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An Energy-Efficient Task Scheduling Heuristic Algorithm Without Virtual Machine Migration in Real-Time Cloud Environments

  • Yi ZhangEmail author
  • Liuhua Chen
  • Haiying Shen
  • Xiaohui Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9955)

Abstract

Reducing energy consumption has become an important task in cloud datacenters. Many existing scheduling approaches in cloud datacenters try to consolidate virtual machines (VMs) to the minimum number of physical machines (PMs) and hence minimize the energy consumption. VM live migration technique is used to dynamically consolidate VMs to as few PMs as possible; however, it introduces high migration overhead. Furthermore, the cost factor is usually not taken into account by existing approaches, which will lead to high payment cost for cloud users. In this paper, we aim to achieve energy reduction for cloud providers and payment saving for cloud users, and at the same time, without introducing VM migration overhead and without compromising deadline guarantees for user tasks. Motivated by the fact that some of the tasks have relatively loose deadlines, we can further reduce energy consumption by proactively postponing the tasks without waking up new PMs. In this paper, we propose a heuristic task scheduling algorithm called Energy and Deadline Aware with Non-Migration Scheduling (EDA-NMS) algorithm. EDA-NMS exploits the looseness of task deadlines and tries to postpone the execution of the tasks that have loose deadlines in order to avoid waking up new PMs. When determining the VM instant types, EDA-NMS selects the instant types that are just sufficient to guarantee task deadline to reduce user payment cost. The results of extensive experiments show that our algorithm performs better than other existing algorithms on achieving energy efficiency without introducing VM migration overhead and without compromising deadline guarantees.

Keywords

Virtualized cloud Real-time task Scheduling Criticality Energy-aware 

Notes

Acknowledgments

The work on this paper has been supported by Scientific and Technological Research Program for Guangxi Educational Commission grants \(\sharp \)2013YB113, Guangxi Universities key Laboratory Fund of Embedded Technology and Intelligent Information Processing.

References

  1. 1.
    Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency Comput. Pract. Experience 24(13), 1397–1420 (2012)CrossRefGoogle Scholar
  2. 2.
    Berral, J.L., Gavalda, R., Torres, J.: Adaptive scheduling on power-aware managed data-centers using machine learning. In: Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing. pp. 66–73. IEEE Computer Society (2011)Google Scholar
  3. 3.
    Van den Bossche, R., Vanmechelen, K., Broeckhove, J.: Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In: Proceedings of CLOUD, pp. 228–235. IEEE (2010)Google Scholar
  4. 4.
    Burns, A., Davis, R.: Mixed criticality systems-a review. Department of Computer Science, University of York, Technical report (2013)Google Scholar
  5. 5.
    Calheiros, R.N., Buyya, R.: Energy-efficient scheduling of urgent bag-of-tasks applications in clouds through DVFS. In: Proceedings of CloudCom, pp. 342–349. IEEE (2014)Google Scholar
  6. 6.
    Chen, H., Zhu, X., Guo, H., Zhu, J., Qin, X., Wu, J.: Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment. J. Syst. Softw. 99, 20–35 (2015)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Gao, Y., Wang, Y., Gupta, S.K., Pedram, M.: An energy and deadline aware resource provisioning, scheduling and optimization framework for cloud systems. In: Proceedings of the Ninth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, p. 31. IEEE Press (2013)Google Scholar
  9. 9.
  10. 10.
    He, C., Zhu, X., Guo, H., Qiu, D., Jiang, J.: Rolling-horizon scheduling for energy constrained distributed real-time embedded systems. J. Syst. Softw. 85(4), 780–794 (2012)CrossRefGoogle Scholar
  11. 11.
    Hosseinimotlagh, S., Khunjush, F.: Migration-less energy-aware task scheduling policies in cloud environments. In: Proceedings of WAINA, pp. 391–397. IEEE (2014)Google Scholar
  12. 12.
    Hosseinimotlagh, S., Khunjush, F., Samadzadeh, R.: Seats: smart energy-aware task scheduling in real-time cloud computing. J. Supercomput. 71(1), 45–66 (2015)CrossRefGoogle Scholar
  13. 13.
    Mall, R.: Real-Time Systems: Theory and Practice. Pearson Education, India (2009)Google Scholar
  14. 14.
    Mao, M., Li, J., Humphrey, M.: Cloud auto-scaling with deadline and budget constraints. In: Proceedings of GRID, pp. 41–48. IEEE (2010)Google Scholar
  15. 15.
    Pop, F., Dobre, C., Cristea, V., Bessis, N., Xhafa, F., Barolli, L.: Deadline scheduling for aperiodic tasks in inter-cloud environments: a new approach to resource management. J. Supercomput. 71(5), 1754–1765 (2015)CrossRefGoogle Scholar
  16. 16.
    Qiu, M., Sha, E.H.M.: Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems. ACM Trans. Des. Autom. Electron. Syst. (TODAES) 14(2), 25 (2009)Google Scholar
  17. 17.
    Sengupta, A., Pal, T.K.: Fuzzy preference ordering of intervals. In: Sengupta, A., Pal, T.K. (eds.) Fuzzy Preference Ordering of Interval Numbers in Decision Problems. STUDFUZZ, vol. 238, pp. 59–89. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  18. 18.
    Tang, Z., Qi, L., Cheng, Z., Li, K., Khan, S.U., Li, K.: An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. J. Grid Comput. 1–20 (2015)Google Scholar
  19. 19.
    Veni, T., Bhanu, S.: A survey on dynamic energy management at virtualization level in cloud data centers. Comput. Sci. Inf. Technol. 3, 107–117 (2013)Google Scholar
  20. 20.
    Wang, W.J., Chang, Y.S., Lo, W.T., Lee, Y.K.: Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments. J. Supercomput. 66(2), 783–811 (2013)CrossRefGoogle Scholar
  21. 21.
  22. 22.
    Zhu, X., Yang, L.T., Chen, H., Wang, J., Yin, S., Liu, X.: Real-time tasks oriented energy-aware scheduling in virtualized clouds. TOCC 2(2), 168–180 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yi Zhang
    • 1
    Email author
  • Liuhua Chen
    • 2
  • Haiying Shen
    • 2
  • Xiaohui Cheng
    • 1
  1. 1.School of Information and EngineeringGuilin University of TechnologyGuangxiChina
  2. 2.School of Electrical and Computer EngineeringClemson UniversityClemsonUSA

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