Task Offloading Optimization Design for Delay-Sensitive and Energy-Constrained Applications in Fog Computing

Task offloading is a key technology in fog computing, which allows resource-intensive tasks to be offloaded from terminal devices to fog nodes with higher computing capabilities. However, traditional task offloading methods usually use heuristic methods, which are highly dependent on the mode. They cannot effectively optimize the delay, resulting in a decline in service quality. To solve this problem, a task offloading method based on deep reinforcement learning (DRL) is proposed in this paper. We applied various methods to improve the Deep Q-Network (DQN) and utilized it for task offloading to enhance system performance in fog computing.