标题： 用于集成机器学习方法的路径感知源路由框架 显示英文标题

标题： Framework for Integrating Machine Learning Methods for Path-Aware Source Routing

摘要： 自软件定义网络（SDN）出现以来，流量工程（TE）被突出显示为可以通过软件控制协议（例如PCEP和MPLS）实现的关键应用之一。作为网络中最具挑战性的问题之一，TE问题涉及诸如分配流的困难决策，要么通过将它们分布在多个路径上，要么使用预留系统，以最小化拥塞。然而，创建优化的解决方案很麻烦且困难，因为流量模式随着网络规模、容量和需求的变化而变化。AI方法可以通过找到最佳网络性能的优化TE解决方案来缓解这个问题。基于SDN的TE工具，如Teal、Hecate等，使用分类技术或深度强化学习来找到最优的网络TE解决方案，并在仿真中得到展示。通过源路由工具进行的路由控制，例如PolKA，可以帮助动态地重新定向网络流。在本文中，我们提出了一种新的框架，利用Hecate在真实网络上实际演示TE，并与源路由工具PolKA协作。通过实时流量统计，Hecate使用这些数据计算最优路径，然后将这些路径传达给PolKA以分配流。本文做出了若干贡献，以展示如何使用模拟生态系统来测试该框架，该生态系统模仿真实的P4测试床场景。这项工作对于真正工程化的自动驾驶网络具有重要价值，有助于将理论转化为实践。 显示英文摘要

摘要： Since the advent of software-defined networking (SDN), Traffic Engineering (TE) has been highlighted as one of the key applications that can be achieved through software-controlled protocols (e.g. PCEP and MPLS). Being one of the most complex challenges in networking, TE problems involve difficult decisions such as allocating flows, either via splitting them among multiple paths or by using a reservation system, to minimize congestion. However, creating an optimized solution is cumbersome and difficult as traffic patterns vary and change with network scale, capacity, and demand. AI methods can help alleviate this by finding optimized TE solutions for the best network performance. SDN-based TE tools such as Teal, Hecate and more, use classification techniques or deep reinforcement learning to find optimal network TE solutions that are demonstrated in simulation. Routing control conducted via source routing tools, e.g., PolKA, can help dynamically divert network flows. In this paper, we propose a novel framework that leverages Hecate to practically demonstrate TE on a real network, collaborating with PolKA, a source routing tool. With real-time traffic statistics, Hecate uses this data to compute optimal paths that are then communicated to PolKA to allocate flows. Several contributions are made to show a practical implementation of how this framework is tested using an emulated ecosystem mimicking a real P4 testbed scenario. This work proves valuable for truly engineered self-driving networks helping translate theory to practice.