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Assessment of Load in Cloud Computing Environment Using C-means Clustering Algorithm

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Intelligent and Cloud Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 194))

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Abstract

Load balancing is an important concept which helps to raise the throughput. It helps to increase the performance of the system by assigning virtual machine for the execution of user request in less time. It assists in raising user satisfaction level and reducing user response time. As we know, the demand for cloud services raises day by day, which lead to load balancing as a major problem. The technique helps to distribute the workload among all available servers so that user can get their resource in less time. Cloud load balancing introduced the distribution of workload traffic and demands that reside over the Internet. It takes advantage of the cloud extensibility and possesses alertness to meet redirected workload demands and to improve long-term opportunity. The purpose of load balancing is to continue the system constancy and develop the performance considerably. We propose a load balancing technique by using c-means clustering to handle tasks efficiently which conserve less amount of energy.

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References

  1. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging it platforms: vision, hype and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25, 599–616 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Mills, M.P.: The cloud begins with coal: big data, big networks, big infrastructure and big power. Technical report, National Mining Association, American Coalition for Clean Coal Electricity (2013)

    Google Scholar 

  4. Hohnerlein, J., Duan, L.: Characterizing cloud datacenters in energy efficiency, performance and quality of service. In: ASEE Gulf-Southwest Annual Conference, The University of Texas, San Antonio, American Society for Engineering Education (2015)

    Google Scholar 

  5. Panda, S.K., Jana, P.K.: Efficient task scheduling algorithms for heterogeneous multi-cloud environment. J. Supercomput. 71, 1505–1533 (2015)

    Google Scholar 

  6. Li, J., Qiu, M., Ming, Z., Quan, G., Qin, X., Gu, Z.: Online optimization for scheduling preemptable tasks on iaas cloud system. J. Parallel Distrib. Comput. 72, 666–677 (2012)

    Article  Google Scholar 

  7. Sanjaya, K.P., Gupta, I., Jana, P.K.: Task scheduling algorithms for multi-cloud systems: allocation-aware approach. Inf. Syst. Front. 1–19 (2017). Springer, SCIE

    Google Scholar 

  8. Khemka, B., Friese, R., Pasricha, S., Maciejewski, A.A., Siegel, H.J., Koenig, G.A., Powers, S., Hilton, M., Rambharos, R., Poole, S.: Utility driven dynamic resource management in an over subscribed energy-constrained heterogeneous system. In: 28th IEEE International Parallel and Distributed Processing Symposium Workshops, pp. 58–67 (2014)

    Google Scholar 

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

    Google Scholar 

  10. Panda, S.K., Jana, P.K.: An efficient energy saving task consolidation algorithm for cloud computing. In: Third IEEE International Conference on Parallel, Distributed and Grid Computing, pp. 262–267 (2014)

    Google Scholar 

  11. Fan, X., Weber, W., Barroso, L.A.: Power provisioning for a warehouse-sized computer. In: The 34th Annual International Symposium on Computer Architecture, pp. 13–23. ACM (2007)

    Google Scholar 

  12. Chen, G., He, W., Liu, J., Nath, S., Rigas, L., Xiao, L., Zhao, F.: Energy-aware server provisioning and load dispatching for connection-intensive internet services. In: 5th USENIX Symposium on Networked Systems Design and Implementation, pp. 337–350 (2008)

    Google Scholar 

  13. Srikantaiah, S., Kansal, A., Zhao, F.: Energy aware consolidation for cloud computing. In: International Conference on Power Aware Computing and Systems, pp. 1–5 (2008)

    Google Scholar 

  14. Tesfatsion, S.K., Wadbro, E., Tordsson, J.: A combined frequency scaling and application elasticity approach for energy-efficient cloud computing. Sustain. Comput. Inf. Syst. 4, 205–214 (2014)

    Google Scholar 

  15. Chen, H., Zhu, X., Guo, H., Zhu, J., Qin, X., Wu, J.: Towards energy-efficient scheduling real-time tasks under uncertain cloud environment. J. Syst. Softw. 99, 20–35 (2015)

    Article  Google Scholar 

  16. Panda, S.K., Jana, P.K.: SLA-based task scheduling algorithms for heterogeneous multi-cloud environment. J. Supercomput. Springer, SCI 73(6), 2730–2762 (2017)

    Article  Google Scholar 

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Correspondence to Sushree Bibhuprada B. Priyadarshini .

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Behura, A., Priyadarshini, S.B. (2021). Assessment of Load in Cloud Computing Environment Using C-means Clustering Algorithm. In: Mishra, D., Buyya, R., Mohapatra, P., Patnaik, S. (eds) Intelligent and Cloud Computing. Smart Innovation, Systems and Technologies, vol 194. Springer, Singapore. https://doi.org/10.1007/978-981-15-5971-6_23

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