Delay Minimization in Multi-Slice Resource Allocation for User-Edge-Cloud Collaborative Offloading

抄録

With the expansion of industrial applications and data volumes, building a user-edge-cloud collaborative network and efficiently distributing computing tasks has emerged as a critical solution to alleviate terminal computing burdens and ensure quality of service (QoS). However, existing studies often overlook extra delays in the offloading process, including queuing, propagation, and wired transmission delays, significantly impacting task delay and offloading strategies. To this end, this paper proposes a multi-slice task offloading and resource allocation scheme for user-edge-cloud networks, targeting delay minimization while considering various delay factors. This scheme jointly optimizes offloading mode, offloading ratio, user association, and resource allocation under task delay constraints. To address the problem's non-convexity, an alternating optimization framework is employed to decompose the problem into offloading mode selection and resource allocation subproblems. Specifically, a deep reinforcement learning (DRL)-based algorithm is developed for offloading mode selection, while convex optimization techniques are applied to determine optimal offloading ratios and resource allocation. Additionally, a matching theory-based algorithm establishes optimal connections between users and base stations (BSs). Simulations validate the effectiveness of the proposed scheme, showing that the three-layer offloading mode, i.e., collaborative computing across user, edge, and cloud reduces latency compared to single-layer and two-layer modes for large-scale tasks.