OSGS: A Framework for Online Scheduling of Satellite-Ground Collaborative Inference With Space Edge Computing

Abstract

With the rapid deployment of camera-equipped nanosatellites in Low Earth Orbit (LEO) for high-resolution Earth observation, traditional data processing pipelines that rely solely on ground stations are struggling to manage the escalating data volume and stringent latency requirements. Space Edge Computing (SEC) has emerged as a transformative solution that enables data processing directly on satellites, alleviating the burden on ground infrastructure and facilitating real-time inference for critical applications. However, SEC brings forth new challenges in online scheduling for satellite-ground collaborative inference, especially when optimizing the balance between resource allocation and inference accuracy across large LEO constellations. This article introduces the Online Satellite-Ground Scheduling (OSGS) framework, a novel approach designed to address these challenges by minimizing both system-wide cost and quality of service (QoS) degradation under resource and latency constraints. OSGS dynamically adjusts the selection of ground stations, configuration, and resource provisioning decisions in response to real-time satellite and ground station status, including the amount of collected data, ground station resource pricing, and satellite-ground connection latency. OSGS employs an innovative combination of primal-dual approximation techniques and lazy-switching method to handle the NP-hard, time-coupled challenges inherent in long-term optimization for satellite-ground collaborative inference. We rigorously prove the approximation ratio of the proposed algorithm and further derive the competitive analysis of the online algorithm framework. Extensive trace-driven simulations verify the effectiveness of the proposed framework, where OSGS demonstrates significant improvements over baseline methods.