Research on Edge-Device Collaborative Task Offloading for Dependent Tasks

Edge-device collaborative computing can provide efficient computing services for emerging intelligent applications by coordinating the resources of terminal devic-es and edge servers. Given the characteristics of such applications, which are usually composed of multiple dependent subtasks, how to achieve efficient task collaborative processing becomes a key challenge. This paper models dependent tasks as DAG, and its multi-node collaborative scheduling problem has been proven to be an NP-hard problem. To solve this problem, this paper designs a two-stage optimization framework: first, the predicted cost priority is introduced in the task sorting stage, the subtask computational cost is estimated to dynami-cally adjust the order, and combined with the deep reinforcement learning meth-od, the optimal unloading location is matched for each task based on the proximal policy optimization PPO algorithm. Simulation results show that the designed method can effectively reduce the cost of task completion.