Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping

Shangpeng Wang, Chenyuan Zhang, Zihan Su, Limin Liu and Jun Long ()
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Shangpeng Wang: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Chenyuan Zhang: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Zihan Su: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Limin Liu: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Jun Long: School of Computer Science and Engineering, Central South University, Changsha 410083, China

Mathematics, 2025, vol. 13, issue 7, 1-30

Abstract: Multi-satellite collaborative computing has achieved task decomposition and collaborative execution through inter-satellite links (ISLs), which has significantly improved the efficiency of task execution and system responsiveness. However, existing methods focus on single-task execution and lack multi-task parallel processing capability. Most methods ignore task priorities and dependencies, leading to excessive waiting times and poor scheduling results. To address these problems, this paper proposes a task decomposition and resource mapping method based on task priorities and resource constraints. First, we introduce a graph theoretic model to represent the task dependency and priority relationships explicitly, combined with a novel algorithm for task decomposition. Meanwhile, we construct a resource allocation model based on game theory and combine it with deep reinforcement learning to achieve resource mapping in a dynamic environment. Finally, we adopt the theory of temporal logic to formalize the execution order and time constraints of tasks and solve the dynamic scheduling problem through mixed-integer nonlinear programming to ensure the optimality and real-time updating of the scheduling scheme. The experimental results demonstrate that the proposed method improves resource utilization by up to about 24% and reduces overall execution time by up to about 42.6% in large-scale scenarios.

Keywords: task decomposition; satellite network; resource mapping; reinforcement learning; task dependency (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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