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

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Publicado en:Mathematics vol. 13, no. 7 (2025), p. 1183
Autor Principal: Wang, Shangpeng
Outros autores: Zhang, Chenyuan, Su, Zihan, Liu, Limin, Long, Jun
Publicado:
MDPI AG
Materias:
Parallel processing
Game theory
Task scheduling
Deep learning
Collaboration
Satellite communications
Priorities
Optimization
Resource allocation
Mapping
Decomposition
Energy consumption
Nonlinear programming
Efficiency
Scheduling
Temporal logic
Remote sensing
Intersatellite communications
Cooperation
Genetic algorithms
Convex analysis
Algorithms
Linear programming
Methods
Mixed integer
Resource utilization
Real time
Constraints
Ground stations
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Resumo: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.
ISSN:2227-7390
DOI:10.3390/math13071183
Fonte:Engineering Database