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Cluster Computing

, Volume 22, Supplement 1, pp 513–520 | Cite as

An effective task scheduling algorithm based on dynamic energy management and efficient resource utilization in green cloud computing environment

  • Yong LuEmail author
  • Na Sun
Article

Abstract

With the expanding of its scale and the energy cost factors being ignored in green cloud computing, the problem of high energy cost and low efficiency is exposed. Based on the concepts and principles of load balancing, a novel energy-efficient load balancing global optimization algorithm, called resource-aware load balancing clonal algorithm for task scheduling, is proposed to deal with the problem of energy consumption in green cloud computing. Firstly, the problem is formulated as a combinatorial optimization problem that aims to optimize both energy consumption and load balancing. Then, the resource-aware scheduling algorithm is proposed based on load balancing strategy and clonal selection principle. Finally, simulation studies show that the proposed algorithm can effectively reduce energy consumption in green cloud computing, and its exploration and exploitation abilities can be enhanced and well balanced.

Keywords

Green cloud computing Energy consumption constraint Data centers Combinatorial optimization problem Clonal selection resource scheduling algorithm 

Notes

Acknowledgements

This work is supported by the project of the First-Class University and the First-Class Discipline(10301-017004011501), and the National Natural Science Foundation of China.

References

  1. 1.
    Park, J., Baek, N., Kim, S.H.: A text-based user interface scheme for low-tier embedded systems: an object-oriented approach. Clust. Comput. 19(4), 1879–1884 (2016)Google Scholar
  2. 2.
    Xiang, X., Lin, C., Chen, X.: Energy-efficient link selection and transmission scheduling in mobile cloud computing. IEEE Wirel. Commun. Lett. 3(2), 153–156 (2014)Google Scholar
  3. 3.
    Mastelic, T., Brandic, I.: Recent trends in energy-efficient cloud computing. IEEE Cloud Comput. 2(1), 40–47 (2015)Google Scholar
  4. 4.
    Liu, F., et al.: Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications. IEEE Wirel. Commun. 20(3), 14–22 (2013)Google Scholar
  5. 5.
    Fallahpour, A., Beyranvand, H., Salehi, J.A.: Energy-efficient manycast routing and spectrum assignment in elastic optical networks for cloud computing environment. J. Lightwave Technol. 33(19), 4008–4018 (2015)Google Scholar
  6. 6.
    Hajj, H., et al.: An algorithm-centric energy-aware design methodology. IEEE Trans. Very Larg. Scale Integr. Syst. 22(11), 2431–2435 (2014)Google Scholar
  7. 7.
    Dabbagh, M., et al.: Toward energy-efficient cloud computing: prediction, consolidation, and overcommitment. IEEE Netw. 29(2), 56–61 (2015)Google Scholar
  8. 8.
    Xiaohu, G.: Energy-efficiency optimization for MIMO-OFDM mobile multimedia communication systems with QoS constrains. IEEE Trans. Veh. Technol. 63(5), 2127–2138 (2014)Google Scholar
  9. 9.
    Shu, W., Wang, W.: A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing. EURASIP J. Wirel. Commun. Netw. 64, 1–9 (2014)Google Scholar
  10. 10.
    Park, S.T., Park, E.M., Seo, J.H., Li, G.: Erratum to: Factors affecting the continuous use of cloud service: focused on security risks. Clust. Comput. 19(2), 485–495 (2016)Google Scholar
  11. 11.
    Rodriguez, M.A., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)Google Scholar
  12. 12.
    Lin, X., et al.: Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment. IEEE Trans. Serv. Comput. 8(2), 175–186 (2015)Google Scholar
  13. 13.
    Li, J., et al.: Cost-efficient coordinated scheduling for leasing cloud resources on hybrid workloads. Parallel Comput. 44(2), 1–17 (2015)MathSciNetGoogle Scholar
  14. 14.
    Tsai, J.T., Fang, J.C., Chou, J.H.: Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput. Oper. Res. 40(12), 3045–3055 (2013)zbMATHGoogle Scholar
  15. 15.
    Kessaci, Y., Melab, N., Talbi, E.G.: A multi-start local search heuristic for an energy efficient VMs assignment on top of the open Nebula cloud manager. Fut. Gener. Comput. Syst. 29(1), 1–20 (2013)Google Scholar
  16. 16.
    Lien, D., Bert, V.: Efficient resource management for virtual desktop cloud computing. J. Supercomput. 62(1), 741–767 (2012)Google Scholar
  17. 17.
    Jie, S., Yan, L., Zhenxing, Y.: An energy efficiency model and measurement method in cloud computing environment. J. Softw. 23(2), 200–213 (2012)Google Scholar
  18. 18.
    Zhu, R., Zhang, X., Liu, X., Shu, W., Mao, T., Jalaeian, B.: ERDT: energy-efficient reliable decision transmission for cooperative spectrum sensing in Industrial IoT. IEEE Access 3, 2366–2378 (2015)Google Scholar
  19. 19.
    Li, Y., Yanhong, S., LihChyun, Z.: Distributed air index for efficient spatial query processing in road sensor networks on the air. Int. J. Commun. Syst. 30(5), 1–23 (2017)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Information EngineeringMinzu University of ChinaBeijingChina

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