Abstract

In view of the increasing number of edge servers in edge computing environment, how to make each server give full play to its own performance and improve the efficiency of task migration has become the focus of edge computing research. In this paper, an optimized discrete binary particle swarm optimization (DBPSO) algorithm is proposed for task migration. By optimizing the inertia weight and learning factors, the binary particle swarm optimization algorithm is compared with the random task distribution, and the optimal solution of task migration is obtained. Through the simulation test in cloudsim, the optimized algorithm can migrate tasks more efficiently. The effectiveness is reflected in the time complexity and energy consumption of migration.

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