Multi‐Agent Based Online Cooperative Computation Offloading and Migration Strategy for Vehicular Edge Computing

ABSTRACT Vehicular edge computing (VEC) has emerged as a promising paradigm to reduce the latency of vehicular tasks by leveraging edge computing resources. However, the high mobility of vehicles and the limited computational capacity of edge servers (ESs) present significant challenges to achieving efficient VEC. To address these challenges, this paper proposes a fine‐grained computation task cooperative offloading and migration strategy. Specifically, applications are decomposed into multiple interdependent subtasks, which are collaboratively executed across multiple ESs. As vehicles move, computation tasks are dynamically migrated among ESs to ensure service continuity. The joint optimisation of task offloading and migration is formulated as a multi‐stage mixed integer non‐linear programming problem. To tackle this problem, we first employ Lyapunov optimisation to transform the multi‐stage problem into a deterministic optimisation problem at each time slot, aiming to maximise long ‐term system revenue. Furthermore, considering the dynamic environment characterised by vehicle mobility, time‐varying channels, subtask dependencies and inter‐vehicle channel interference, we integrate a graph convolutional network (GCN) into the counterfactual multi‐agent policy gradients (COMA) framework. By integrating Lyapunov optimisation with COMA‐GCN, we propose Ly‐COMA, a novel algorithm that effectively minimises the average task execution delay. Extensive experimental results demonstrate that the proposed algorithm outperforms existing methods in terms of average delay reduction and migration cost efficiency.