Optimizing Resource Utilization and Performance in LEO Satellite Edge Computing: A Joint Service Deployment and Task Offloading Approach

While service-oriented low Earth orbit (LEO) satellite edge computing (LSEC) frameworks enable diverse edge services for user tasks, the heterogeneous distribution of terrestrial users causes substantial imbalance in computational task loads across satellites. This asymmetric workload leads to inefficient resource utilization and degraded edge computing performance. To address these challenges, we propose a joint optimization framework that integrates edge service deployment and task offloading, supported by service popularity analysis and spatiotemporal user-task modeling. The framework employs a two-timescale design: at the large timescale, an improved atomic orbital search (iAOS) heuristic dynamically optimizes service placement, configuration, and resource allocation; at the small timescale, a direction-selective multi-agent double deep Q-network (DS-MDDQN) leverages deep reinforcement learning to route tasks to the most suitable processing nodes. Extensive simulations show that our approach significantly outperforms six representative baselines in both user-perceived performance and system-level efficiency. Replacement studies further verify the effectiveness of each component: iAOS enhances resource utilization and reduces task failure through optimized service deployment, while DS-MDDQN mitigates network dynamics and lowers task completion latency via adaptive task offloading.