Dependent Task Graph Offloading Model Based on Deep Reinforcement Learning in Mobile Edge Computing
I tiakina i:
| I whakaputaina i: | Electronics vol. 14, no. 16 (2025), p. 3184-3208 |
|---|---|
| Kaituhi matua: | |
| Ētahi atu kaituhi: | , , , |
| I whakaputaina: |
MDPI AG
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| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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MARC
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|---|---|---|---|
| 001 | 3244012072 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14163184 |2 doi | |
| 035 | |a 3244012072 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Guo Ruxin |u School of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China; 17737798424@163.com (R.G.); 17776085146@163.com (L.Z.); 1020231176@glut.edu.com (L.L.) | |
| 245 | 1 | |a Dependent Task Graph Offloading Model Based on Deep Reinforcement Learning in Mobile Edge Computing | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Mobile edge computing (MEC) has emerged as a promising solution for enabling resource-constrained user devices to run large-scale and complex applications by offloading their computational tasks to the edge servers. One of the most critical challenges in MEC is designing efficient task offloading strategies. Traditional approaches either rely on non-intelligent algorithms that lack adaptability to the dynamic edge environment, or utilize learning-based methods that often ignore task dependencies within applications. To address this issue, this study investigates task offloading for mobile applications with interdependent tasks in an MEC system, employing a deep reinforcement learning framework. Specifically, we model task dependencies using a Directed Acyclic Graph (DAG), where nodes represent subtasks and directed edges indicate their dependency relationships. Based on task priorities, the DAG is transformed into a topological sequence of task vectors. We propose a novel graph-based offloading model, which combines an attention-based network and a Proximal Policy Optimization (PPO) algorithm to learn optimal offloading decisions. Our method leverages offline reinforcement learning through the attention network to capture intrinsic task dependencies within applications. Experimental results show that our proposed model exhibits strong decision-making capabilities and outperforms existing baseline algorithms. | |
| 653 | |a Scheduling | ||
| 653 | |a Integer programming | ||
| 653 | |a Deep learning | ||
| 653 | |a Edge computing | ||
| 653 | |a Applications programs | ||
| 653 | |a Graph theory | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Decision making | ||
| 653 | |a Neural networks | ||
| 653 | |a Task complexity | ||
| 653 | |a Optimization | ||
| 653 | |a Mobile computing | ||
| 653 | |a Computation offloading | ||
| 653 | |a Attention | ||
| 653 | |a Algorithms | ||
| 653 | |a Methods | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Heuristic | ||
| 653 | |a Workloads | ||
| 653 | |a Energy consumption | ||
| 653 | |a Markov analysis | ||
| 700 | 1 | |a Zhou Lunyu |u School of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China; 17737798424@163.com (R.G.); 17776085146@163.com (L.Z.); 1020231176@glut.edu.com (L.L.) | |
| 700 | 1 | |a Li Linzhi |u School of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China; 17737798424@163.com (R.G.); 17776085146@163.com (L.Z.); 1020231176@glut.edu.com (L.L.) | |
| 700 | 1 | |a Song, Yuhui |u School of Environmental Science and Engineering, Guilin University of Technology, Guilin 541006, China | |
| 700 | 1 | |a Xie Xiaolan |u School of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China; 17737798424@163.com (R.G.); 17776085146@163.com (L.Z.); 1020231176@glut.edu.com (L.L.) | |
| 773 | 0 | |t Electronics |g vol. 14, no. 16 (2025), p. 3184-3208 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244012072/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244012072/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244012072/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |