A Decentralized Priority Offloading Strategy Based on DQN
Edge Computing(EC)provides users with low-latency,high-response services at the edge of a network.Therefore,task offloading strategies with high-resource utilization and low-latency have become a popular research direction.However,most existing task offloading research is based on a centralized architecture,and offloading strategies and resource scheduling are conducted using centralized facilities,which produce additional energy consumption,high-latency,and are susceptible to the risk of a single point of failure.To address these challenges,a Decentralized Priority Deep Q Network(DP-DQN)offloading strategy is proposed.First,a communication matrix is established to simulate the limited communication state of the edge server.Second,by setting task priorities,tasks can be switched between different edge servers to ensure that each server independently formulates uninstallation policies,to complete decentralization of the uninstallation task.Finally,additional computing resources are allocated to tasks based on the number of jumps to improve resource utilization efficiency and the optimization effect.To verify the effectiveness of the proposed strategy,a comparative study is conducted on the convergence performance of parameters under different DQN.The experimental results demonstrate that in different testing scenarios,the performance of the proposed DP-DQN strategy is superior to that of local,fully greedy,and multi-objective task offloading algorithms,and the performance could be improved by approximately 11％-19％.
万方数据
维普
