Cluster Computing

, Volume 19, Issue 4, pp 1787–1800 | Cite as

SOCCER: Self-Optimization of Energy-efficient Cloud Resources

  • Sukhpal Singh
  • Inderveer Chana
  • Maninder Singh
  • Rajkumar Buyya


Cloud data centers often schedule heterogeneous workloads without considering energy consumption and carbon emission aspects. Tremendous amount of energy consumption leads to high operational costs and reduces return on investment and contributes towards carbon footprints to the environment. Therefore, there is need of energy-aware cloud based system which schedules computing resources automatically by considering energy consumption as an important parameter. In this paper, energy efficient autonomic cloud system [Self-Optimization of Cloud Computing Energy-efficient Resources (SOCCER)] is proposed for energy efficient scheduling of cloud resources in data centers. The proposed work considers energy as a Quality of Service (QoS) parameter and automatically optimizes the efficiency of cloud resources by reducing energy consumption. The performance of the proposed system has been evaluated in real cloud environment and the experimental results show that the proposed system performs better in terms of energy consumption of cloud resources and utilizes these resources optimally.


Cloud computing Energy Resource scheduling Autonomic computing Self-optimization Green computing 



One of the authors, Sukhpal Singh (SRF-Professional), acknowledges the Department of Science and Technology (DST), Government of India, for awarding him the INSPIRE (Innovation in Science Pursuit for Inspired Research) Fellowship (Registration/IVR Number: 201400000761 [DST/INSPIRE/03/2014/000359]) to carry out this research work. We would like to thank all the anonymous reviewers for their valuable comments and suggestions for improving the paper.


