Software-defined networking (SDN) is fundamentally changing the way networks operate, enabling programmable and flexible network management and configuration. As the de facto standard southbound interface of SDN, OpenFlow defines how the control plane interacts with the data forwarding plane. In OpenFlow, flow tables play a significant role in packet forwarding. However, the size of the flow table is limited due to power, cost, and silicon area constraints and capacity-limited tables cannot hold all of the active flows in medium-to-large-scale SDN networks. Thus, when a flow table reaches capacity, an intelligent eviction strategy, which efficiently manages the limited flow table resource, is critical. In this paper, we propose Smart Table EntRy Eviction for OpenFlow Switches (STEREOS), which uses machine learning to classify flow entries as active or inactive and forms the basis for intelligent eviction. Trace-driven simulations demonstrate that STEREOS increases flow table usage by more than 50% and reduces incorrect flow entry evictions by up to 78%, compared with the dominant Least Recently Used eviction policy. Moreover, packet-level simulations of a datacenter network demonstrate that STEREOS can greatly reduce the control overhead, increase overall network throughput by 19%, and reduce packet loss rate by 70%.

中文翻译：

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STEREOS：OpenFlow 交换机的智能表条目驱逐

软件定义网络 (SDN) 正在从根本上改变网络的运行方式，实现可编程和灵活的网络管理和配置。OpenFlow作为SDN事实上的标准南向接口，定义了控制平面与数据转发平面的交互方式。在 OpenFlow 中，流表在数据包转发中扮演着重要的角色。然而，由于功率、成本和硅面积的限制，流表的大小是有限的，容量有限的表无法容纳大中型 SDN 网络中的所有活动流。因此，当流表达到容量时，有效管理有限流表资源的智能驱逐策略至关重要。在本文中，我们提出了用于 OpenFlow 交换机 (STEREOS) 的智能表条目驱逐，它使用机器学习将流条目分类为活动或非活动，并构成智能驱逐的基础。跟踪驱动的模拟表明，与占主导地位的最近最少使用驱逐策略相比，STEREOS 将流表使用率增加了 50% 以上，并将不正确的流条目驱逐减少了多达 78%。此外，数据中心网络的数据包级模拟表明，STEREOS 可以大大降低控制开销，将整体网络吞吐量提高 19%，并将丢包率降低 70%。