基于改进混合遗传算法的云资源调度算法

在云计算中，系统规模和虚拟机迁移数量都是十分庞大的，需要高效的调度策略对其进行优化。将云计算的任务分配抽象为背包求解问题，可通过遗传算法进行求解。传统的遗传算法具有局部搜索能力差以及早熟现象的缺点，本文采用遗传和贪婪相结合的混合遗传算法。针对混合遗传算法在资源利用率与能源消耗的收敛速度较慢问题，本文通过改进适应度函数，改变了适应度函数在不同染色体间的差异度，从而提高了染色体在选择算子中的择优性能。仿真结果表明，该方法能够有效提高混合遗传算法在云计算资源优化中的收敛速度。

Cloud Resource Scheduling Based on Improved Hybrid Genetic Algorithm

The size of system and the number of virtual machine migration in cloud computing are very large, for which the efficient scheduling strategy is essential. The task allocation for cloud computing can be abstracted to knapsack problem, and then is solved by genetic algorithm. The traditional genetic algorithm has the shortcoming of poor local searching ability and precocious phenomenon, which can adopt the combination of genetic and greed hybrid genetic algorithm to solve. For hybrid genetic algorithm convergence speed problem in resource utilization and energy consumption, in this paper, we change the fitness function to increase the difference of chromosomes and improve the performance of chromosome preferred in selection operator. The simulation results show that this method can effectively improve the hybrid genetic algorithm convergence speed in cloud computing resources optimization.