On-Orbit Computing and Caching: A Distributed Regret Learning Approach

Abstract

Satellite-Terrestrial Integrated Networks (STINs) leverage the global reach of satellite systems to push onboard computing and caching resources toward the network edge, enabling truly ubiquitous, anytime-anywhere services for remote Internet of Things (IoT) applications. In this context, efficiently orchestrating the constrained onboard computing and caching resources under stochastic service demands in a scalable, low-complexity, and resilient manner remains a critical challenge. Existing solutions primarily rely on centralized optimization or multi-agent learning techniques, which struggle to cope with the non-stationarity arising from the interdependence of autonomous agent decisions. In this work, we take a step further and develop a distributed game-theoretic framework for joint task offloading and service caching in STINs, providing provable equilibrium guarantees. Specifically, the joint problem is formulated as a non-cooperative stochastic game among IoT devices that autonomously determine their computing, association, and satellite caching strategies to minimize their end-to-end latency subject to energy and cache capacity constraints. The formulated game is proven to converge to a Correlated Equilibrium (CE), which generalizes the Nash Equilibrium (NE) to correlated, probabilistic strategy profiles across devices. Two distributed no-regret learning algorithms, operating under different information availability and rationality regimes, are introduced to derive the CE. The effectiveness and efficiency of the two no-regret learning algorithms are validated through extensive simulations, considering alternative equilibria, learning-based methods, baseline computing schemes, and varying network and algorithm configurations.