Computation Tasks Offloading Scheme Based on Multi-cloudlet Collaboration for Edge Computing
Qingyong Wang, Yingchi Mao, Yichao Wang, Longbao Wang · 2019
Focus on the issue of complex process and long response time of task offloading in multi-cloudlet, a weighted self-adaptive inertia weight particle swarm optimization algorithm (WAIW-PSO) based on multi-cloudlet collaboration is proposed. Firstly, the task execution process of mobile terminal-cloudlet-remote cloud is modeled. Secondly, considering the competition of computing resources by multiple users, the task offloading model based on multi-cloudlet collaboration is constructed. Since the complexity of solving the optimal offloading scheme is excessively high, the WAIW-PSO is proposed to solve the offloading problem. The simulation results show that compared with the standard particle swarm optimization algorithm (PSO) and the particle swarm optimization algorithm with decreasing inertia weight based on Gaussian function (GDIWPSO), WAIW-PSO algorithm can adjust the inertia weight according to evolutionary iteration numbers and individual fitness, and the optimization ability is strong. The time to find the optimal offloading scheme is the shortest. The experiment also shows that the collaborative task offloading scheme based on WAIW-PSO algorithm can reduce the total offloading time by at least 20% compared with the non-collaborative one in the multi-cloudlet system.