Multi-objective optimization for initial virtual machine placement in cloud data center

Virtual machine (VM) placement in the cloud infrastructure is an important problem that remains to be effectively addressed. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for cloud data center. In this paper, a management framework for virtual machine placement in an IaaS environment was firstly presented, then the initial VM placement problem was defined as a multi-objective optimization problem and finally multi-objective optimization for initial virtual machine placement based on Ant Colony Optimization (ACO) was proposed to determine VM placement strategy. The proposed algorithm is a distributed optimization method, which is beneficial to parallel computing. It has the positive feedback mechanism, and through the pheromone is constantly updated, it can get the optimal solution by the efficient convergence. Experimental results show that compared to heuristic method and genetic algorithm, the proposed algorithm can achieve the optimal balance in multiple conflict objectives, which effectively reduces the resource wastage and power consumption, and minimize violation of SLA.