Enabling Real-Time Computing and Transmission Services in Large-Scale LEO Satellite Networks

Abstract

Enhanced by onboard computing resources and intersatellite links (ISLs), large-scale satellite networks (SNs) can supplement terrestrial networks, facilitating real-time edge computing and transmission in remote areas at low latency. This capability benefits applications such as real-time remote object tracking, vehicular edge computing, and environmental monitoring. However, different from terrestrial vehicular networks, satellite mobility and the intermittent nature of ISLs often render many computing satellites inaccessible, necessitating efficient scheduling scheme for joint computing and communication resource allocation. To meet this requirement, we investigate joint computing and transmission strategy for real-time computing applications over SNs to reduce end-to-end delays. We formulate the real-time computing and transmission problem over time-varying SNs as an integer linear programming problem. The mathematical optimization method has an exponential time complexity of O(2|Lτ | (4|Vτ | + 4|Lτ |)2|Lτ |) in the worst case, which grows rapidly with the growth of network sizes. Here, |Vτ | and |Lτ | denote the number of nodes and links, respectively, within the time windowτ . Resorting to the designed k-shortest path-based method yields a worst-case complexity ofO(|Vτ |! |Vτ |3).While efficient under common scenarios, its running time is still relatively unstable and may become tens of times longer when computing resources become scarce. To enhance efficiency, we further design a graphbased algorithm that leverages the solution space's unique structure, thereby achieving optimal solutions with polynomial time complexity O(3|Lτ | + (2 log |Vτ | + 1)|Vτ |). Extensive evaluations conducted on the world's largest Starlink system validate the efficacy of the designed methods and highlight their potential synergy.