Deep Reinforcement Learning for Offloading and Shunting in Hybrid Edge Computing Network
Jiadong Zhang, Wenxiao Shi, Ruidong Zhang, Sicheng Liu · 2021
Multi-access edge computing (MEC) is a distributed computing framework that provides computation capability and data storage at the edge of the network to save bandwidth and reduce latency. However, the computing capacity of the MEC system could be insufficient when an excessive number of tasks are offloaded for execution. To alleviate the excessive burden on MECSs and tap the underutilized resources of WDs, we investigate the computation offloading and shunting problem in the hybrid edge computing (HEC) network. In this paper, we formulate an optimization problem to minimize the average weighted sum of total time delay and energy consumption. Due to the high computational complexity and dimensionality, we propose the deep reinforcement learning-based computation offloading and shunting (DCOS) algorithm to solve this problem. Finally, we validate the convergence property and evaluate the time complexity of the DCOS algorithm. Compared with the other algorithms, the simulation results show that the DCOS algorithm can reduce the average weighted cost significantly.