Distributed Network Querying with Bounded Approximate Caching

  • Badrish Chandramouli
  • Jun Yang
  • Amin Vahdat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3882)


As networks continue to grow in size and complexity, distributed network monitoring and resource querying are becoming increasingly difficult. Our aim is to design, build, and evaluate a scalable infrastructure for answering queries over distributed measurements, at reduced costs (in terms of both network traffic and query latency) while maintaining required precision. In this infrastructure, each network node owns a set of numerical measurements and actively maintains bounds on these values cached at other nodes. We can answer queries approximately, using bounds from nearby caches to avoid contacting the owners directly. We focus on developing efficient and scalable techniques to place, locate, and manage bounded approximate caches across a large network. We have developed two approaches: One uses a recursive partitioning of the network space to place caches in a static, controlled manner, while the other uses a locality-aware distributed hash table to place caches in a dynamic and decentralized manner. In this paper, we focus on the latter approach. Experiments over a large-scale emulated network show that our techniques are very effective in reducing query costs while generating an acceptable amount of background traffic; they are also able to exploit various forms of locality that are naturally present in queries, and adapt to volatility of measurements.


Overlay Network Distribute Hash Table Cache Size Query Region Nearby Node 
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.


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  1. 1.
    Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. CACM (1970)Google Scholar
  2. 2.
    Castro, M., Druschel, P., Kermarrec, A., Rowstron, A.: SCRIBE: A large-scale and decentralized application-level multicast infrastructure. IEEE JSAC (2002)Google Scholar
  3. 3.
    Chandramouli, B., Yang, J., Vahdat, A.: Distributed network querying with bounded approximate caching. Technical report, Department of Computer Science, Duke University (June 2004)Google Scholar
  4. 4.
    Chang, H., Govindan, R., Jamin, S., Shenker, S., Willinger, W.: Towards Capturing Representative AS-Level Internet Topologies. In: SIGMETRICS (2002)Google Scholar
  5. 5.
    Dar, S., Franklin, M.J., Jónsson, B., Srivastava, D., Tan, M.: Semantic data caching and replacement. In: VLDB (1996)Google Scholar
  6. 6.
    Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar
  7. 7.
    Huebsch, R., Hellerstein, J.M., Lanham, N., Loo, B.T., Shenker, S., Stoica, I.: Querying the internet with PIER. In: Aberer, K., Koubarakis, M., Kalogeraki, V. (eds.) VLDB 2003. LNCS, vol. 2944, Springer, Heidelberg (2004)Google Scholar
  8. 8.
    Massie, M.L., Chun, B.N., Culler, D.E.: The Ganglia distributed monitoring system: Design, implementation, and experience. Parallel Computing (2004)Google Scholar
  9. 9.
    Ng, T.S.E., Zhang, H.: Predicting internet network distance with coordinates-based approaches. In: IEEE INFOCOM (2002)Google Scholar
  10. 10.
    Olston, C.: Approximate Replication. PhD thesis, Stanford University (2003)Google Scholar
  11. 11.
    Olston, C., Loo, B.T., Widom, J.: Adaptive precision setting for cached approximate values. In: SIGMOD (2001)Google Scholar
  12. 12.
  13. 13.
    Rabinovich, M., Spatschek, O.: Web caching and replication. Addison-Wesley, Reading (2002)Google Scholar
  14. 14.
    Rowstron, A., 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, p. 2001. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Shah, S., Ramamritham, K., Shenoy, P.J.: Maintaining coherency of dynamic data in cooperating repositories. In: Bressan, S., Chaudhri, A.B., Li Lee, M., Yu, J.X., Lacroix, Z. (eds.) CAiSE 2002 and VLDB 2002. LNCS, vol. 2590, Springer, Heidelberg (2003)Google Scholar
  16. 16.
    Vahdat, A., Yocum, K., Walsh, K., Mahadevan, P., Kostić, D., Chase, J., Becker, D.: Scalability and accuracy in a large-scale network emulator. ACM SIGOPS Operating Systems Review (2002)Google Scholar
  17. 17.
    Van Renesse, R., Birman, K.P., Vogels, W.: Astrolabe: A robust and scalable technology for distributed system monitoring, management, and data mining. ACM TOCS (2003)Google Scholar
  18. 18.
    Yalagandula, P., Dahlin, M.: A scalable distributed information management system. In: SIGCOMM (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Badrish Chandramouli
    • 1
  • Jun Yang
    • 1
  • Amin Vahdat
    • 2
  1. 1.Dept. of Computer ScienceDuke UniversityUSA
  2. 2.Dept. of Computer Science and Engg.University of CaliforniaSan DiegoUSA

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