Multi-objective Optimization Algorithm Based on BBO for Virtual Machine Consolidation Problem

Abstract

Cloud computing is a promising technology having ability to influence the way of the provision of computing and storage resources through virtual machine (VM). VM Consolidation is an efficient way to improve power efficiency and quality guarantee for on-demand services. However, it is an integer programming problem and as well as a NP-hard problem to find optimal solutions within polynomial time. In this paper, the VM consolidation problem is formulated as a multi-objective optimization problem, which has three conflicting objectives, i.e., reducing power consumption, achieving good load balancing and shortening VM migration time. We propose a multi-objective optimization algorithm based on biogeography-based optimization (BBO) for the VM consolidation problem, which is named as MBBO/DE: Multi-objective Biogeography-Based Optimization algorithm hybrid with Differential Evolution. It utilizes cosine migration model, differential strategies and Gaussian mutation model to improve the quality of habitats and the ability of finding optimal solutions. Experiments have been conducted to evaluate the effectiveness of MBBO/DE using synthetic and real-world instances. Experimental results show that MBBO/DE obtains a better performance while simultaneously reducing power consumption and achieving good load balancing within a satisfactory time as compared to genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO) and BBO.