Task Offloading Scheme Based on PTSS-DQN Algorithm for Latency Optimization in Multi-User and Multitasking Environments

Edge computing is gaining widespread attention, particularly in the domain of task offloading. Efficiently and accurately offloading tasks to edge servers presents a significant challenge due to its complexity. In this paper, we depart from the traditional user-to-edge offloading scenario and instead focus on a model where users offload tasks to base stations. These base stations, equipped with servers and possessing an extended communication range, centrally manage all tasks. A base station can either offload tasks to a specific edge server within its communication range or handle them locally. To address the scheduling challenge among multiple tasks, we propose a priority-based Time Slice Scheduling (PTSS) algorithm to calculate task processing latency. Additionally, we introduce the PTSS-DQN algorithm by integrating the Deep Q-Network (DQN) to jointly optimize the offloading strategy, resource allocation, time-slice distribution, and task execution order. Experimental results demonstrate the effectiveness of our proposed approach.