Summary cache: a scalable wide-area web cache sharing protocol
IEEE/ACM Transactions on Networking (TON)
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Fast hash table lookup using extended bloom filter: an aid to network processing
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
An improved construction for counting bloom filters
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
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The Bloom Filter (BF), a space-and-time-efficient hash-coding method, is used as one of the fundamental modules in several network processing algorithms and applications such as route lookups, cache hits, packet classification, per-flow state management or network monitoring. BF is a simple space-efficient randomized data structure used to represent a data set in order to support membership queries. However, BF generates false positives, and cannot count the number of distinct elements. A counting Bloom Filter (CBF) can count the number of distinct elements, but CBF needs more space than BF. We propose an alternative data structure of CBF, and we called this structure an Adaptive Bloom Filter (ABF). Although ABF uses the same-sized bit-vector used in BF, the number of hash functions employed by ABF is dynamically changed to record the number of appearances of a each key element. Considering the hash collisions, the multiplicity of a each key element on ABF can be estimated from the number of hash functions used to decode the membership of the each key element. Although ABF can realize the same functionality as CBF, ABF requires the same memory size as BF. We describe the construction of ABF and IABF (Improved ABF), and provide a mathematical analysis and simulation using Zipf's distribution. Finally, we show that ABF can be used for an unpredictable data set such as real network traffic.