使用强化学习的SDN流条目管理
SDN Flow Entry Management Using Reinforcement Learning
摘要：
现代信息技术服务在很大程度上依赖于云基础设施来提供服务。这些云基础设施构建在数据中心网络（DCN）之上，这些网络采用高速链路、快速交换设备和冗余来构建，以提供更好的灵活性和弹性。在这种环境中，网络流量包括具有分区和聚合流量模式的长期（大象）和短期（老鼠）流。虽然基于SDN的方法可以有效地为这些流分配网络资源，但由于网络重新配置而导致的开销可能很大。在启用OpenFlow的交换机中部署的三进制内容可寻址存储器（TCAM）的容量有限的情况下，确定哪些转发规则应该保留在流表中以及在SDN交换机上的表未命中的情况下哪些规则应该由SDN控制器处理是至关重要的。为了获得满足减少控制器和交换机之间引入的长期控制平面开销的目标的流条目，需要这样做。为了实现这一目标，我们提出了一种机器学习技术，利用两种变化的强化学习（RL）算法，其中第一种是传统的强化学习算法为基础，而另一种是深度强化学习为基础。使用RL算法的仿真结果表明，在减少长期控制平面开销方面有大约60%的改进，并且与给定4KB的固定大小流表的多布隆过滤器（MBF）方法相比，表命中率有大约14%的改进。
英文摘要：
Modern information technology services largely depend on cloud infrastructures to provide their services. These cloud infrastructures are built on top of datacenter networks (DCNs) constructed with high-speed links, fast switching gear, and redundancy to offer better flexibility and resiliency. In this environment, network traffic includes long-lived (elephant) and short-lived (mice) flows with partitioned and aggregated traffic patterns. Although SDN-based approaches can efficiently allocate networking resources for such flows, the overhead due to network reconfiguration can be significant. With limited capacity of Ternary Content-Addressable Memory (TCAM) deployed in an OpenFlow enabled switch, it is crucial to determine which forwarding rules should remain in the flow table, and which rules should be processed by the SDN controller in case of a table-miss on the SDN switch. This is needed in order to obtain the flow entries that satisfy the goal of reducing the long-term control plane overhead introduced between the controller and the switches. To achieve this goal, we propose a machine learning technique that utilizes two variations of reinforcement learning (RL) algorithms-the first of which is traditional reinforcement learning algorithm based while the other is deep reinforcement learning based. Emulation results using the RL algorithm show around 60% improvement in reducing the long-term control plane overhead, and around 14% improvement in the table-hit ratio compared to the Multiple Bloom Filters (MBF) method given a fixed size flow table of 4KB.
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