Virtual Machine Placement via Bin Packing in Cloud Data Centers

Abstract

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

Optimal Virtual Machine Placement Based on Grey Wolf Optimization

“…where x ij (t + 1) is the updated binary position at iteration t; x ij 1 , x ij 2 , x ij 3 are calculated according to Equations (18)- (20); random is a random number ∈ [0, 1]; and function sigmoid(a) is formulated as follows:…”

“…Every wolf updates its solution according to α, β, and δ via Equation (26). (18)- (20). Applying DGWO-VMP on the example that is illustrated in Figure 1 yields the following: the α solution is represented as S = {3, 1, 3, 1, 1}, which indicates that V 1 is submitted to P 3 , V 3 is submitted to P 1 and so on.…”

“…In [18], an improved genetic algorithm has been introduced for maximizing the multidimensional resource usage and minimizing the communication traffic. In [19,20], the proposed method used multicapacity bin packing to find the optimal assignment for the VMP problem. In [21], a method for optimizing a neural network that forecasts the power consumption via GA was proposed.Grey wolf optimization (GWO) is an evolutionary algorithm.…”

An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

“…Bin packing is used to solve the VM placement problem efficiently [32]. Multiple physical hosts having different capacities are used to pack number of VMs.…”

“…Two objectives are considered in this work: minimization of utilization rate of physical servers and wastage of resources. The first objective is formulated as follows [32]:…”