Traffic Optimization (TO) is a well-known and established topic in datacenters with the fundamental goal of operating networks efficiently. Traditional TO heuristics may suffer from performance penalty as it mismatches actual traffic, while Artificial Intelligence (AI) which has undergone a renaissance recently is gradually being applied to the network optimization and has shown excellent advantages. However, the current AI technologies (e.g., DGN, DRL, DBA, etc.) have difficulty in adapting to the dynamic and variable characteristics of the network due to their lack of generalization ability, which limits the development of intelligent networks. As Graph Neural Network (GNN) can support relational reasoning and combinatorial generalization, we research how to model and optimize traffic in datacenters with GNN in this paper. First, we proposed a GNN model for reasoning Flow Completion Time (FCT), which is able to provide accurate estimation of never-seen network states. Then we designed a GNN-based optimizer for TO, which can be used to in flow routing, flow scheduling and topology management. Finally, the experimental results verify that the GNN model has a high inference accuracy, and the GNN-based optimizer can significantly reduce the average / p10 (the 10th percentile) FCT. Therefore, GNN has a great potential in network modeling and optimization, and has a wide range of applications.

流量优化（TO）是数据中心中众所周知的既定主题，其基本目标是有效地运营网络。传统的TO启发式算法可能会因与实际流量不匹配而遭受性能损失，而最近经历了复兴的AI（AI）逐渐被应用到网络优化中，并显示出卓越的优势。然而，由于缺乏通用化能力，当前的AI技术（例如DGN，DRL，DBA等）难以适应网络的动态和可变特性，这限制了智能网络的发展。由于图神经网络（GNN）可以支持关系推理和组合泛化，因此本文研究了如何使用GNN在数据中心中建模和优化流量。第一，我们提出了一种用于推理流完成时间（FCT）的GNN模型，该模型能够提供对从未见过的网络状态的准确估计。然后，我们为TO设计了基于GNN的优化器，该优化器可用于流路由，流调度和拓扑管理。最后，实验结果验证了GNN模型具有较高的推理准确性，并且基于GNN的优化程序可以显着降低平均值/ p10（第10个百分位数）FCT。因此，GNN在网络建模和优化方面具有巨大的潜力，并具有广泛的应用范围。实验结果证明，GNN模型具有较高的推理精度，并且基于GNN的优化器可以显着降低平均值/ p10（第10个百分位数）FCT。因此，GNN在网络建模和优化方面具有巨大的潜力，并具有广泛的应用范围。实验结果证明，GNN模型具有较高的推理精度，并且基于GNN的优化器可以显着降低平均值/ p10（第10个百分点）FCT。因此，GNN在网络建模和优化方面具有巨大的潜力，并具有广泛的应用范围。