%0 Journal Article %A Longbao WANG %A Qingyong WANG %A Yichao WANG %A Yingchi MAO %T Computing task offloading based on multi-cloudlet collaboration %D 2020 %R 10.11772/j.issn.1001-9081.2019081367 %J Journal of Computer Applications %P 328-334 %V 40 %N 2 %X

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.

%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019081367