Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Approaches for Improving Bloom Filter-Based Set Membership Query

  • Lee, HyunYong (Energy System Research Section, Honam Research Center, Electronics Telecommunications Research Institute (ETRI)) ;
  • Lee, Byung-Tak (Energy System Research Section, Honam Research Center, Electronics Telecommunications Research Institute (ETRI))
  • Received : 2017.11.29
  • Accepted : 2018.03.15
  • Published : 2019.06.30
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Abstract

We propose approaches for improving Bloom filter in terms of false positive probability and membership query speed. To reduce the false positive probability, we propose special type of additional Bloom filters that are used to handle false positives caused by the original Bloom filter. Implementing the proposed approach for a routing table lookup, we show that our approach reduces the routing table lookup time by up to 28% compared to the original Bloom filter by handling most false positives within the fast memory. We also introduce an approach for improving the membership query speed. Taking the hash table-like approach while storing only values, the proposed approach shows much faster membership query speed than the original Bloom filter (e.g., 34 times faster with 10 subsets). Even compared to a hash table, our approach reduces the routing table lookup time by up to 58%.

Keywords

E1JBB0_2019_v15n3_550_f0001.png 이미지

Fig. 2. BF-based routing table lookup for L2 switching.

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Fig. 3. A high-level illustration of the approach using nBFs.

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Fig. 4. The condition to outperform BFs only case.

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Fig. 5. A high-level illustration of the approach using sBF.

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Fig. 6. False positive rate w.r.t. the storage space (600 K elements, 10 subsets, and 10% NACTIVE).

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Fig. 7. False positive rate w.r.t. the number of subsets (1 MB storage space, 600 K elements, and 10% NACTIVE ).

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Fig. 8. Bits per element for achieving the target false positive rate (1 MB storage space, 600 K elements,10 subsets, and 10% NACTIVE).

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Fig. 9. Comparison with existing approaches (nBF).

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Fig. 10. A high-level illustration of sTable.

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Fig. 11. The membership query error rate w.r.t the number of subsets (1 MB storage space, 600 Kelements, and 10% NACTIVE).

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Fig. 12. The processing time w.r.t the number of subsets (1 MB storage space, 600 K elements, and 10%NACTIVE).

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Fig. 13. The required amount of storage space for achieving the target error probability (1 MB storagespace, 600 K elements, 10 subsets, and 10% NACTIVE).

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Fig. 1. Element insertion and membership query using a BF: (a) filter initialization, (b) element insertion, and (c) membership query.

Table 1. Routing table lookup time

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table lookup time by 58% compared to the hash table. Table 2. Comparison of sTable and hash table

E1JBB0_2019_v15n3_550_t0002.png 이미지

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