Adaptive Cuckoo Filters

TL;DR

The paper introduces the adaptive cuckoo filter (ACF), a data structure that dynamically reacts to false positives to reduce their rate, improving approximate set membership queries especially in packet processing scenarios.

Contribution

It presents the adaptive cuckoo filter (ACF), a novel extension of cuckoo filters that adaptively removes false positives to enhance accuracy.

Findings

ACF significantly reduces false positive rates in simulations.

Theoretical model confirms the effectiveness of ACF in lowering false positives.

Experimental results on real packet traces demonstrate practical benefits.

Abstract

We introduce the adaptive cuckoo filter (ACF), a data structure for approximate set membership that extends cuckoo filters by reacting to false positives, removing them for future queries. As an example application, in packet processing queries may correspond to flow identifiers, so a search for an element is likely to be followed by repeated searches for that element. Removing false positives can therefore significantly lower the false positive rate. The ACF, like the cuckoo filter, uses a cuckoo hash table to store fingerprints. We allow fingerprint entries to be changed in response to a false positive in a manner designed to minimize the effect on the performance of the filter. We show that the ACF is able to significantly reduce the false positive rate by presenting both a theoretical model for the false positive rate and simulations using both synthetic data sets and real packet traces.