In the management of computer network systems, the service function chaining (SFC) modules play an important role by generating efficient paths for network traffic through physical servers with virtualized network functions (VNF). To provide the highest quality of services, the SFC module should generate a valid path quickly even in various network topology situations including dynamic VNF resources, various requests, and changes of topologies. The previous supervised learning method demonstrated that the network features can be represented by graph neural networks (GNNs) for the SFC task. However, the performance was limited to only the fixed topology with labeled data. In this paper, we apply reinforcement learning methods for training models on various network topologies with unlabeled data. In the experiments, compared to the previous supervised learning method, the proposed methods demonstrated remarkable flexibility in new topologies without re-designing and re-training, while preserving a similar level of performance.

中文翻译：

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用于服务功能链的图神经网络的强化学习

在计算机网络系统的管理中，服务功能链 (SFC) 模块通过为具有虚拟化网络功能 (VNF) 的物理服务器生成网络流量的有效路径而发挥重要作用。为了提供最高质量的服务，即使在各种网络拓扑情况下，包括动态 VNF 资源、各种请求和拓扑变化，SFC 模块也应快速生成有效路径。先前的监督学习方法表明，对于 SFC 任务，网络特征可以由图神经网络 (GNN) 表示。然而，性能仅限于具有标记数据的固定拓扑。在本文中，我们应用强化学习方法在具有未标记数据的各种网络拓扑上训练模型。在实验中，