On reducing false positives of a bloom filter in trie-based algorithms
Ju Hyoung Mun, Hyesook Lim
Abstract
Ju Hyoung Mun, Hyesook Lim
Abstract
Many IP address lookup approaches employ Bloom filters to obtain a high-speed search performance. Especially, the search performance of trie-based algorithms can be significantly improved by adding Bloom filters, because Bloom filters can determine whether a node exists in a trie without accessing the trie. The false positive rate of a Bloom filter must be reduced to enhance the lookup performance. One important characteristic of a trie is that all the ancestors of a node are also stored. The proposed IP lookup algorithm utilizes this characteristic in reducing the false positive rate of a Bloom filter without increasing the Bloom filter size. When a Bloom filter produces a positive result for a node of a trie, we propose to check whether the ancestors of the node are also positives. Because Bloom filters have no false negatives, the negative of the ancestor means that the positive of the node is false. Simulation results show that the false positive rate is reduced up to 67\% using the exact same amount of memory. The proposed approach can be applied to other trie-based algorithms employing Bloom filters.
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Many IP address lookup approaches employ Bloom filters to obtain a high-speed search performance. Especially, the search performance of trie-based algorithms can be significantly improved by adding Bloom filters, because Bloom filters can determine whether a node exists in a trie without accessing the trie. The false positive rate of a Bloom filter must be reduced to enhance the lookup performance. One important characteristic of a trie is that all the ancestors of a node are also stored. The proposed IP lookup algorithm utilizes this characteristic in reducing the false positive rate of a Bloom filter without increasing the Bloom filter size. When a Bloom filter produces a positive result for a node of a trie, we propose to check whether the ancestors of the node are also positives. Because Bloom filters have no false negatives, the negative of the ancestor means that the positive of the node is false. Simulation results show that the false positive rate is reduced up to 67\% using the exact same amount of memory. The proposed approach can be applied to other trie-based algorithms employing Bloom filters.
Key concepts: Bloom filter, Trie, False positive paradox, Computer science, False positive rate, Node (physics), Algorithm, Filter (signal processing)