基于多微云协作的计算任务卸载

针对多微云计算模式下计算任务卸载过程复杂、任务响应时间长的问题，构建面向多微云协作的计算任务卸载模型，并提出加权自适应惯性权重的粒子群优化（WAIW-PSO）算法，快速求解最优卸载策略。首先，对移动终端-微云-远程云的任务执行过程进行建模；其次，考虑多用户对计算资源的竞争，构建基于多微云协作的任务卸载模型；最后，针对求解最佳任务卸载策略复杂度过高的情况，提出WAIW-PSO算法求解卸载问题。仿真实验结果表明，与标准粒子群优化（PSO）算法以及基于高斯函数递减惯性权重的粒子群优化（GDIWPSO）算法相比，WAIW-PSO算法可以根据进化代数和个体适应度综合调整惯性权重，寻优能力较强，求解最优卸载策略的时间最短；在不同设备数、任务数等情况下选择不同任务卸载策略进行对比实验的结果表明，基于WAIW-PSO算法的卸载策略可以明显缩短任务总完成时间。

Computing task offloading based on multi-cloudlet collaboration

Focusing on the problems of complex process and long response time of task offloading in multi-cloudlet mode， a computing task offloading model based on multi-cloudlet collaboration was constructed， and a Weighted self-Adaptive Inertia Weight Particle Swarm Optimization （WAIW-PSO） algorithm was proposed to solve the optimal offloading scheme quickly. Firstly， the task execution process of mobile terminal-cloudlet-remote cloud was modeled. Secondly， considering the competition of computing resources by multiple users， the task offloading model based on multi-cloudlet collaboration was constructed. Finally， since the complexity of solving the optimal offloading scheme was excessively high， the WAIW-PSO was proposed to solve the offloading problem. Simulation results show that compared with the standard Particle Swarm Optimization （PSO） algorithm and the PSO algorithm with Decreasing Inertia Weight based on Gaussian function （GDIWPSO）， WAIW-PSO algorithm can adjust the inertia weight according to evolutionary generation and individual fitness， and it has the better optimization ability and the shortest time for finding the optimal offloading scheme. Experimental results on different task unloading schemes with different numbers of equipments and tasks show that the WAIW-PSO algorithm based offloading schemes can significantly shorten the total task completion time.