Abstract:  The paper researches on the migrations of virtual machines among hosts to improve system load balancing degree (including two aspects: CPU and disk I/O) while reducing the migration cost as much as possible. So, the purpose is to find out the best possible mapping scheme between hosts and virtual machines in the system. This paper puts forward the concept of affinity about virtual machine, and defines the calculation method of affinity index; then builds the virtual machine scheduling model based on genetic algorithm. In the model, the crossover operation drives the affinity index of the mapping scheme increased as much as possible, the mutation operation makes the difference between CPU and disk I/O of host tending to converge. In each generation, selection strategy selects bigger fitness in each pair of parent individuals and child individual, so that the population is constantly evolving, and the final solution space of the mapping scheme is obtained. The paper proposes a VM balanced scheduling algorithm based on genetic algorithm, the algorithm selects the best solution from the final solution space of the mapping scheme, which considers the load balancing problem from a global perspective; the algorithm calculates the impact of migration in advance and carries out substantive migration after obtaining the best mapping scheme, therefore, it reduces the migration cost; the algorithm uses MTALB algorithm to allocate multi-type tasks evenly to VMs, the effect of system load balancing is better. Experimental results show that the proposed algorithm has overall advantages over first fit and round robin algorithm and NABM algorithm in terms of specific indicators of migration cost and system load balancing. In the key indicator-task processing rate， it is respectively improves by 25% and 12% than that of the first fit and round robin scheduling algorithm and NABM algorithm.

Key words: cloud computing, virtual machine scheduling and migration, affinity, genetic algorithm, MTALB algorithm； load balancing