Machine Learning Based Flow Entry Eviction for OpenFlow Switches

Abstract

Software Defined Networking (SDN) is fundamentally changing the way networks work, which enables programmable and flexible network management and configuration. As the de facto southbound interface of SDN, OpenFlow defines how the control plane can directly interact with the forwarding plane. In OpenFlow, flow tables play a significant role in packet forwarding. However, the capacity of flow table is limited due to power, cost, and silicon area constraints. The capacity-limited flow table cannot hold the explosive flows generated by the fine- grained granularity control mechanism used in SDN. Thus the flow table is frequently overflowed. In the case of overflow, eviction strategy which replaces existing flow entries with the new ones is critical to guarantee the efficient usage of the flow table. In this paper, we present a machine learning based eviction approach which can identify whether a flow entry is active or inactive and thus timely evict the inactive flow entries when flow table overflow occurs. Our simulations based on real network packet traces show that the proposed method can increase the usage of flow table by more than 55% and reduce the number of capacity misses by up to 80%, compared with the Least Recently Used eviction policy.