  1. 1.
    Singh, S., Chana, I.: EARTH: Energy-aware autonomic resource scheduling in cloud computing. J. Intel. Fuzzy Syst. 30(3), 1581–1600 (2016)CrossRefGoogle Scholar
  2. 2.
    Singh, S., Chana, I.: Resource provisioning and scheduling in clouds: QoS perspective. J. Supercomput. 72(3), 926–960 (2016)CrossRefGoogle Scholar
  3. 3.
    Singh, S., Chana, I.: Q-aware: Quality of service based cloud resource provisioning. Comput. Electr. Eng. 47, 138–160 (2015)CrossRefGoogle Scholar
  4. 4.
    Singh, S., Chana, I.: QRSF: QoS-aware resource scheduling framework in cloud computing. J. Supercomput. 71(1), 241–292 (2015)CrossRefGoogle Scholar
  5. 5.
    Chen, K., Hu, C., Zhang, X., Zheng, K., Chen, Y., Vasilakos, A.V.: Survey on routing in data centers: insights and future directions. IEEE Netw. 25(4), 6–10 (2011)CrossRefGoogle Scholar
  6. 6.
    Mastelic, T., Oleksiak, A., Claussen, H., Brandic, I., Pierson, J.M., Vasilakos, A.V.: Cloud computing: survey on energy efficiency. ACM Comput. Surv. (CSUR) 47(2), 1–36 (2015)CrossRefGoogle Scholar
  7. 7.
    Choi, S., Chung, K., Yu, H.: Fault tolerance and QoS scheduling using CAN in mobile social cloud computing. Clust. Comput. 17(3), 911–926 (2014)CrossRefGoogle Scholar
  8. 8.
    Wang, B., Qi, Z., Ma, R., Guan, H., Vasilakos, A.V.: A survey on data center networking for cloud computing. Comput. Netw. 91, 528–547 (2015)CrossRefGoogle Scholar
  9. 9.
    Salehi, M. A., Krishna, P. R., Deepak, K. S., Buyya, R.: Preemption-aware energy management in virtualized data centers. In: The Proceeding of 5th IEEE International Conference on Cloud Computing (CLOUD), pp. 844–851 (2012)Google Scholar
  10. 10.
    Ren, S., He, Y., Xu, F.: Provably-efficient job scheduling for energy and fairness in geographically distributed data centers. In: The Proceeding of 32nd IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 22–31 (2012)Google Scholar
  11. 11.
    Pelley, S., Meisner, D., Zandevakili, P., Wenisch, T.F., Underwood, J.: Power routing: dynamic power provisioning in the data center. ACM Sigplan Not. 45(3), 231–242 (2010)CrossRefGoogle Scholar
  12. 12.
    Urgaonkar, R., Urgaonkar, B., Neely, M. J., Sivasubramaniam, A.: Optimal power cost management using stored energy in data centers. In: The Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, pp. 221–232 (2011)Google Scholar
  13. 13.
    Shen, S., Wang, J.: Stochastic modeling and approaches for managing energy footprints in cloud computing service. Serv. Sci. 6(1), 15–33 (2014)CrossRefGoogle Scholar
  14. 14.
    Changtian, Y., Jiong, Y.: Energy-aware genetic algorithms for task scheduling in cloud computing. In: The Proceedings of the Seventh IEEE ChinaGrid Annual Conference, pp. 43–48 (2012)Google Scholar
  15. 15.
    Ma, Y., Gong, B., Sugihara, R., Gupta, R.: Energy-efficient deadline scheduling for heterogeneous systems. J. Parallel Distrib. Comput. 72(12), 1725–1740 (2012)CrossRefzbMATHGoogle Scholar
  16. 16.
    Kim, N., Cho, J., Seo, E.: Energy-credit scheduler: an energy-aware virtual machine scheduler for cloud systems. Future Gener. Comput. Syst. 32, 128–137 (2014)CrossRefGoogle Scholar
  17. 17.
    Chen, C., He, B., Tang, X.: Green-aware workload scheduling in geographically distributed data centers. In: The Proceeding of 4th IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 82–89 (2012)Google Scholar
  18. 18.
    Wang, L., Zhang, F., Vasilakos, A.V., Hou, C., Liu, Z.: Joint virtual machine assignment and traffic engineering for green data center networks. ACM SIGMETRICS Perform. Eval. Rev. 41(3), 107–112 (2014)CrossRefGoogle Scholar
  19. 19.
    Wang, L., Zhang, F., Zheng, K., Vasilakos, A. V., Ren, S., Liu, Z: Energy-efficient flow scheduling and routing with hard deadlines in data center networks. In: The Proceeding of 34th IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 248–257 (2014)Google Scholar
  20. 20.
    Shu, Z., Wan, J., Zhang, D., Li, D.: Cloud-integrated cyber-physical systems for complex industrial applications. Mobile Netw. Appl. 1–14 (2015)Google Scholar
  21. 21.
    Wang, L., Zhang, F., Aroca, J. A., Vasilakos, A. V., Zheng, K., Hou, C, Liu, Z.: GreenDCN: a general framework for achieving energy efficiency in data center networks. IEEE J. Sel. Areas Commun, 32(1), 4–15 (2014)Google Scholar
  22. 22.
    Polverini, M., Cianfrani, A., Ren, S., Vasilakos, A.V.: Thermal-aware scheduling of batch jobs in geographically distributed data centers. IEEE Trans. Cloud Comput. 2(1), 71–84 (2014)CrossRefGoogle Scholar
  23. 23.
    Liu, Q., Wan, J., Zhou, K.: Cloud manufacturing service system for industrial-cluster-oriented application. J. Internet Technol. 15(3), 373–380 (2014)Google Scholar
  24. 24.
    Mashayekhy, L., Nejad, M.M., Grosu, D., Vasilakos, A.V.: An online mechanism for resource allocation and pricing in clouds. IEEE Trans. Comput. 65(4), 1172–1184 (2016)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Xu, D., Liu, X., Vasilakos, A.: V: traffic-aware resource provisioning for distributed clouds. IEEE Cloud Comput. 2(1), 30–39 (2015)CrossRefGoogle Scholar
  26. 26.
    Singh, S., Chana, I.: Efficient cloud workload management framework. Masters Dissertation. Thapar University, India. (2013). Retrieved From:

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentThapar UniversityPatialaIndia
  2. 2.CLOUDS Lab, Department of Computing and Information SystemsThe University of MelbourneMelbourneAustralia

Personalised recommendations