International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 7, No. 3, 8 Mar. 2015
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An Efficient Virtual Machine Scheduling Technique in Cloud Computing Environment
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Author(s)
Index Terms
Cloud computing, load balancing, VM scheduling, response time, resource leak
Abstract
Cloud is a collection of heterogeneous resources and requirements of these resources can change dynamically. Cloud providers are always interested in maximizing the resources utilization and the associated revenues, by trimming down energy consumption and operational expenses, while on the other hand cloud users are interested in minimizing response time and optimizing overall application throughput. In cloud environment to allocate the resources with minimum overhead time along with efficient utilization of available resources is very challenging task. The resources in cloud datacenter are allocated using a virtual machine (VM) scheduling technique. So there is a need of an efficient VM scheduling technique to maximize system performance and cost saving. In this paper two dynamic virtual machine scheduling techniques i.e. Best fit and Worst fit are proposed for reducing the response time along with efficient and balanced resource utilization. The proposed algorithms removes the limitations of the previously proposed Novel Vector based algorithm and minimizes the response time complexity in order of O(log n) and O(1) using Best Fit and Worst Fit strategies respectively.
Cite This Paper
Vijaypal S. Rathor, R. K. Pateriya, Rajeev K. Gupta, "An Efficient Virtual Machine Scheduling Technique in Cloud Computing Environment", International Journal of Modern Education and Computer Science (IJMECS), vol.7, no.3, pp.39-46, 2015. DOI:10.5815/ijmecs.2015.03.06
Reference
[1]R. Buyya, J. Broberg, A. Goscinski, “Cloud Computing: Principle and Paradigms”, 1st ed., Hoboken: John Wiley & Sons, 2011.
[2]Michael Miller, “Cloud Computing: Web-Based Applications That Change the Way You Work and Collaborate Online”, 1st ed., USA: Que Publishing, 2008.
[3]Weiss. “Computing in the Clouds”, netWorker, 11(4): 16-25, ACM Press, New York, USA, Dec. 2007.
[4]T. Mather, S. Kumaraswamy, and S. Latif, “Cloud Security and Privacy”, 1st ed., USA: O’Reilly Media, 2009, pp. 11-25.
[5]K. Yang, J. Gu,T. Zhao and G. Sun , “An Optimized Control Strategy for Load Balancing based on Live Migration of Virtual Machine ”, in Proc. 6th Annual China grid Conference (ChinaGrid), Liaoning: IEEE, 2011.
[6]W. Gersch and T. Brotherton, “AR model prediction of time series with trends and seasonalities: A contrast with Box-Jenkins modeling”, in Proc. 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, 1980, 19(1): 988-990.
[7]W. Tian, Y. Zhao, Y. Zhong, M. Xu and C. Jing, ”A dynamic and integrated load-balancing scheduling algorithm for Cloud datacenters”, in Proc. International Conference on Cloud Computing and Intelligence Systems (CCIS), Beijing : IEEE, 2011.
[8]X. Li, Z. Qian, R. Chi, B. Zhang, and S. Lu, “Balancing Resource Utilization for Continuous Virtual Machine Requests in Clouds”, in Proc. Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), Palermo: IEEE, 2012.
[9]R. Buyya, A. Beloglazov, and J. Abawajy, “Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges”, in proceedings International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), Las Vegas, USA, July 12-15, 2010.
[10]M. Yue. “A simple proof of the inequality FFD (L)< 11/9 OPT (L)+ 1,for all l for the FFD bin-packing algorithm”. Acta Mathematicae Applicatae Sinica (English Series), 7(4):321331, 1991.
[11]Beloglazov and R. Buyya, “Adaptive Threshold-Based Approach for Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers”, in Proc. 8th International Workshop on Middleware for Grids, Clouds and e-Science, Ney York: ACM, 2010.
[12]M. Mishra and A. Sahoo, “On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach”, in Proc. International Conference on Cloud Computing (CLOUD), Washington: IEEE, 2011.
[13]T.WOOD, P. Shenoy and A. Venkataramani, “Black-box and Gray-box Strategies for Virtual Machine Migration”, in proceedings 4th USENIX conference on Networked systems design & implementation (NSDI), Berkeley: ACM, 2007.
[14]H. ZHENG, L. ZHOU, J. WU, “Design and Implementation of Load Balancing in Web Server Cluster System”, Journal of Nanjing University of Aeronautics & Astronautics, Vol. 38 No. 3 Jun. 2006.
[15]Rodrigo N Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar AF De Rose, and Rajkumar Buyya, “Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms”, Software: Practice and Experience, 41(1):23{50, 2011.
[16]Bhathiya Wickremasinghe, Rodrigo N. Calheiros, and Rajkumar Buyya, “CloudAnalyst: A CloudSim-based Visual Modeller for Analysing Cloud Computing Environments and Applications”, in: Proceedings 24th International Conference on Advanced Information Networking and Applications (AI NA), 2010.
[17]Subramanian S, Nitish Krishna G, Kiran Kumar M, Sreesh P4and G R Karpagam, “An Adaptive Algorithm For Dynamic Priority Based Virtual Machine Scheduling In Cloud”, International Journal of Computer Science Issues (IJCSI), Vol. 9, Issue 6, No 2, November 2012.