Cloud computing has come to be a significant commercial infrastructure offering utility-oriented IT services to users worldwide.\nHowever, data centers hosting cloud applications consume huge amounts of energy, leading to high operational cost and greenhouse\ngas emission. Therefore, green cloud computing solutions are needed not only to achieve high level service performance but also\nto minimize energy consumption. This paper studies the dynamic placement of virtual machines (VMs) with deterministic and\nstochastic demands. In order to ensure a quick response toVMrequests and improve the energy efficiency, a two-phase optimization\nstrategy has been proposed, inwhichVMsare deployed in runtime and consolidated into servers periodically. Based on an improved\nmultidimensional space partition model, a modified energy efficient algorithm with balanced resource utilization (MEAGLE) and\na live migration algorithm based on the basic set (LMABBS) are, respectively, developed for each phase. Experimental results have\nshown that under different VMs� stochastic demand variations, MEAGLE guarantees the availability of stochastic resources with a\ndefined probability and reduces the number of required servers by 2.49% to 20.40% compared with the benchmark algorithms. Also,\nthe difference between the LMABBS solution and Gurobi solution is fairly small, but LMABBS significantly excels in computational\nefficiency.
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