Delay Minimization in Hybrid Edge Computing Networks: A DDQN-Based Task Offloading Approach

In this paper, we investigate the task offloading strategy in a hybrid edge computing networks, where the tasks from end devices can be either executed locally, offloaded to the edge server or forwarded to other friendly devices for processing. In addition, these tasks in system are also assumed to be generated stochastically and with different priorities. With respect to the model, we consider minimizing the total task delay of the system while ensuring that the high priority tasks been completed precedently. To do so, an optimization problem is formulated to determine the task offloading strategy for each task. A deep reinforcement learning approach is designed to solve the problem, where the double deep Q network (DDQN) is employed as the agent module. Simulation results show that the proposed algorithm achieves 25% higher utility than the greedy one. In addition, the performance is only 11% lower compared to the optimal solution given by exhaustive search, which confirms the effectiveness of the proposed algorithm.