi-core processors are widely used in mobile devices, personal computers, and servers. Existing energy efficient task scheduling methods chiefly focus on reducing the energy consumption of the processor itself, and assume that the cores of the processor are controlled independently. However, the cores of some processors in the market are divided into several voltage islands, in each of which the cores must operate on the same status, and the cost of the server includes not only energy cost of the processor but also the energy of other components of the server and the cost of user waiting time. In this paper, we propose a cost-aware scheduling algorithm ICAS for computation intensive tasks on multi-core server. Tasks are first allocated to cores, and optimal frequency of each core is computed, and the frequency of each voltage island is finally determined. The experiments' results show the cost of ICAS is much lower than the existing method.

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References

  1. L. A. Barroso, "The Price of Performance," Queue, vol.3, no.7, pp. 48-53, 2005. https://doi.org/10.1145/1095408.1095420
  2. M. Dayarathna, Y. Wen, R. Fan, "Data Center Energy Consumption Modeling: A Survey," IEEE Communications Survey & Tutorials, vol.18, no.1, pp.732-794, 2016. https://doi.org/10.1109/COMST.2015.2481183
  3. W. Forrest. "How to cut data centre carbon emissions?,"
  4. L. Barroso, U. Holzle, "The Case for Energy Proportional Computing," IEEE Computer, vol.40, no.12, pp.33-37, 2007.
  5. U.S. Energy Information Administration, "Net generation by energy source: Total (all sectors),"
  6. M. Webb, "SMART 2020: enabling the lowcarbon economy in the information age, a report by The Climate Group on behalf of the Global eSustainability Initiative (GeSI)," Global eSustainability Initiative (GeSI) Technical report, 2008.
  7. C.C. Lin, Y.C. Syu, C.J. Chang, J.J. Wu, P. Liu, et al, "Energy-efficient Task Scheduling for Multi-core Platforms with per-core DVFS," Journal of Parallel and Distributed Computing, vol. 86, pp. 71-81, 2015. https://doi.org/10.1016/j.jpdc.2015.08.004
  8. S. Liu, Q. Qiu, Q. Wu, "Energy Aware Dynamic Voltage and Frequency Selection for Real-Time Systems with Energy Harvesting," in Proc. of the conference on Design, automation and test in Europe, pp.236-241. March 10-14, 2008.
  9. S. Herbert, D. Marculescu, "Variation-Aware Dynamic Voltage/Frequency Scaling," in Proc. of IEEE International Symposium on High Performance Computer Architecture, pp.301-312, February 14-18, 2009.
  10. C.A. Barros, L.F.Q. Silveira, C.A. Valderrama, S. Xavier-de-Souza, "Optimal processor dynamic-energy reduction for parallel workloads on heterogeneous multi-core architectures," Microprocessors and Microsystems, vol. 39, pp. 418-425, 2015. https://doi.org/10.1016/j.micpro.2015.05.009
  11. M. Moeng, R. Melhem, "Applying Statistical Machine Learning to Multicore Voltage & Frequency Scaling," in Proc. of the 7th ACM international conference on Computing frontiers, pp.277-286, May 17-19, 2010.
  12. H. Aydin, Q. Yang, "Energy-aware Partitioning for Multiprocessor Real-time Systems," in Proc. of the 17th IEEE International Parallel and Distributed Processing Symposium, pp.113.2, April 22-26, 2003.
  13. R. E. Korf, E. L. Schreiber, M. D. Moffitt, "Optimal Sequential Multi-Way Number Partitioning," in Proc. Of International Symposium on Artificial Intelligence and Mathematics, pp.1-7, January 6-8, 2014.
  14. F. Kong, W. Yi, Q. Deng, "Energy-Efficient Scheduling of Real-Time Tasks on Cluster-Based Multicores," in Proc. of Design, Automation & Test in Europe Conference & Exhibition, pp.1135-1140, March 14-18, 2011.
  15. S. Pagani, J.J. Chen, M. Li, "Energy Efficiency on Multi-Core Architectures with Multiple Voltage Islands," IEEE Transactions On Parallel And Distributed Systems, vol.26, no.6, pp.1608-1621, 2015. https://doi.org/10.1109/TPDS.2014.2323260
  16. S. Pagani, J.J. Chen, "Energy Efficiency Analysis for the Single Frequency Approximation (SFA) Scheme," ACM Transactions on Embedded Computing Systems, vol.13, no.5s, pp.1-25, 2014.
  17. J. Liu, J. Guo, "Energy efficient scheduling of real-time tasks on multi-core processors with voltage islands," Future Generation Computer Systems, vol.56, pp.202-210, 2016. https://doi.org/10.1016/j.future.2015.06.003
  18. J. Mair, K. Leung, Z. Huang, "Metrics and Task Scheduling Policies for Energy Saving in Multicore Computers," in Proc. of 11th IEEE/ACM International Conference on Grid Computing, pp.266-273, October 25-28, 2010.
  19. K. Shen, A. Shriraman, S. Dwarkadas, X. Zhang, "Power and energy containers for multicore servers," in Proc. of the 12th ACM SIGMETRICS/ PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems, pp.403-404, June 11-15, 2012.
  20. C. G. Tseng, S. Figueira, "An analysis of the energy efficiency of multi-threading on multi-core machines," in Proc. of the International Conference on Green Computing, pp.283-290, August 15-18, 2010.
  21. Xinning Hui, Zhihui Du, J. Liu, Hongyang Sun, Yuxiong He and D. A. Bader, "When Good Enough Is Better: Energy-Aware Scheduling for Multicore Servers," in Proc. of IEEE International Parallel and Distributed Processing Symposium Workshops, pp. 984-993, May 29- June 2, 2017.
  22. K. Li, "Improving Multicore Server Performance and Reducing Energy Consumption by Workload Dependent Dynamic Power Management," IEEE Transactions on Cloud Computing, vol.4, no.2, pp.122-137, 2016. https://doi.org/10.1109/TCC.2015.2440238