A Simulated Annealing Combined Genetic Algorithm for Virtual Machine Migration in Cloud Datacenters
Yidong Li, Xiangjun Meng, Hairong Dong · 2016
Resource allocation in data centers is a significant research area in cloud computing. A high-efficiency resource allocation strategy can save the operating cost for cloud service providers, and less amount of carbon dioxide emissions to the atmosphere. While the Service Level Agreement(SLA) of customers can be guaranteed. So the cloud providers have to deal with the cost-performance trade-off: the minimisation of cost, while meeting the SLAs. In this paper, we present a simulated annealing combined genetic algorithm based virtual machine migration strategy for solving the resource allocation and scheduling problem in cloud computing environment, which models the resource allocation as a binary multiple knapsack problem. Experimental results show that this method is able to achieve better data center operation cost then basic genetic algorithms.