Resource Adaptive Distributed Information Sharing

  • Hans Vatne Hansen
  • Vera Goebel
  • Thomas Plagemann
  • Matti Siekkinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6164)

Abstract

We have designed, implemented and evaluated a resource adaptive distributed information sharing system where automatic adjustments are made internally in our information sharing system in order to cope with varying resource consumption. CPU load is monitored and a light-weight trigger mechanism is used to avoid overload situations on a per-machine basis. Additional improvements are obtained by calculating what we call a utility score to better determine how the data structures in the system should be arranged. Our results show that resource adaptation is an efficient way of improving query throughput, and that it is most effective when the number of stored data items in the system is large or many queries are performed concurrently. By applying resource adaptation, we are able to significantly improve the performance of our information sharing system.

Keywords

autonomic networks self-optimization resource adaptation 

References

  1. 1.
    ANA Project. ANA Blueprint, sixth framework programme edition (February 2008)Google Scholar
  2. 2.
    Bharambe, A.R., Agrawal, M., Seshan, S.: Mercury: Supporting scalable multi-attribute range queries. In: ACM SIGCOMM (2004)Google Scholar
  3. 3.
    Ferrari, D., Zhou, S.: An empirical investigation of load indices for load balancing applications. Technical Report UCB/CSD-87-353, EECS Department, University of California, Berkeley (May 1987)Google Scholar
  4. 4.
    Jokela, P., Zahemszky, A., Rothenberg, C.E., Arianfar, S., Nikander, P.: Lipsin: line speed publish/subscribe inter-networking. In: SIGCOMM 2009: Proceedings of the ACM SIGCOMM 2009 conference on Data communication, pp. 195–206. ACM, New York (2009)CrossRefGoogle Scholar
  5. 5.
    Kangasharju, J., Ross, K.W., Turner, D.A.: Optimizing file availability in peer-to-peer content distribution. In: IEEE INFOCOM 2007, 26th IEEE International Conference on Computer Communications, May 2007, pp. 1973–1981 (2007)Google Scholar
  6. 6.
    Koponen, T., Chawla, M., Chun, B.-G., Ermolinskiy, A., Kim, K.H., Shenker, S., Stoica, I.: A data-oriented (and beyond) network architecture. SIGCOMM Comput. Commun. Rev. 37(4), 181–192 (2007)CrossRefGoogle Scholar
  7. 7.
    Li, X., Bian, F., Zhang, H., Diot, C., Govindan, R., Hong, W., Iannaccone, G.: Mind: A distributed multi-dimensional indexing system for network diagnosis. In: Proceedings of INFOCOM 2006, pp. 1–12 (2006)Google Scholar
  8. 8.
    Liang, J., Gu, X., Nahrstedt, K.: Self-configuring information management for large-scale service overlays. In: INFOCOM, pp. 472–480 (2007)Google Scholar
  9. 9.
    The Linux man-pages project. Linux Programmer’s Manual. SYSINFO(2) (November 1997)Google Scholar
  10. 10.
    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
  11. 11.
    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, p.10. USENIX Association, Berkeley (2004)Google Scholar
  12. 12.
    Shen, H., Xu, C.-Z.: Elastic routing table with provable performance for congestion control in dht networks. In: ICDCS 2006: Proceedings of the 26th IEEE International Conference on Distributed Computing Systems, Washington, DC, USA, p. 15. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  13. 13.
    Stoica, I., Morris, R., Karger, D., Frans Kaashoek, M., Balakrishnan, H.: Chord: A scalable peer-to-peer lookup service for internet applications. In: Proceedings of the ACM SIGCOMM 2001 Conference, San Diego, California, August 2001, pp. 149–160 (2001)Google Scholar
  14. 14.
    Van Renesse, R., Birman, K.P., Vogels, W.: Astrolabe: A robust and scalable technology for distributed system monitoring, management, and data mining. ACM Trans. Comput. Syst. 21(2), 164–206 (2003), doi.acm.org/10.1145/762483.762485 CrossRefGoogle Scholar
  15. 15.
    Walsworth, C., Aben, E., Claffy, K.C., Andersen, D.: The caida anonymized 2009 internet traces - equinix-chicago.dira.20090331-055905.utc (2009), http://www.caida.org/data/passive/passive_2009
  16. 16.
    Yalagandula, P., Dahlin, M.: A scalable distributed information management system. In: SIGCOMM 2004: Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications, pp. 379–390. ACM, New York (2004)CrossRefGoogle Scholar
  17. 17.
    Zhong, M., Shen, K., Seiferas, J.: Replication degree customization for high availability. In: Eurosys 2008: Proceedings of the 3rd ACM SIGOPS/EuroSys European Conference on Computer Systems 2008, pp. 55–68. ACM, New York (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hans Vatne Hansen
    • 1
  • Vera Goebel
    • 1
  • Thomas Plagemann
    • 1
  • Matti Siekkinen
    • 2
  1. 1.Department of InformaticsUniversity of Oslo 
  2. 2.Department of Computer Science and EngineeringAalto University School of Science and Technology 

Personalised recommendations