Graph-Aware Temporal Encoder Based Service Migration and Resource Allocation in Satellite Networks

TL;DR

This paper introduces GATE, a graph-aware temporal encoder, to optimize service migration and resource allocation in satellite networks, addressing the challenges of dynamic coverage and limited resources for latency-sensitive applications.

Contribution

It proposes a novel spatio-temporal graph neural network framework combined with a hybrid reinforcement learning approach for efficient satellite service management.

Findings

GATE effectively models spatial and temporal dependencies in satellite networks.

The proposed method improves service continuity and resource utilization.

Simulation results demonstrate superior performance over baseline strategies.

Abstract

The rapid expansion of latency-sensitive applications has sparked renewed interest in deploying edge computing capabilities aboard satellite constellations, aiming to achieve truly global and seamless service coverage. On one hand, it is essential to allocate the limited onboard computational and communication resources efficiently to serve geographically distributed users. On the other hand, the dynamic nature of satellite orbits necessitates effective service migration strategies to maintain service continuity and quality as the coverage areas of satellites evolve. We formulate this problem as a spatio-temporal Markov decision process, where satellites, ground users, and flight users are modeled as nodes in a time-varying graph. The node features incorporate queuing dynamics to characterize packet loss probabilities. To solve this problem, we propose a Graph-Aware Temporal Encoder (GATE) that jointly models spatial correlations and temporal dynamics. GATE uses a two-layer graph convolutional network to extract inter-satellite and user dependencies and a temporal convolutional network to capture their short-term evolution, producing unified spatio-temporal representations. The resulting spatial-temporal representations are passed into a Hybrid Proximal Policy Optimization (HPPO) framework. This framework features a multi-head actor that outputs both discrete service migration decisions and continuous resource allocation ratios, along with a critic for value estimation. We conduct extensive simulations involving both persistent and intermittent users distributed across real-world population centers.