Advertisement

A Multi-attribute Data Structure with Parallel Bloom Filters for Network Services

  • Yu Hua
  • Bin Xiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4297)

Abstract

A Bloom filter has been widely utilized to represent a set of items because it is a simple space-efficient randomized data structure. In this paper, we propose a new structure to support the representation of items with multiple attributes based on Bloom filters. The structure is composed of Parallel Bloom Filters (PBF) and a hash table to support the accurate and efficient representation and query of items. The PBF is a counter-based matrix and consists of multiple submatrixes. Each submatrix can store one attribute of an item. The hash table as an auxiliary structure captures a verification value of an item, which can reflect the inherent dependency of all attributes for the item. Because the correct query of an item with multiple attributes becomes complicated, we use a two-step verification process to ensure the presence of a particular item to reduce false positive probability.

Keywords

Hash Function Multiple Attribute Hash Table Intersection Operation Bloom Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bloom, B.: Space/time Trade-offs in Hash Coding with Allowable Errors. Communications of the ACM 13, 422–426 (1970)MATHCrossRefGoogle Scholar
  2. 2.
    Fan, L., Cao, P., Almeida, J., Broder, Z.A.: Summary cache: a scalable wide area web cache sharing protocol. IEEE/ACM Transaction on Networking 8, 281–293 (2000)CrossRefGoogle Scholar
  3. 3.
    Mitzenmacher, M.: Compressed Bloom filters. IEEE/ACM Transaction on Networking 10, 604–612 (2002)CrossRefGoogle Scholar
  4. 4.
    Zhu, Y.F., Jiang, H., Wang, J.: Hierarchical Bloom Filter Arrays (HBA): A Novel, Scalable Metadata Management System for Large Cluster-based Storage. In: Proceedings of the 5th IEEE International Conference on Cluster Computing (Cluster), pp. 165–174 (2004)Google Scholar
  5. 5.
    Kumar, A., Xu, J., Wang, J., Spatschek, O., Li, L.: Space-Code Bloom filter for efficient per-flow traffic measurement. In: Proceedings of the IEEE INFOCOM, vol. 3, pp. 1762–1773 (2004)Google Scholar
  6. 6.
    Saar, C., Yossi, M.: Spectral Bloom filters. In: Proceedings of the ACM SIGMOD, pp. 241–252 (2003)Google Scholar
  7. 7.
    Broder, A., Mitzenmacher, M.: Network applications of Bloom filters: a survey. Internet Mathematics 1, 485–509 (2005)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Xiao, B., Chen, W., He, Y.X., Sha, E.H.M.: An active detecting method against SYN flooding attack. In: Proceedings of the 11th International Conference on Parallel and Distributed Systems (ICPADS), vol. 1, pp. 709–715 (2005)Google Scholar
  9. 9.
    Feng, W.C., Kandlur, D.D., Saha, D., Shin, K.G.: Stochastic Fair Blue: A Queue Management Algorithm for Enforcing Fairness. In: Proceedings of the IEEE INFOCOM, vol. 3, pp. 1520–1529 (2001)Google Scholar
  10. 10.
    Cuenca-Acuna, F.M., Peery, C., Martin, R.P., Nguyen, T.D.: PlantP:Using gossiping to build content addressable peer-to-peer information sharing communities. In: Proceedings of the 12th IEEE High Performance Distributed Computing, pp. 236–246 (2003)Google Scholar
  11. 11.
    Broder, A., Mitzenmacher, M.: Using multiple hash functions to improve IP lookups. In: Proceedings of the IEEE INFOCOM, vol. 3, pp. 1454–1463 (2001)Google Scholar
  12. 12.
    Baboescu, F., Varghese, G.: Scalable packet classification. In: Proceedings of the ACM SIGCOMM, pp. 199–210 (2001)Google Scholar
  13. 13.
    Dharmapurikar, S., Krishnamurthy, P., Taylor, D.E.: Longest Prefix Matching Using Bloom Filters. In: Proceedings of the ACM SIGCOMM, pp. 201–212 (2003)Google Scholar
  14. 14.
    Kumar, A., Xu, J., Zegura, E.W.: Efficient and scalable query routing for unstructured peer-to-peer networks. In: Proceedings of the IEEE INFOCOM, vol. 2, pp. 1162–1173 (2005)Google Scholar
  15. 15.
    Song, H.Y., Dharmapurikar, S., Turner, J., Lockwood, J.: Fast Hash Table Lookup Using Extended Bloom Filter: An Aid to Network Processing. In: Proceedings of the ACM SIGCOMM, pp. 181–192 (2005)Google Scholar
  16. 16.
    Guo, D.K., Wu, J., Chen, H.H., Luo, X.J.: Theory and Network Application of Dynamic Bloom Filters. In: Proceedings of the IEEE INFOCOM (2006)Google Scholar
  17. 17.
    Rhea, S.C., Kubiatowicz, J.: Probabilistic location and routing. In: Proceedings of the IEEE INFOCOM, vol. 3, pp. 1248–1257 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yu Hua
    • 1
    • 2
  • Bin Xiao
    • 1
  1. 1.Department of ComputingHong Kong Polytechnic UniversityKowloonHong Kong
  2. 2.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

Personalised recommendations