Existing task offloading strategy usually makes offloading decision within one time slot without considering the internal relationship between multiple offload time slots, so they cannot be offloaded according to the actual needs of tasks. To solve this problem, this paper proposed a task secondary application offloading strategy based on deep Q network(DQN-TSAO). Firstly, this paper introduced a three-layer of cloud-edge-end architecture that supported task secondary application offloading, and established priority model, delay model and energy consumption model for task offloading. Secondly, aiming at minimizing system energy consumption, it transformed the energy consumption optimization problem into a Markov decision process problem of maximum cumulative offloading reward. Finally, DQN-TSAO algorithm could extract the task offload characteristics of each time slot, which enabled the task to obtain the optimal offloading decision of multiple time slots in the continuous interaction with the environment. Simulation results validate that DQN-TSAO algorithm can effectively reduce the total energy consumption of the system in a period of time.

[2022-07-20] Accepted Paper /doc/2022.04.0220/v1

[2022-12-05] Printed Article /doc/2022.04.0220/v2