CuCoTrack: Cuckoo Filter Based Connection Tracking

TL;DR

CuCoTrack is a memory-efficient, cuckoo hash-based connection tracking scheme that uses dynamic fingerprints to reduce memory usage and ensure fast lookups, suitable for hardware implementation.

Contribution

It introduces a novel connection tracking data structure leveraging cuckoo hashing and dynamic fingerprints for improved efficiency and collision avoidance.

Findings

16-bit fingerprints effectively prevent collisions in practical scenarios.

CuCoTrack reduces memory and bandwidth requirements for connection tracking.

The scheme is validated through theoretical analysis and simulation.

Abstract

This paper introduces CuCoTrack, a cuckoo hash based data structure designed to efficiently implement connection tracking. The proposed scheme exploits the fact that queries always match one existing connection to compress the 5-tuple that identifies the connection. This reduces significantly the amount of memory needed to store the connections and also the memory bandwidth needed for lookups. CuCoTrack uses a dynamic fingerprint to avoid collisions thus ensuring that queries are completed in at most two memory accesses and facilitating a hardware implementation. The proposed scheme has been analyzed theoretically and validated by simulation. The results show that using 16 bits for the fingerprint is enough to avoid collisions in practical configurations.