Joint Optimization of Caching, Migration, and Offloading in Satellite-Assisted Marine Networks

Satellite-assisted Mobile Edge Computing (MEC) is a promising paradigm for enabling low-latency and high-efficiency computing in deep-sea and far-offshore marine environments. However, the inherent heterogeneity of three-layer marine networks - comprising satellites, Uncrewed Surface Vehicles (USVs), and Autonomous Underwater Vehicles (AUVs) - introduces unique challenges. These include the coupling of underwater acoustic and above-water Radio Frequency (RF) communication links, the highly constrained computing and caching resources of edge devices, and the strong interdependence between service caching, vertical task offloading, and horizontal migration. Existing solutions often overlook these cross-layer dynamics and the spatio-temporal interactions among network nodes, leading to suboptimal task scheduling and degraded system utility. To address these challenges, we formulate a joint optimization problem that maximizes the Quality of Experience (QoE), with decision variables spanning caching placement, task migration, and offloading under resource constraints. Through rigorous theoretical analysis, we prove that the formulated problem is NP-hard, highlighting its inherent computational intractability. To overcome this, we propose an Attention-Enhanced Multi-Agent Reinforcement Learning algorithm (AE-MARL), which adopts a hybrid policy network to learn discrete decisions and continuous resource allocation. Furthermore, a lightweight attention module is integrated to infer the importance of partial observations and guide collaborative decision-making across agents. Extensive experiments and analysis under diverse system configurations demonstrate that AE-MARL consistently outperforms state-of-the-art baselines.