Fault-Tolerant Aggregation by Flow Updating

  • Paulo Jesus
  • Carlos Baquero
  • Paulo Sérgio Almeida
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5523)

Abstract

Data aggregation plays an important role in the design of scalable systems, allowing the determination of meaningful system-wide properties to direct the execution of distributed applications. In the particular case of wireless sensor networks, data collection is often only practicable if aggregation is performed. Several aggregation algorithms have been proposed in the last few years, exhibiting different properties in terms of accuracy, speed and communication tradeoffs. Nonetheless, existing approaches are found lacking in terms of fault tolerance. In this paper, we introduce a novel fault-tolerant averaging based data aggregation algorithm. It tolerates substantial message loss (link failures), while competing algorithms in the same class can be affected by a single lost message. The algorithm is based on manipulating flows (in the graph theoretical sense), that are updated using idempotent messages, providing it with unique robustness capabilities. Furthermore, evaluation results obtained by comparing it with other averaging approaches have revealed that it outperforms them in terms of time and message complexity.

Keywords

Wireless Sensor Network Overlay Network Aggregation Function Link Failure Node Failure 
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.

References

  1. 1.
    Van Renesse, R.: The importance of aggregation. In: Schiper, A., Shvartsman, M.M.A.A., Weatherspoon, H., Zhao, B.Y. (eds.) Future Directions in Distributed Computing. LNCS, vol. 2584, pp. 87–92. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Stoica, I., Morris, R., Karger, D., Kaashoek, M., Balakrishnan, H.: Chord: A scalable peer-to-peer lookup service for internet applications. In: SIGCOMM 2001: Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications, pp. 149–160 (August 2001)Google Scholar
  3. 3.
    Abraham, I., Malkhi, D.: Probabilistic quorums for dynamic systems. Distributed Computing 18(2), 113–124 (2005)CrossRefMATHGoogle Scholar
  4. 4.
    Madden, S., Franklin, M., Hellerstein, J., Hong, W.: TAG: a Tiny AGgregation service for ad-hoc sensor networks. ACM SIGOPS Operating Systems Review 36(SI), 131–146 (2002)CrossRefGoogle Scholar
  5. 5.
    Li, J., Sollins, K., Lim, D.: Implementing aggregation and broadcast over distributed hash tables. ACM SIGCOMM Computer Communication Review 35(1), 81–92 (2005)CrossRefGoogle Scholar
  6. 6.
    Birk, Y., Keidar, I., Liss, L., Schuster, A., Wolff, R.: Veracity radius: capturing the locality of distributed computations. In: PODC 2006: Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing (July 2006)Google Scholar
  7. 7.
    Kempe, D., Dobra, A., Gehrke, J.: Gossip-based computation of aggregate information. In: Proceedings. 44th Annual IEEE Symposium on Foundations of Computer Science, pp. 482–491 (2003)Google Scholar
  8. 8.
    Jelasity, M., Montresor, A., Babaoglu, O.: Gossip-based aggregation in large dynamic networks. In: ACM Transactions on Computer Systems, TOCS (2005)Google Scholar
  9. 9.
    Chen, J.-Y., Pandurangan, G., Xu, D.: Robust computation of aggregates in wireless sensor networks: Distributed randomized algorithms and analysis. IEEE Transactions on Parallel and Distributed Systems 17(9), 987–1000 (2006)CrossRefGoogle Scholar
  10. 10.
    Wuhib, F., Dam, M., Stadler, R., Clemm, A.: Robust monitoring of network-wide aggregates through gossiping. In: 10th IFIP/IEEE International Symposium on Integrated Network Management, pp. 226–235 (2007)Google Scholar
  11. 11.
    Baquero, C., Almeida, P.S., Menezes, R.: Fast estimation of aggregates in unstructured networks. In: International Conference on Autonomic and Autonomous Systems (ICAS), Valencia, Spain. IEEE Computer Society, Los Alamitos (2009)Google Scholar
  12. 12.
    Mosk-Aoyama, D., Shah, D.: Computing separable functions via gossip. In: PODC 2006: Proceedings of the twenty-fifth annual ACM symposium on Principles of Distributed Computing, pp. 113–122 (2006)Google Scholar
  13. 13.
    Kostoulas, D., Psaltoulis, D., Gupta, I., Birman, K., Demers, A.: Decentralized schemes for size estimation in large and dynamic groups. In: Fourth IEEE International Symposium on Network Computing and Applications, pp. 41–48 (2005)Google Scholar
  14. 14.
    Massoulié, L., Merrer, E., Kermarrec, A.-M., Ganesh, A.: Peer counting and sampling in overlay networks: random walk methods. In: PODC 2006: Proceedings of the twenty-fifth annual ACM symposium on Principles of Distributed Computing (July 2006)Google Scholar
  15. 15.
    Ganesh, A., Kermarrec, A., Le Merrer, E., Massoulié, L.: Peer counting and sampling in overlay networks based on random walks. Distributed Computing 20(4), 267–278 (2007)CrossRefMATHGoogle Scholar
  16. 16.
    Jelasity, M., Montresor, A.: Epidemic-style proactive aggregation in large overlay networks. In: 24th International Conference on Distributed Computing Systems (2004)Google Scholar
  17. 17.
    Lynch, N.A.: Distributed Algorithms. Morgan Kaufmann Publishers Inc., San Francisco (1996)MATHGoogle Scholar
  18. 18.
    Diestel, R.: Graph Theory, 3rd edn. Graduate Texts in Mathematics, vol. 173. Springer, Heidelberg (2005)MATHGoogle Scholar
  19. 19.
    Erdős, P., Rényi, A.: On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences 5, 17–61 (1960)MathSciNetMATHGoogle Scholar
  20. 20.
    Kaashoek, M.F., Karger, D.R.: Koorde: A simple degree-optimal distributed hash table. In: Kaashoek, M.F., Stoica, I. (eds.) IPTPS 2003. LNCS, vol. 2735, pp. 98–107. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Paulo Jesus
    • 1
  • Carlos Baquero
    • 1
  • Paulo Sérgio Almeida
    • 1
  1. 1.University of Minho (CCTC-DI)BragaPortugal

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