Network softwarization has recently been enabled via the software-defined networking (SDN) paradigm, which separates the data plane from control plane allowing for a flexible and centralized control of networks. This separation facilitates implementation of machine learning techniques for network management and optimization. In this work, a machine learning-based multipath routing (MLMR) framework is proposed for software-defined networks with quality-of-service (QoS) constraints and flow rules space constraints. The QoS-aware multipath routing problem in SDN is modeled as multicommodity network flow problem with side constraints, that is known to be NP-hard. The proposed framework utilizes network status estimates, and their corresponding routing configurations available at the network central controller to learn a mapping function between them. Once the mapping function is learned, it is applied on live-inputs of network status and routing requests to predict a multipath routing solutions in real-time. Performance evaluations of the MLMR framework on real traces of network traffic verify its accuracy and resilience to noise in training data. Furthermore, the MLMR framework demonstrates more than 98.99% improvement in computational efficiency.

中文翻译：

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用于软件定义网络的基于机器学习的多路径路由

最近通过软件定义网络 (SDN) 范式启用了网络软件化，该范式将数据平面与控制平面分开，从而实现了对网络的灵活和集中控制。这种分离有助于实现用于网络管理和优化的机器学习技术。在这项工作中，为具有服务质量 (QoS) 约束和流规则空间约束的软件定义网络提出了一种基于机器学习的多路径路由 (MLMR) 框架。SDN 中的 QoS 感知多路径路由问题被建模为具有边约束的多商品网络流问题，这被称为 NP-hard。所提出的框架利用网络状态估计及其在网络中央控制器处可用的相应路由配置来学习它们之间的映射函数。一旦学习了映射函数，它就会应用于网络状态和路由请求的实时输入，以实时预测多路径路由解决方案。MLMR 框架在网络流量的真实轨迹上的性能评估验证了其对训练数据中噪声的准确性和弹性。此外，MLMR 框架的计算效率提高了 98.99% 以上。