[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3745/JIPS.04.0116

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))

Publication Information

Journal of Information Processing Systems / v.15, no.3, 2019 , pp. 550-569 More about this Journal

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

Additional Filters; Bloom Filter; False Positive Probability; Hash Table; Processing Time;

Citations & Related Records

Times Cited By KSCI : 1 (Citation Analysis)

Reference
Cited By KSCI

1	S. Dharmapurikar, P. Krishnamurthy, and D. E. Taylor, "Longest prefix matching using bloom filters," IEEE/ACM Transactions on Networking (TON), vol. 14, no. 2, pp. 397-409, 2006. DOI
2	M. Yu, A. Fabrikant, and J. Rexford, "BUFFALO: bloom filter forwarding architecture for large organizations," in Proceedings of the 5th International Conference on Emerging Networking Experiments and Technologies, Rome, Italy, 2009, pp. 313-324.
3	H. Wu, D. Yang, and G. Zhang, "SybilBF: defending against Sybil attacks via Bloom filters," ETRI Journal, vol. 33, no. 5, pp. 826-829, 2011. DOI
4	J. Byers, J. Considine, M. Mitzenmacher, and S. Rost, "Informed content delivery across adaptive overlay networks," ACM SIGCOMM Computer Communication Review, vol. 32, no. 4, pp. 47-60, 2002. DOI
5	L. Fan, P. Cao, J. Almeida, and A. Z. Broder, "Summary cache: a scalable wide-area web cache sharing protocol," IEEE/ACM Transactions on Networking (TON), vol. 8, no. 3, pp. 281-293, 2000. DOI
6	B. H. Bloom, "Space/time trade-offs in hash coding with allowable errors," Communications of the ACM, vol. 13, no. 7, pp. 422-426, 1970. DOI
7	T. Yang, A. X. Liu, M. Shahzad, Y. Zhong, Q. Fu, Z. Li, G. Xie, and X. Li, "A shifting bloom filter framework for set queries," Proceedings of the VLDB Endowment, vol. 9, no. 5, pp. 408-419, 2016. DOI
8	Y. Liu, X. Ge, D. H. C. Du, and X. Huang, "Par-BF: a parallel partitioned Bloom filter for dynamic data sets," The International Journal of High Performance Computing Applications, vol. 30, no. 3, pp. 259-275, 2016. DOI
9	S. Tarkoma, C. E. Rothenberg, and E. Lagerspetz, "Theory and practice of bloom filters for distributed systems," IEEE Communications Surveys & Tutorials, vol. 14, no. 1, pp. 131-155, 2011.
10	M. Zhong, P. Lu, K. Shen, and J. Seiferas, "Optimizing data popularity conscious bloom filters," in Proceedings of the 27th ACM Symposium on Principles of Distributed Computing, Toronto, Canada, 2008, pp. 355-364.
11	J. Bruck, J. Gao, and A. Jiang, "Weighted bloom filter," in Proceedings of 2006 IEEE International Symposium on Information Theory, Seattle, WA, 2006, pp. 2304-2308.
12	Click modular router, https://github.com/kohler/click.
13	D. Wagner, "A generalized birthday problem," in Advances in Cryptology-CRYPTO 2002. Heidelberg: Springer, 2002, pp. 288-304.
14	B. Fan, D. G. Andersen, M. Kaminsky, and M. D. Mitzenmacher, "Cuckoo filter: Practically better than bloom," in Proceedings of the 10th ACM International on Conference on emerging Networking Experiments and Technologies, Sydney, Australia, 2014, pp. 75-88.
15	A. Crainiceanu, "Bloofi: a hierarchical Bloom filter index with applications to distributed data provenance," in Proceedings of the 2nd International Workshop on Cloud Intelligence, Trento, Italy, 2013.
16	J. Lu, T. Yang, Y. Wang, H. Dai, L. Jin, H. Song, and B. Liu, "One-hashing bloom filter," in Proceedings of 2015 IEEE 23rd International Symposium on Quality of Service (IWQoS), Portland, OR, 2015, pp. 289-298.
17	B. Donnet, B. Baynat, and T. Friedman, "Retouched bloom filters: allowing networked applications to trade off selected false positives against false negatives," in Proceedings of the 2006 ACM CoNEXT Conference, Lisbon, Portugal, 2006.
18	C. Kim, M. Caesar, A. Gerber, and J. Rexford, "Revisiting route caching: the world should be flat," in Passive and Active Network Measurement. Heidelberg: Springer, 2009, pp. 3-12.
19	COIN-OR Interior Point Optimizer (IPOPT) [Online]. Available: https://github.com/coin-or/Ipopt.