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Issue No.01 - January (2010 vol.21)
pp: 20-32
Bin Xiao , Hong Kong Polytechnic University, Hong Kong
ABSTRACT
One widely used mechanism for representing membership of a set of items is the simple space-efficient randomized data structure known as Bloom filters. Yet, Bloom filters are not entirely suitable for many new network applications that support network services like the representation and querying of items that have multiple attributes as opposed to a single attribute. In this paper, we present an approach to the accurate and efficient representation and querying of multiattribute items using Bloom filters. The approach proposes three variant structures of Bloom filters: Parallel Bloom Filter (referred as PBF) structure, PBF with a hash table (PBF-HT), and PBF with a Bloom filter (PBF-BF). PBF stores multiple attributes of an item in parallel Bloom filters. The auxiliary HT and BF provide functions to capture the inherent dependency of all attributes of an item. Compared to standard Bloom filters to represent items with multiple attributes, the proposed PBF facilitates much faster query service and both PBF-HT and PBF-BF structures achieve much lower false positive probability with a result to save storage space. Simulation and experimental results demonstrate that the new space-efficient Bloom filter structures can efficiently and accurately represent multiattribute items and quickly respond queries at the cost of a relatively small false positive probability.
INDEX TERMS
Network services, parallel Bloom filters, false positives, data structure.
CITATION
Bin Xiao, "Using Parallel Bloom Filters for Multiattribute Representation on Network Services", IEEE Transactions on Parallel & Distributed Systems, vol.21, no. 1, pp. 20-32, January 2010, doi:10.1109/TPDS.2009.39
REFERENCES
[1] H. Burton, “Space/Time Trade-Offs in Hash Coding with Allowable Errors,” Comm. ACM, vol. 13, no. 7, pp. 422-426, 1970.
[2] D. Ficara, S. Giordano, G. Procissi, and F. Vitucci, “MultiLayer Compressed Counting Bloom Filters,” Proc. IEEE INFOCOM, pp.311-315, 2008.
[3] A. Broder and M. Mitzenmacher, “Network Applications of Bloom Filters: A Survey,” Internet Math., vol. 1, pp. 485-509, 2005.
[4] A. Pagh, R. Pagh, and S. Rao, “An Optimal Bloom Filter Replacement,” Proc. 16th Ann. ACM-SIAM Symp. Discrete Algorithms, pp. 823-829, 2005.
[5] M. Zhong, P. Lu, K. Shen, and J. Seiferas, “Optimizing Data Popularity Conscious Bloom Filters,” Proc. ACM Symp. Principles of Distributed Computing (PODC), 2008.
[6] F. Hao, M. Kodialam, and T. Lakshman, “Building High Accuracy Bloom Filters Using Partitioned Hashing,” Proc. ACM SIGMETRICS, pp.277-288, 2007.
[7] F. Hao, M. Kodialam, and T.V. Lakshman, “Incremental Bloom Filters,” Proc. IEEE INFOCOM, pp. 1741-1749, 2008.
[8] B. Donnet, B. Baynat, and T. Friedman, “Retouched Bloom Filters: Allowing Networked Applications to Trade Off Selected False Positives Against False Negatives,” Proc. Int'l Conf. Emerging Networking Experiments and Technologies (CoNEXT), 2006.
[9] D. Guo, J. Wu, H. Chen, and X. Luo, “Theory and Network Application of Dynamic Bloom Filters,” Proc. IEEE INFOCOM, 2006.
[10] Y. Hua and B. Xiao, “A Multi-Attribute Data Structure with Parallel Bloom Filters for Network Services,” Proc. IEEE Int'l Conf. High Performance Computing (HiPC), pp. 277-288, Dec. 2006.
[11] E. Riedel, M. Kallahalla, and R. Swaminathan, “A Framework for Evaluating Storage System Security,” Proc. Conf. File and Storage Technologies (FAST), pp. 15-30, 2002.
