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1
On Adding Bloom Filters to Longest Prefix Matching Algorithms
Author: Hyesook Lim, Kyuhee Lim, Nara Lee, and Kyong-hye Park
Publisher: IEEE TRANSACTIONS ON COMPUTERS
Presenter: Yuen-Shuo Li
Date: 2012/09/17
2Outline
Idea
Bloom Filter
Parallel Multiple-Hashing
Binary Search on Trie Levels
Performance
Conclusion
3Idea
4Bloom Filter(1/4)
used to test whether an element is a member of a set
have a strong space and time advantage
Elements can be added to the set, but not removed
FAST!
5Bloom Filter(2/4)
False positives retrieval results are possible.
But false negatives are not.
positive may be wrong!
6Bloom Filter(3/4)
a bit-vector multiple hash function
7Bloom Filter(4/4)
It is important to properly control the rate of the false positive in designing a Bloom filter.
k: hash functionsm: array sizen: element size
𝑘=𝑚
2⌈ log2𝑛⌉ln 2
8Parallel Multiple-Hashing(1/3)
parallel hashing for each prefix length
use multiple-hash functions to reduce collisions
9Parallel Multiple-Hashing(2/3)
Adding a Bloom Filter
to reduce the implementation complexity
10
reduce the implementation complexity
Parallel Multiple-Hashing(3/3)
parallel is not necessary since filter filters out the length of the input
11Binary Search on Trie Levels(1/4)
separates the binary trie, according to the level of the trie
markers are pre-computed in the internal nodes
12Binary Search on Trie Levels(2/4)
for a 6-bit input 111000
P5
not matchdone!
13Binary Search on Trie Levels(3/4)
Bloom filter filters out each input that does not have a node in the binary
trie
14Binary Search on Trie Levels(4/4)
Leaf-Pushed Trie uses leaf-pushing to make every prefix disjoint
finishes a search when a match to a prefix occurs
15Performance
search performance with and without Bloom filter for PMH
16Performance
search performance with and without Bloom filter for W-BSL
17Performance
search performance with and without Bloom filter for L-BSL
18Conclusion
Bloom filter is a simple but extremely powerful data structure that will improve the performance.
19
Thanks