基于差分进化搜索的边边协同计算任务卸载算法OA

Edge computing power network(EdgeCPN),as a new computing paradigm,flexibly sched-ules fragmented computing resources in CPN to meet different task requirements,enabling efficient computing-task offloading for large-scale terminal scenarios.This paper proposes a mobile device comput-ing task offloading model based on edge-to-edge collaboration.This model divides task offloading in EdgeCPN into two phases:the construction of the edge computing resource pool and the allocation of end-edge resources.We propose a differential evolutionary search scheme based on the optimal-cost comput-ing resource pool to minimize the total delay of task offloading.First,a subset of the optimal cost comput-ing resource pool is selected based on the user's budget.Second,leveraging the available computing re-sources of the pool,the differential evolution algorithm is used to jointly optimize the task offloading deci-sions of the mobile devices with the objective of minimizing total delay,thereby matching each terminal's computing task to an appropriate server.Simulation results show that this scheme significantly improves the efficiency and stability of computing resource scheduling performance in EdgeCPN.