[12] L. Fan, P. Cao, J. Almeida, and A. Broder, “Summary Cache: A Scalable Wide Area Web Cache Sharing Protocol,” IEEE/ACM Trans. Networking, vol. 8, no. 3, pp. 281-293, June 2000.
[13] A.J. Menezes, P.C. van Oorschot, and S.A. Vanstone, Handbook of Applied Cryptography. CRC Press, 1997.
[14] S.C. Rhea and J. Kubiatowicz, “Probabilistic Location and Routing,” Proc. IEEE INFOCOM, 2002.
[15] A. Barron, “Entropy and the Central Limit Theorem,” The Annals of Probability, vol. 14, no. 1, pp. 336-342, 1986.
[16] W. chang Feng, D.D. Kandlur, D. Saha, and K.G. Shin, “Stochastic Fair Blue: A Queue Management Algorithm for Enforcing Fairness,” Proc. IEEE INFOCOM, 2001.
[17] F.M. Cuenca-Acuna, C. Peery, R.P. Martin, and T.D. Nguyen, “PlantP: Using Gossiping to Build Content Addressable Peer-to-Peer Information Sharing Communities,” Proc. Conf. High Performance Distributed Computing (HPDC), 2003.
[18] S. Dharmapurikar, P. Krishnamurthy, and D.E. Taylor, “Longest Prefix Matching Using Bloom Filters,” Proc. ACM SIGCOMM, 2003.
[19] A. Broder and M. Mitzenmacher, “Using Multiple Hash Functions to Improve IP Lookups,” Proc. IEEE INFOCOM, 2001.
[20] F. Baboescu and G. Varghese, “Scalable Packet Classification,” Proc. ACM SIGCOMM, 2001.
[21] B. Xiao, W. Chen, and Y. He, “A Novel Technique for Detecting DDoS Attacks at the Early Stage,” J. Supercomputing, vol. 36, pp.235-248, 2006.
[22] M. Mitzenmacher, “Compressed Bloom Filters,” IEEE/ACM Trans. Networking, vol. 10, no. 5, pp. 604-612, Oct. 2002.
[23] Y. Zhu, H. Jiang, J. Wang, and F. Xian, “HBA: Distributed Metadata Management for Large Cluster-Based Storage Systems,” IEEE Trans. Parallel and Distributed Systems, vol. 19, no. 6, pp. 750-763, June 2008.
[24] A. Kumar, J. Xu, and E.W. Zegura, “Efficient and Scalable Query Routing for Unstructured Peer-to-Peer Networks,” Proc. IEEE INFOCOM, 2005.
[25] C. Saar and M. Yossi, “Spectral Bloom Filters,” Proc. ACM SIGMOD, 2003.
[26] Y. Hua, Y. Zhu, H. Jiang, D. Feng, and L. Tian, “Scalable and Adaptive Metadata Management in Ultra Large-Scale File Systems,” Proc. Int'l Conf. Distributed Computing Systems (ICDCS), pp. 403-410, 2008.
[27] F. Deng and D. Rafiei, “Approximately Detecting Duplicates for Streaming Data Using Stable Bloom Filters,” Proc. ACM SIGMOD, 2006.
[28] F. Liu and G. Heijenk, “Context Discovery Using Attenuated Bloom Filters in Ad-Hoc Networks,” J. Internet Eng., vol. 1, no. 1, pp. 49-58, 2007.
[29] F. Bonomi, M. Mitzenmacher, R. Panigrah, S. Singh, and G. Varghese, “Beyond Bloom Filters: From Approximate Membership Checks to Approximate State Machines,” Proc. ACM SIGCOMM, 2006.
[30] H. Song, S. Dharmapurikar, J. Turner, and J. Lockwood, “Fast Hash Table Lookup Using Extended Bloom Filter: An Aid to Network Processing,” Proc. ACM SIGCOMM, 2005.
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