Flow Correlator: A Flow Table Cache Management Strategy

Switching, routing, and security functions are the backbone of packet processing networks. Fast and efficient processing of packets requires maintaining the state information for many transient network connections. In particular, modern stateful firewalls, security monitoring devices, and Software-Defined Networking (SDN) dataplanes require maintaining state-ful flow tables. These flow tables often grow much larger than can fit on-chip, requiring caching to maintain performance.This paper focuses on improving caching efficiency, an important architectural component of the packet processing data planes. We present a novel predictive approach (Flow Correlator) to network flow table cache management by adapting the Hashed Perceptron binary classifier to improve the reliability and performance of the data plane caching. We also discovered an iterative approach to feature selection and ranking while adapting the Hashed Perceptron mechanism to network flow table cache management.Through extensive experimentation, we demonstrate improved caching efficiency of the proposed Flow Correlator mechanism. We also rigorously validate the performance and generic applicability of our technique across real-world datasets.