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Energy Saving Task Consolidation Technique in Cloud Centers with Resource Utilization Threshold

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Progress in Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 563))

Abstract

The data centers are the world’s biggest consumers of electricity. The consumption of energy in the cloud is proportional to the CPU utilization of the virtual machines (VMs). As the size of the cloud infrastructure increases the complexity of the resource allocation problem increases and becomes very difficult to solve it efficiently. This is an NP-Hard problem. There are several heuristics that may be used to solve the problem. Through task consolidation, we can get many benefits such as maximizing cloud computing resource, utilization of resources in a better way, efficient use of power, customization of IT services, Quality of Service, and other reliable services, etc. We find from the literature review that there is a high level of coupling between energy consumption and resource utilization. This paper presents the resource allocation problem in cloud computing with the objective to minimize energy consumed in computation. The simulation results show that a 70% principle of CPU utilization is the most energy efficient threshold for task consolidation in a virtual cluster. It has been verified with MaxUtil and ECTC (Energy Conscious Task Consolidation) algorithms.

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References

  1. Wen, G., Hong, J., Xu, C., Balaji, P., Feng, S., Jiang, P.: Energy-aware hierarchical scheduling of applications in large scale data centers. In: International Conference on Cloud and Service Computing (2009)

    Google Scholar 

  2. Kim, K.H., Buyya, R., Kim, J.: Power aware scheduling of bag-of-tasks applications with deadline constraints on DVS-enabled clusters. In: Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid’07), pp. 541–548 (2007)

    Google Scholar 

  3. Lee, Y.C., Zomaya, A.Y.: Energy efficient utilization of resources in cloud computing systems. J. Supercomput. 60, 268–280 (2012)

    Article  Google Scholar 

  4. Hsu, C.H., Chen, S.C., Lee, C.C., Chang, H.Y., Lai, K.C., Li, K.C., Rong, C.: Energy-aware task consolidation technique for cloud computing. In: 2011 IEEE Third International Conference on In Cloud Computing Technology and Science, pp. 115–121 (2011)

    Google Scholar 

  5. Srikantaiah, S., Kansal, A., Zhao F.: Energy aware consolidation for cloud computing. In: International Conference on PowerAware Computing and Systems (2008)

    Google Scholar 

  6. Ali, S., Siegel, H.J., Maheswaran, M., Hensgen, D.: Task execution time modeling for heterogeneous computing systems. In: Heterogeneous Computing Workshop, 2000 (HCW 2000) Proceedings, pp. 185–199 (2000)

    Google Scholar 

  7. Hsu, C., Chen, S., Lee, C., Chang, H., Lai, K., Li, K., Rong, C.: Energy-aware task consolidation technique for cloud computing. In: Third IEEE International Conference on Cloud Computing Technology and Science (2011)

    Google Scholar 

  8. Fan, X., Weber, X.-D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. In: Proceedings of the 34th Annual International Symposium on Computer Architecture (ISCA’07), pp. 13–23 (2007)

    Google Scholar 

  9. Lee, Y.C., Zomaya, A.Y.: Energy efficient utilization of resources in cloud computing systems. J. Supercomput. (Springer) 60, 268–280 (2012)

    Article  Google Scholar 

  10. Meisner, D., Gold, B.T., Wenisch, T.F.: PowerNap: eliminating server idle power. In: Proceedings of the 14th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’09), pp. 205–216 (2009)

    Google Scholar 

  11. Hsu, C.H., Slagter, K.D., Chen, S.C., Chung, Y.C.: Optimizing energy consumption with task consolidation in clouds. Inf. Sci. 258, 452–462 (2014)

    Article  Google Scholar 

  12. Lee, Y.C., Zomaya, A.Y.: Minimizing energy consumption for precedence-constrained applications using dynamic voltage scaling. In: Proceedings of the International Symposium on Cluster Computing and the Grid (CCGRID ’09), pp. 92–99 (2009)

    Google Scholar 

  13. Tian, W., Xiong, Q., Cao, J.: An online parallel scheduling method with application to energy-efficiency in cloud computing. J. Supercomput. (Springer) 66, 1773–1790 (2013)

    Article  Google Scholar 

  14. Kim, N., Cho, J., Seo, E.: Energy-credit scheduler: an energy-aware virtual machine scheduler for cloud systems. Future Gener. Comput. Syst. (Elsevier) 32, 126–137 (2014)

    Google Scholar 

  15. Beloglazov, A.: Energy-efficient management of virtual machines in data centers for cloud computing. Ph.D. thesis, Department of Computing and Information Systems, The University of Melbourne (2013)

    Google Scholar 

  16. Koomey, J.: Growth in data center electricity use 2005–2010. A report by Analytical Press, completed at the request of The New York Times 9 (2011)

    Google Scholar 

  17. Bojanova, I., Samba, A.: Analysis of cloud computing delivery architecture models. In: Proceedings of International Conference on Advanced Information Networking and Applications, pp. 45–458 (2011)

    Google Scholar 

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Correspondence to Mahendra Kumar Gourisaria .

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Gourisaria, M.K., Patra, S.S., Khilar, P.M. (2018). Energy Saving Task Consolidation Technique in Cloud Centers with Resource Utilization Threshold. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 563. Springer, Singapore. https://doi.org/10.1007/978-981-10-6872-0_63

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  • DOI: https://doi.org/10.1007/978-981-10-6872-0_63

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