Global Task Graph-Based Multi-Agent Offloading for Dependency-Aware Satellite Edge Computing

Abstract

Satellite edge computing (SEC) enables low-latency services by leveraging Low Earth Orbit (LEO) satellites. However, emerging applications increasingly generate dependency-aware tasks with complex precedence constraints, leading to challenging multi-DAG (directed acyclic graph) offloading under dynamic satellite environments. To address this issue, we propose a global task graph (GTG)-based multi-agent offloading frame-work named GTG-MARL, for dependency-aware task execution in regional satellite-ground collaborative networks. Specifically, we construct a GTG by merging multiple DAGs to capture both intra-task dependencies and inter-task coupling, thereby achieving unified dependency-aware modeling. Then, a graph neural network (GNN) is lev-eraged to encode the GTG into dependency-aware subtask embeddings. To handle dependency-aware offloading in dy-namic environments, we develop a multi-agent reinforce-ment learning framework based on generalized proximal policy optimization, where each LEO satellite acts as an agent for decision-making while a centralized critic stabilizes offline learning to eliminate real-time on-orbit communication overhead. During the execution phase, only the light-weight trained actors are deployed on satellites for on-orbit inference. Extensive experiments demonstrate that the pro-posed method significantly reduces task completion delay compared to state-of-the-art baselines, validating the practi-cal efficiency and low synchronization cost of the proposed framework for regional SEC deployment.