Abstract
A virtual machine placement strategy based on the trade-off between energy consumption and SLA is presented. Aiming at dynamical changes of workload requirements, a self-adaptive placement strategy RLWR based on robust local weight regression is presented, which could decide the overload time of hosts dynamically. After detecting overloaded hosts, one virtual machine migration selection algorithm MNM is proposed. The MNM’s objective is to get minimal migration number. The migrated virtual machines are deployed using bin-packing algorithm PBFDH. The experimental results show that our algorithm has obvious advantages than other algorithms.
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Zhang, X., Yue, Q., He, Z. (2014). Dynamic Energy-Efficient Virtual Machine Placement Optimization for Virtualized Clouds. In: Jia, L., Liu, Z., Qin, Y., Zhao, M., Diao, L. (eds) Proceedings of the 2013 International Conference on Electrical and Information Technologies for Rail Transportation (EITRT2013)-Volume II. Lecture Notes in Electrical Engineering, vol 288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53751-6_47
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DOI: https://doi.org/10.1007/978-3-642-53751-6_47
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