Non-Sticky Fingers: Policy-Driven Self-optimization for DHTs

  • Matti Siekkinen
  • Vera Goebel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5918)


It is a common situation with distributed hash tables (DHT) that insertions and lookups frequently target only specific fractions of the entire value range. We present in this paper a self-optimization scheme for DHTs that optimizes the routing behavior in such situations. In our scheme, called Non-Sticky (NS) fingers, each node continuously measures the routing behavior and guides neighboring nodes to adjust their NS fingers (a subset of all the long distance links that the node establishes) accordingly in order to shortcut the most popular sections of routes. Our scheme enables self-optimization, which means that it adapts to the current system state and only operates when advantageous. It is also policy-driven, which means that the application can specify its policy on the tradeoff between performance and cost efficiency. We implemented the NS-fingers scheme for an existing order-preserving DHT and report the evaluation results. Our simulation results show that in a realistic application scenario, NS-fingers can halve the number of routing hops.


Video Streaming Range Query Distribute Hash Table Service Demand Distance Link 
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.


  1. 1.
    Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., Balakrishnan, H.: Chord: A scalable peer-to-peer lookup service for internet applications. In: Proceedings of SIGCOMM 2001, pp. 149–160 (2001)Google Scholar
  2. 2.
    Rowstron, A.I.T., Druschel, P.: Pastry: Scalable, decentralized object location, and routing for large-scale peer-to-peer systems. In: Guerraoui, R. (ed.) Middleware 2001. LNCS, vol. 2218, pp. 329–350. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Ratnasamy, S., Francis, P., Handley, M., Karp, R., Schenker, S.: A scalable content-addressable network. In: Proceedings of SIGCOMM 2001, pp. 161–172 (2001)Google Scholar
  4. 4.
    Bharambe, A.R., Agrawal, M., Seshan, S.: Mercury: supporting scalable multi-attribute range queries. In: Proceedings of SIGCOMM 2004, pp. 353–366 (2004)Google Scholar
  5. 5.
    Klemm, F., Girdzijauskas, S., Boudec, J.Y.L., Aberer, K.: On routing in distributed hash tables. In: P2P 2007: Proceedings of the Seventh IEEE International Conference on Peer-to-Peer Computing, pp. 113–122. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  6. 6.
    Dabek, F., Cox, R., Kaashoek, F., Morris, R.: Vivaldi: a decentralized network coordinate system. In: Proceedings of SIGCOMM 2004, pp. 15–26. ACM, New York (2004)Google Scholar
  7. 7.
    Ramasubramanian, V., Sirer, E.G.: Beehive: O(1)lookup performance for power-law query distributions in peer-to-peer overlays. In: NSDI 2004: Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation, p. 8 (2004)Google Scholar
  8. 8.
    Gupta, A., Liskov, B., Rodrigues, R.: One hop lookups for peer-to-peer overlays. In: Ninth Workshop on Hot Topics in Operating Systems (HotOS-IX), Lihue, Hawaii, pp. 7–12 (2003)Google Scholar
  9. 9.
    Gupta, I., Birman, K., Linga, P., Demers, A., van Renesse, R.: Kelips: Building an efficient and stable P2P DHT through increased memory and background overhead. In: Kaashoek, M.F., Stoica, I. (eds.) IPTPS 2003. LNCS, vol. 2735, Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Leong, B., Liskov, B., Demaine, E.: EpiChord: parallelizing the chord lookup algorithm with reactive routing state management. Proceedings of ICON 1, 270–276 (2004)Google Scholar
  11. 11.
    Tati, K., Voelker, G.M.: ShortCuts: Using Soft State to Improve DHT Routing. In: Chi, C.-H., van Steen, M., Wills, C. (eds.) WCW 2004. LNCS, vol. 3293, pp. 44–62. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Sripanidkulchai, K., Maggs, B., Zhang, H.: Efficient content location using interest-based locality in peer-to-peer systems. In: Proceedings of INFOCOM 2003, vol. 3, pp. 2166–2176 (2003)Google Scholar
  13. 13.
    Manku, G., Bawa, M., Raghavan, P.: Symphony: Distributed hashing in a small world. In: Proceedings of the USITS 2003 (2003)Google Scholar
  14. 14.
    Kleinberg, J.: The small-world phenomenon: an algorithm perspective. In: STOC 2000: Proceedings of the 32nd annual ACM symposium on Theory of computing, pp. 163–170. ACM, New York (2000)Google Scholar
  15. 15.
    Siekkinen, M., Goebel, V.: Non-sticky fingers: Policy-driven self-optimization for order-preserving dhts. Technical report, University of Oslo / Helsinki University of Technology (2009),
  16. 16.
    Rhea, S., Geels, D., Roscoe, T., Kubiatowicz, J.: Handling churn in a DHT. In: ATEC 2004: Proceedings of the annual conference on USENIX Annual Technical Conference, Berkeley, CA, USA, USENIX Association, p. 10 (2004)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Matti Siekkinen
    • 1
    • 2
  • Vera Goebel
    • 2
  1. 1.Dept. of Computer Science and EngineeringHelsinki University of TechnologyFinland
  2. 2.Dept. of InformaticsUniversity of OsloBlindernNorway

Personalised recommendations