Robust SDN Synchronization in Mobile Networks Using Deep Reinforcement and Transfer Learning
Robust SDN Synchronization in Mobile Networks Using Deep Reinforcement and Transfer Learning
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DOI：
10.1109/icc45041.2023.10278580
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发表时间：
2023-05
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通讯作者：
Akrit Mudvari;Konstantinos Poularakis;L. Tassiulas
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作者：
Akrit Mudvari;Konstantinos Poularakis;L. Tassiulas
用于SDN部署的逻辑集中式控制器架构是一种很好理解和实现的方法，然而，由于可扩展性、隐私等问题，需要开发和实现一种健壮的物理分布式SDN控制器架构。在分布式SDN环境中，需要维护集中式逻辑网络视图，这意味着分布式控制器需要通过同步来保持关于其他控制器的网络的通知的鲁棒方法。这在控制器和网络环境不断变化的移动的无线网络中尤其如此，因此为此，我们开发了一种基于深度强化和迁移学习的方法，该方法为控制器提供了一种有效的策略，用于与其他控制器同步并在此类网络中保持逻辑上集中的视图。我们表明，我们以应用程序为中心的方法在不同类型的应用程序中表现良好，包括最短路径路由和负载平衡，优于基于强化学习的方法以及循环方法。
A logically centralized controller architecture for SDN deployments is a well understood and implemented method, however because of issues such as scalability, privacy and more, there is a need to develop and implement a robust physically distributed SDN controller architecture. In a distributed SDN environment, a centralized logical network view needs to be maintained, which means the distributed controllers need a robust method of remaining informed about other controller's network through synchronization. This is specially true in mobile, wireless networks with changing controller and network environment, so to this end we develop a deep reinforcement and transfer learning based method that provides the controllers with an efficient policy for synchronizing with other controllers and maintaining a logically centralized view in such networks. We show that our application-centric method performs well for different kinds of applications including shortest path routing and load balancing, outperforming a reinforcement learning based method as well as a round robin method.