A Memory-optimized Bloom Filter using An Additional Hashing Function Author: Mahmood Ahmadi, Stephan Wong Publisher: IEEE GLOBECOM 2008 Presenter: Yu-Ping.

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Presentation transcript:

A Memory-optimized Bloom Filter using An Additional Hashing Function Author: Mahmood Ahmadi, Stephan Wong Publisher: IEEE GLOBECOM 2008 Presenter: Yu-Ping Chiang Date: 2009/04/29

Outline Related work  Regular bloom filter  Pruned bloom filter BFAH (bloom filter with an additional hashing function) Performance

Regular bloom filter Address Bit-array R0 R2 R3 R1 H1(R1) H2(R1) H3(R1) R0 R1 R0 R1 R2 R3 R2 R3 R0

Regular bloom filter - search Address Bit-array Input: X H1(X) H3(X) R0 R1 R0 R1 R2 R3 R2 R3 H2(X) R0 NO match any rule !! Disadvantages: ‧ Can’t delete rule ‧ Duplicate rules in memory

Pruned bloom filter Bit-array Address R0 R2 R3 R1 R0 R1 R2 R Counter ‧ After set bit-array for all rules, save rule only in smallest counter position.

Outline Related work  Regular bloom filter  Pruned bloom filter BFAH (bloom filter with an additional hashing function) Performance

BFAH Determine which place will use to insert item. Address Bit-array R0 R2 R3 R1 H1(R1) H2(R1) H3(R1) Additional hash function

BFAH - example Address Bit-array R0 H1(R1) H2(R1) H3(R1) Additional hash function rule_num mod 3 Input : 0 Output : 0 R0

BFAH - example Address Bit-array R0 R1 Additional hash function rule_num mod 3 Input : 1 Output : 1 R0 R1

BFAH - example Address Bit-array R0 R2 R3 R1 Additional hash function rule_num mod 3 R0 R1 R3 R2

Outline Related work  Regular bloom filter  Pruned bloom filter BFAH (bloom filter with an additional hashing function) Performance

R.B : Regular Bloom filter P.C.B : Pruned Counting Bloom filter M.B : BFAH k = # of hash functions m = size of bit array n = # of items (rules)

Performance R.B : Regular Bloom filter P.C.B : Pruned Counting Bloom filter M.B : BFAH k = # of hash functions m = size of bit array n = # of items (rules)

Performance Average number of collisions for all rule-set.