PipeSIA: An Efficient Pipeline-Parallel Distributed Space Inference Approach for LEO Constellations

Large-scale low earth orbit (LEO) satellite constellations have become important to support ubiquitous communications. The vast computational resources embedded within satellite networks are driving the rapid development of space computing and satellite Cloud-native technologies, with various satellite artificial intelligence (AI) applications. However, a single satellite only has limited communication and computation resources, especially when dealing with remote sensing images by deep learning methods. To address this issue, this paper proposed a pipeline inference approach for Cloud-native satellites: PipeSIA, which enables multiple satellites to execute inference tasks cooperatively. A satellite constellation was designed for PipeSIA, whose network topology was also organized as a pipeline for efficient communication. Different from conventional computation offloading schemes, the proposed scheme jointly considers the node sequence and task allocation problem. A joint optimization problem is formulated as a series multi-arms bandit (SMAB). Then <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex>-greedy algorithm and deep Q-network (DQN) are applied to optimize the node selection and task allocation. Simulation results show that the proposed scheme can effectively reduce the overall delay.