Modeling the impact of pre-eviction time on utilization of SDN flow rules for adaptive timeout policies

Software-Defined Networking paradigm allows the control plane to centrally supervise the data planes for improving network manageability. It parallely enforces customizability and programmability at the switches. TCAM, an expensive, and compact memory is used in the switches to store flow rules, procured from the controller. Because of the memory constraints, the unused flow rules should be expedited in timely manner to reduce flow table occupancy and miss ratio. Simple Hard and idle timeout fields of the flow rules trigger automatic eviction. The objective of this work is model the performance of the flow rules which use dynamic timeouts in SDN flow tables. Literature reveals that eviction policies; Least Recently Used (LRU) and Adaptive Timeouts were proposed for enhancing the performance. Many of the works are targeted towards measuring the performance outcomes through simulations. In this paper, We proposed a novel idea of measuring the utilization of flow rules for adaptive timeouts through its pre-eviction time that captures the idle time since its last usage to eviction.Through mathematical modeling, we have studied the impact of pre-eviction time on utilization for adaptive hard and idle timeout policies. Our theoretical analysis illustrates that Adaptive Hard timeout policy experiences less pre-eviction time than Adaptive Idle timeout policy and yields better utilization. We have also done brief experimental simulation to demonstrate our findings practically.