移动云环境面向多重服务选择的计算卸载算.

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 d ecision is a NP problem. In order to address this problem, this paper designed a genetic algorithm to find the best application p artitioning decision solution for computation offloading. In genetic population initialization, this algorithm combined of predefined and random chromosomes to initialize 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 population, which could better measure the difference between chromosomes. The population crossover used respectively the inbreeding and c rossbreeding to enrich individual species. The algorithm used the modified genetic operations to reduce the ineffective solutions and obtain the best feas ible solution in a reasonable time. It evaluated the efficiency of the proposed algorithm using graphs of real mobile applications in simulation experime nts. 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. [ABSTRACT FROM AUTHOR]