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Service Composition for Satellite Computing
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AI
DOI：10.1109/tsc.2026.3675415.png)
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Abstract
En 中文
The diverse array of services provided by satellite constellations now plays a crucial role in human life, economic development, and national security. Imagine a scenario where, regardless of a user’s location on Earth, commands can be transmitted to a satellite constellation. The system intelligently selects the most suitable services across the satellites, dynamically combining them to ensure rapid and seamless global fulfillment of the user’s requirements. Although ideal in concept, the utilization of services is constrained by the limitations of satellite performance. This issue becomes especially critical when multiple services are requested simultaneously within a satellite constellation. An inefficient service composition strategy can fail to ensure adequate quality of service for users while also increases system load and significantly shortens the satellites’ lifespan. In this work, we consider a service composition problem under a constrained space scenario, and we introduce a Cooperative-enhanced Multi-Agent RL-based model (named CMAR) to address this issue. Specifically, CMAR formalizes the service composition problem as a Multi-agent Markov Decision Process, where two agents are designed to manage the selection for satellites and services separately to shrink the searching space. Considering collaborative difficulties caused by such a decomposition strategy, where agents can only observe partial changes in the environment, a mask-guided QMIX network is further designed to strengthen their corporation for proper joint actions. Our proposed method has been evaluated on one real-world dataset released by Tiansuan Constellation and two simulated datasets, the empirical results show that our model can improve the performance significantly compared with both traditional and RL-based baselines.
Keywords:
Service composition
satellite computing
cooperative-enhanced MARL
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