Multi-objective Ant Colony Optimization Algorithm Based on Load Balance

Virtual machine (VM) placement is a process of mapping VMs to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony optimization algorithm based on load balance (MACOLB) for the VM placement problem. Firstly, the algorithm for a multi-objective context is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. Secondly, the pheromone adjustment factor (PAF) is given according to the load of physical machine (PM) and the pheromone update rule is transformed correspondingly. Finally, the effectiveness of the proposed algorithm is evaluated by the simulation.