Computing Tasks Saving Schemes Through Early Exit in Edge Intelligence-Assisted Systems

Xin Niu, Xianwei Lv, Wang Chen, Chen Yu, Hai Jin

Abstract: Edge intelligence (EI) is a promising paradigm where end devices collaborate with edge servers to provide artificial intelligence services to users. In most realistic scenarios, end devices often move unconsciously, resulting in frequent computing migrations. Moreover, a surge in computing tasks offloaded to edge servers significantly prolongs queuing latency. These two issues obstruct the timely completion of computing tasks in EI-assisted systems. In this paper, we formulate an optimization problem aiming to maximize computing task completion under latency constraints. To address this issue, we first categorize computing tasks into new computing tasks (NCTs) and partially completed computing tasks (PCTs). Subsequently, based on model partitioning, we design a new computing task saving scheme (NSS) to optimize early exit points for NCTs and computing tasks in the queuing queue. Furthermore, we propose a partially completed computing task saving scheme (PSS) to set early exit points for PCTs during computing migrations. Numerous experiments show that computing saving schemes can achieve at least 90% computing task completion rate and up to 61.81% latency reduction compared to other methods.