Latency-Optimized Multi-User Task Offloading Scheme Using Dynamic Priority and Duplication in Edge Computing

The extensive development of complex applications in embedded devices has driven the rapid development of edge computing, which provides powerful processing capabilities to the edge network. In this context of development, task offloading has received widespread attention as one of the key issues in edge computing. However, since a task usually consists of multiple subtasks with dependencies, the current computing subtask on the local or edge server must wait for the completion of the previous dependent subtask. As a result, existing offloading schemes are often constrained by the complexity of the task topology. Therefore, this paper is aimed at tasks with dependencies described as Directed Acyclic Graphs (DAGs) in devices. First, we optimize the order of subtasks with dynamic priorities. Meanwhile, we employ task duplication to reduce communication latency and thus overall task completion time. Additionally, to support the edge computing environment of multi-user and multi-server, game theory is used to find the optimal offload position for each user, so as to obtain the minimum average computing delay of all tasks. Experimental results show that the proposed algorithm outperforms existing algorithms in terms of task completion delay.