移动云环境面向多重服务选择的计算卸载算法

移动云计算可以通过计算卸载改善移动设备的能效和应用的执行延时。然而面对云端的多重服务选择时，计算卸载决策是NP问题。为了解决这一问题，提出一种遗传算法寻找计算卸载的最优应用分割决策解。遗传种群初始化中，算法联立预定义和随机染色体方法进行初始种群的生成，减少了无效染色体的发生比例。同时，算法为预定义的预留种群设计一种特定的基于汉明距离函数的适应度函数，更好地衡量了染色体间的差异。种群交叉中分别利用近亲交配与杂交繁育丰富了种群个体。算法通过修正的遗传操作减少了无效解的产生，以更合理的时间代价获得了应用分割的最优可行解。应用现实的移动应用任务图进行仿真实验评估了算法效率。评估结论表明，所设计的遗传算法在应用执行能耗、执行时间以及综合权重代价方面均优于对比算法。

Offloading decision algorithm for multiple service selection in mobile cloud environment

Mobile cloud computing can use the computation offloading to improve the energy efficiency of mobile devices and the execution delay of applications. However, in the face of the multiple service selection from cloud, the computation offloading decision is a NP problem. In order to address this problem, this paper designed a genetic algorithm to find the best application partitioning decision solution for computation offloading. In genetic population initialization, this algorithm combined of predefined and random chromosomes to intialize the population, which could reduce the generation of the inefficient chromosomes. At the same time, the algorithm designed a specific fitness function based on Hamming distance function for the predefined reserved populution, which could better measure the difference between chromosomes. The population crossover used respectively the inbreeding and crossbreeding to enrich individual species. The algorithm used the modified genetic operations to reduce the ineffective solutions and obtain the best feasible solution in a reasonable time. It evaluated the efficiency of the proposed algorithm using graphs of real mobile applications in simulation experiments. The evaluated conclusions denote that this designed algorithm has a better performance than the comparision algorithms on the application execution energy, the execution time and the overall weight cost.