Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
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| Veröffentlicht in: | Mathematics vol. 13, no. 7 (2025), p. 1183 |
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| 024 | 7 | |a 10.3390/math13071183 |2 doi | |
| 035 | |a 3188871976 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Wang, Shangpeng |u School of Computer Science and Engineering, Central South University, Changsha 410083, China; <email>204701019@csu.edu.cn</email> (S.W.); <email>zhangchenyuan@csu.edu.cn</email> (C.Z.); <email>szh@csu.edu.cn</email> (Z.S.); <email>liulimin@csu.edu.cn</email> (L.L.) | |
| 245 | 1 | |a Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Parallel processing | ||
| 653 | |a Game theory | ||
| 653 | |a Task scheduling | ||
| 653 | |a Deep learning | ||
| 653 | |a Collaboration | ||
| 653 | |a Satellite communications | ||
| 653 | |a Priorities | ||
| 653 | |a Optimization | ||
| 653 | |a Resource allocation | ||
| 653 | |a Mapping | ||
| 653 | |a Decomposition | ||
| 653 | |a Energy consumption | ||
| 653 | |a Nonlinear programming | ||
| 653 | |a Efficiency | ||
| 653 | |a Scheduling | ||
| 653 | |a Temporal logic | ||
| 653 | |a Remote sensing | ||
| 653 | |a Intersatellite communications | ||
| 653 | |a Cooperation | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Convex analysis | ||
| 653 | |a Algorithms | ||
| 653 | |a Linear programming | ||
| 653 | |a Methods | ||
| 653 | |a Mixed integer | ||
| 653 | |a Resource utilization | ||
| 653 | |a Real time | ||
| 653 | |a Constraints | ||
| 653 | |a Ground stations | ||
| 700 | 1 | |a Zhang, Chenyuan |u School of Computer Science and Engineering, Central South University, Changsha 410083, China; <email>204701019@csu.edu.cn</email> (S.W.); <email>zhangchenyuan@csu.edu.cn</email> (C.Z.); <email>szh@csu.edu.cn</email> (Z.S.); <email>liulimin@csu.edu.cn</email> (L.L.) | |
| 700 | 1 | |a Su, Zihan |u School of Computer Science and Engineering, Central South University, Changsha 410083, China; <email>204701019@csu.edu.cn</email> (S.W.); <email>zhangchenyuan@csu.edu.cn</email> (C.Z.); <email>szh@csu.edu.cn</email> (Z.S.); <email>liulimin@csu.edu.cn</email> (L.L.) | |
| 700 | 1 | |a Liu, Limin |u School of Computer Science and Engineering, Central South University, Changsha 410083, China; <email>204701019@csu.edu.cn</email> (S.W.); <email>zhangchenyuan@csu.edu.cn</email> (C.Z.); <email>szh@csu.edu.cn</email> (Z.S.); <email>liulimin@csu.edu.cn</email> (L.L.) | |
| 700 | 1 | |a Long, Jun |u School of Computer Science and Engineering, Central South University, Changsha 410083, China; <email>204701019@csu.edu.cn</email> (S.W.); <email>zhangchenyuan@csu.edu.cn</email> (C.Z.); <email>szh@csu.edu.cn</email> (Z.S.); <email>liulimin@csu.edu.cn</email> (L.L.); Big Data Institute, Central South University, Changsha 410083, China | |
| 773 | 0 | |t Mathematics |g vol. 13, no. 7 (2025), p. 1183 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3188871976/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3188871976/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3188871976/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |