GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916718923743232 |
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| author | Liang, Ruihuai Yang, Bo Chen, Pengyu Cao, Xuelin Yu, Zhiwen Debbah, Mérouane Niyato, Dusit Poor, H. Vincent Yuen, Chau |
| author_facet | Liang, Ruihuai Yang, Bo Chen, Pengyu Cao, Xuelin Yu, Zhiwen Debbah, Mérouane Niyato, Dusit Poor, H. Vincent Yuen, Chau |
| contents | Optimization is crucial for MEC networks to function efficiently and reliably, most of which are NP-hard and lack efficient approximation algorithms. This leads to a paucity of optimal solution, constraining the effectiveness of conventional deep learning approaches. Most existing learning-based methods necessitate extensive optimal data and fail to exploit the potential benefits of suboptimal data that can be obtained with greater efficiency and effectiveness. Taking the multi-server multi-user computation offloading (MSCO) problem, which is widely observed in systems like Internet-of-Vehicles (IoV) and Unmanned Aerial Vehicle (UAV) networks, as a concrete scenario, we present a Graph Diffusion-based Solution Generation (GDSG) method. This approach is designed to work with suboptimal datasets while converging to the optimal solution large probably. We transform the optimization issue into distribution-learning and offer a clear explanation of learning from suboptimal training datasets. We build GDSG as a multi-task diffusion model utilizing a Graph Neural Network (GNN) to acquire the distribution of high-quality solutions. We use a simple and efficient heuristic approach to obtain a sufficient amount of training data composed entirely of suboptimal solutions. In our implementation, we enhance the backbone GNN and achieve improved generalization. GDSG also reaches nearly 100\% task orthogonality, ensuring no interference between the discrete and continuous generation tasks. We further reveal that this orthogonality arises from the diffusion-related training loss, rather than the neural network architecture itself. The experiments demonstrate that GDSG surpasses other benchmark methods on both the optimal and suboptimal training datasets. The MSCO datasets has open-sourced at this http URL, as well as the GDSG algorithm codes at https://github.com/qiyu3816/GDSG. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_08296 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Liang, Ruihuai Yang, Bo Chen, Pengyu Cao, Xuelin Yu, Zhiwen Debbah, Mérouane Niyato, Dusit Poor, H. Vincent Yuen, Chau Networking and Internet Architecture Machine Learning Optimization is crucial for MEC networks to function efficiently and reliably, most of which are NP-hard and lack efficient approximation algorithms. This leads to a paucity of optimal solution, constraining the effectiveness of conventional deep learning approaches. Most existing learning-based methods necessitate extensive optimal data and fail to exploit the potential benefits of suboptimal data that can be obtained with greater efficiency and effectiveness. Taking the multi-server multi-user computation offloading (MSCO) problem, which is widely observed in systems like Internet-of-Vehicles (IoV) and Unmanned Aerial Vehicle (UAV) networks, as a concrete scenario, we present a Graph Diffusion-based Solution Generation (GDSG) method. This approach is designed to work with suboptimal datasets while converging to the optimal solution large probably. We transform the optimization issue into distribution-learning and offer a clear explanation of learning from suboptimal training datasets. We build GDSG as a multi-task diffusion model utilizing a Graph Neural Network (GNN) to acquire the distribution of high-quality solutions. We use a simple and efficient heuristic approach to obtain a sufficient amount of training data composed entirely of suboptimal solutions. In our implementation, we enhance the backbone GNN and achieve improved generalization. GDSG also reaches nearly 100\% task orthogonality, ensuring no interference between the discrete and continuous generation tasks. We further reveal that this orthogonality arises from the diffusion-related training loss, rather than the neural network architecture itself. The experiments demonstrate that GDSG surpasses other benchmark methods on both the optimal and suboptimal training datasets. The MSCO datasets has open-sourced at this http URL, as well as the GDSG algorithm codes at https://github.com/qiyu3816/GDSG. |
| title | GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2412.08296 |