Abstract
The integration of edge computing and satellite networks has emerged as a promising solution to support remote terrestrial computation with wide coverage and low latency. However, single-satellite computing leads to uneven resource utilization and degraded service quality. To address this, peer offloading is required to improve both service quality and resource efficiency. Asides from peer offloading, diverse service requests also call for an appropriate service deployment strategy, which should be jointly optimized with the offloading decision. In this article, taking into processing cost and service update cost, we formulate a long-term optimization for service deployment and peer offloading. To pursue long-term performance, the problem is first reformulated into a sequence of time-invariant problems. Since frequent service deployment adjustments incur overhead and may cause service interruption, we decompose the time-invariant problem into a service deployment subproblem and a peer offloading subproblem, optimized at different timescales. A hierarchical method iteratively solves the two subproblems. In particular, we propose an online distributed algorithm for small-timescale peer offloading. Each local peer offloading problem is transformed into a capacity-constrained minimum cost maximum flow (C-MCMF) problem, enabling a low-complexity solution via the successive shortest path algorithm. We provide a theoretical analysis showing that the proposed algorithm asymptotically approaches the offline optimum at the expense of system congestion. Moreover, we show that the performance bound grows with the large-timescale interval. Simulation results validate the theoretical analysis and demonstrate the effectiveness of the proposed algorithm in terms of processing cost and service update cost.