A cache architecture for counting Bloom filters: Theory and application

A cache architecture for counting Bloom filters: Theory and application

Ahmadi, M.
Wong, S.

Electrical Engineering, Mathematics and Computer Science

Microelectronics & Computer Engineering

2011-06-30

Within packet processing systems, lengthy memory accesses greatly reduce performance. To overcome this limitation, network processors utilize many different techniques, for example, utilizing multilevel memory hierarchies, special hardware architectures, and hardware threading. In this paper, we introduce a multilevel memory architecture for counting Bloom filters. Based on the probabilities of incrementing of the counters in the counting Bloom filter, a multi-level cache architecture called the cached counting Bloom filter (CCBF) is presented, where each cache level stores the items with the same counters. To test the CCBF architecture, we implement a software packet classifier that utilizes basic tuple space search using a 3-level CCBF. The results of mathematical analysis and implementation of the CCBF for packet classification show that the proposed cache architecture decreases the number of memory accesses when compared to a standard Bloom filter. Based on the mathematical analysis of CCBF, the number of accesses is decreased by at least 53%. The implementation results of the software packet classifier are at most 7.8% (3.5% in average) less than corresponding mathematical analysis results. This difference is due to some parameters in the packet classification application such as number of tuples, distribution of rules through the tuples, and utilized hashing functions.

http://resolver.tudelft.nl/uuid:31697d23-f3ab-4494-bf17-6d849bf47317

https://doi.org/10.1155/2011/475865

Hindawi Publishing Corporation

2090-0155

Journal of Electrical and Computer Engineering, 2011, Article ID 475865

Institutional Repository

journal article

Copyright © 2011 M. Ahmadi and S.Wong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.