With the increasing size of cloud data centers, the number of users and virtual machines\n(VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers.\nThe dramatic growth of internet services results in unbalanced network resources. Resource\nmanagement is an important factor for the performance of a cloud. Various techniques are used\nto manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient\nstrategy to balance the load of cloud data centers. VM-placement is an important subproblem of\nthe VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is\nto minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy\nand cost. An enhanced levy-based particle swarm optimization algorithm with variable sized\nbin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit\nstrategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to\nauthenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab.\nThe given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy\nflight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the\nnumber of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm\noutperformed the other two algorithms.
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