Heterogeneous Virtual Machine Consolidation Using an Improved Grouping Genetic Algorithm
Abstract: Virtual machine (VM) consolidation is a promising approach for improving energy efficiency of the datacenter by increasing the resource utilization of physical machines. However, the live migration technology that VM consolidation relies on is costly in itself, and this migration cost is usually heterogeneous as well as the datacenter. This paper focuses on how to pay limited migration costs to save as much energy as possible via VM consolidation in a heterogeneous cloud environment. That is, how to minimize the energy consumption while keeping most of the VMs in the datacenter unmoved. To capture these two conflicting objectives, a migration cost estimation method is first proposed and then a consolidation score function is defined for overall evaluation. To maximize the consolidation score, an improved grouping genetic algorithm (IGGA) based on a greedy heuristic and a swap operation is proposed for VM consolidation. Experiments show that IGGA performs better than existing consolidation methods.
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