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 http://ieee-dataport.org/13824, as well as the GDSG algorithm codes at https://github.com/qiyu3816/GDSG.

翻译：优化对于移动边缘计算（MEC）网络高效可靠运行至关重要，其中大多数问题属于NP难问题，且缺乏高效的近似算法。这导致最优解的匮乏，从而限制了传统深度学习方法的有效性。现有大多数基于学习的方法需要大量最优数据，且未能充分利用能以更高效率和效果获取的次优数据的潜在优势。本文以多服务器多用户计算卸载（MSCO）问题——该问题在车联网（IoV）和无人机（UAV）网络等系统中广泛存在——为具体场景，提出一种基于图扩散的解生成（GDSG）方法。该方法旨在利用次优数据集工作，同时大概率收敛至最优解。我们将优化问题转化为分布学习问题，并对从次优训练数据集学习的过程给出了清晰解释。我们将GDSG构建为一个多任务扩散模型，利用图神经网络（GNN）学习高质量解的分布。我们采用一种简单高效的启发式方法获取完全由次优解构成的足量训练数据。在实现中，我们增强了骨干GNN并获得了更好的泛化性能。GDSG还实现了接近100%的任务正交性，确保了离散与连续生成任务之间无干扰。我们进一步揭示了这种正交性源于扩散相关的训练损失，而非神经网络架构本身。实验表明，无论在最优还是次优训练数据集上，GDSG均优于其他基准方法。MSCO数据集已在 http://ieee-dataport.org/13824 开源，GDSG算法代码发布于 https://github.com/qiyu3816/GDSG。