A New Adaptive Energy-Aware Job Scheduling in Cloud Computing

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)

Abstract

In the last decade, with the significant growth of the calculation and data concerns over energy use and carbon dioxide emissions caused by the servers have increased. Various scheduling algorithms have been created all of which attempt to reduce the execution time of tasks and have not paid enough attention to reduce energy consumption. Other scheduling algorithms try to reduce the makespan and the energy consumption simultaneously that are known as the energy-aware scheduling algorithms. The algorithm presented in this article schedules the tasks with a focus on reducing makespan and energy consumption. The proposed method provides a new scheduling algorithm using four factors of communication between tasks, the distance between nodes, virtual machines’ status and energy consumption forecasts to reduce makespan and energy consumption. The purpose of this scheduling algorithm is to reduce the displacement between the nodes and optimize VMs execution that using the analytical hierarchy process (AHP) the best decision is made for task implementation.

Keywords

AHP Task scheduling Energy-aware scheduling Cloud computing 

References

  1. 1.
    Moganarangan, N., Babukarthik, R.G., Bhuvaneswari, S., Basha, M.S., Dhavachelvan, P.: A novel algorithm for reducing energy-consumption in cloud computing environment: web service computing approach. J. King Saud Univ. Comput. Inf. Sci. 28(1), 55–67 (2016)CrossRefGoogle Scholar
  2. 2.
    Yang, S., Wieder, P., Yahyapour, R., Fu, X.: Energy-aware provisioning in optical cloud networks. Comput. Netw. 8(118), 78–95 (2017)CrossRefGoogle Scholar
  3. 3.
    Dighe, S., Vangal, S.R., Aseron, P., Kumar, S., Jacob, T., Bowman, K.A., Howard, J., Tschanz, J., Erraguntla, V., Borkar, N., De, V.K.: Within-die variation-aware dynamic-voltage-frequency-scaling with optimal core allocation and thread hopping for the 80-core teraflops processor. IEEE J. Solid-State Circuits. 46(1), 184–93 (2011)CrossRefGoogle Scholar
  4. 4.
    Shamsollah, G., Othman, M., Bakar, M.R.A., Leong, W.J.: Multi-objective method for divisible load scheduling in multi-level tree network. Future Gener. Comput. Syst. 54, 132–143 (2016)Google Scholar
  5. 5.
    Shamsollah, G., Othman, M.: A priority based job scheduling algorithm in cloud computing. Procedia Eng. 50, 778–785 (2012)Google Scholar
  6. 6.
    Rong, H., Zhang, H., Xiao, S., Li, C., Hu, C.: Optimizing energy consumption for data centers. Renew. Sustain. Energy Rev. 31(58), 674–91 (2016)CrossRefGoogle Scholar
  7. 7.
    Singh, A., Mishra, N., Ali, S.I., Shukla, N., Shankar, R.: Cloud computing technology: reducing carbon footprint in beef supply chain. Int. J. Prod. Econ. 30(164), 462–71 (2015)CrossRefGoogle Scholar
  8. 8.
    Chen, D.R., Chiang, K.F.: Cloud-based power estimation and power-aware scheduling for embedded systems. Comput. Electr. Eng. 31(47), 204–21 (2015)CrossRefGoogle Scholar
  9. 9.
    Gerasoulis, A., Yang, T.: On the granularity and clustering of directed acyclic task graphs. IEEE Trans. Parallel Distrib. Syst. 4(6), 686–701 (1993)CrossRefGoogle Scholar
  10. 10.
    Juarez, F., Ejarque, J., Badia, R.M.: Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Future Gener. Comput. Syst. 78, 257–271 (2016)CrossRefGoogle Scholar
  11. 11.
    Aupy, G., Benoit, A., Robert, Y.: Energy-aware scheduling under reliability and makespan constraints. In: 2012 19th International Conference on High Performance Computing (HiPC), 18 Dec 2012, pp. 1–10 (2012)Google Scholar
  12. 12.
    Rizvandi, N.B., Taheri, J., Zomaya, A.Y., Lee, Y.C.: Linear combinations of dvfs-enabled processor frequencies to modify the energy-aware scheduling algorithms. In: 2010 10th IEEE/ACM International Conference on InCluster, Cloud and Grid Computing (CCGrid), 17 May 2010, pp. 388–397 (2010)Google Scholar
  13. 13.
    Chase, J.S., Anderson, D.C., Thakar, P.N., Vahdat, A.M., Doyle, R.P.: Managing energy and server resources in hosting centers. ACM SIGOPS Oper. Syst. Rev. 35(5), 103–16 (2001)CrossRefGoogle Scholar
  14. 14.
    Urgaonkar, B., Shenoy, P., Chandra, A., Goyal, P., Wood, T.: Agile dynamic provisioning of multi-tier internet applications. ACM Trans. Auton. Adapt. Syst. (TAAS). 3(1), 1 (2008)CrossRefGoogle Scholar
  15. 15.
    Saaty, T.L.: How to make a decision: the analytic hierarchy process. Eur. J. Oper. Res. 48(1), 9–26 (1990)CrossRefMATHGoogle Scholar
  16. 16.
    Saaty, T.L. Fundamentals of decision making and priority theory with the analytic hierarchy process. RWS Publications, Pittsburgh (1994)Google Scholar
  17. 17.
    Saaty, T.L.: The modern science of multi-criteria decision making and its practical applications: the AHP/ANP approach. Oper. Res. 61(5), 1101–1118 (2013)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Shamsollah, G.: Multi-criteria divisible load scheduling in binary tree network. Ph. D. Dissertation (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceIslamic Azad UniversityAshtian BranchIran
  2. 2.Iranian Non-profit Association of Distributed Computing and SytemsQomIran

Personalised recommendations