SCUBA: Focus and Context for Real-Time Mesh Network Health Diagnosis

  • Amit P. Jardosh
  • Panuakdet Suwannatat
  • Tobias Höllerer
  • Elizabeth M. Belding
  • Kevin C. Almeroth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4979)

Abstract

Large-scale wireless metro-mesh networks consisting of hundreds of routers and thousands of clients suffer from a plethora of performance problems. The sheer scale of such networks, the abundance of performance metrics, and the absence of effective tools can quickly overwhelm a network operators’ ability to diagnose these problems. As a solution, we present SCUBA, an interactive focus and context visualization framework for metro-mesh health diagnosis. SCUBA places performance metrics into multiple tiers or contexts, and displays only the topmost context by default to reduce screen clutter and to provide a broad contextual overview of network performance. A network operator can interactively focus on problem regions and zoom to progressively reveal more detailed contexts only in the focal region. We describe SCUBA’s contexts and its planar and hyperbolic views of a nearly 500 node mesh to demonstrate how it eases and expedites health diagnosis. Further, we implement SCUBA on a 15-node testbed, demonstrate its ability to diagnose a problem within a sample scenario, and discuss its deployment challenges in a larger mesh. Our work leads to several future research directions on focus and context visualization and efficient metrics collection for fast and efficient mesh network health diagnosis.

Keywords

wireless mesh networks network visualization network health 

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References

  1. 1.
    Tropos Report on Google WiFi Network, www.muniwireless.com/article/articleview/5403
  2. 2.
    Card, S.K., Mackinlay, J.D., Shneiderman, B.: Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann Publishers Inc, San Francisco, CAGoogle Scholar
  3. 3.
    De Couto, D., Aguayo, D., Bicket, J., Morris, R.: A High-throughput Path Metric for Multi-hop Wireless Routing. Wireless Networks 11(4), 419–434 (2005)CrossRefGoogle Scholar
  4. 4.
    Jardosh, A.P., Ramchandran, K.N., Almeroth, K.C., Belding, E.M.: Understanding Congestion in IEEE 802.11b Wireless Networks. In: Proceedings of USENIX IMC, Berkeley, CA (October 2005)Google Scholar
  5. 5.
    Lundgren, H., Ramachandran, K.N., Belding-Royer, E.M., Almeroth, K.C., Benny, M., Hewatt, A., Touma, A., Jardosh, A.P.: Experiences from the Design, Deployment, and Usage of the UCSB MeshNet Testbed. IEEE Wireless Communications Magazine 13, 18–29 (2006)CrossRefGoogle Scholar
  6. 6.
    Marti, S., Giuli, T., Lai, K., Baker, M.: Mitigating Routing Misbehavior in Mobile Ad hoc Networks. In: Proceedings of MOBICOM, Boston, MA, pp. 255–265 (2000)Google Scholar
  7. 7.
    Munzner, T.: Interactive Visualization of Large Graphs and Networks. PhD thesis, Stanford University (June 2000)Google Scholar
  8. 8.
    Paxson, V.: Strategies for Sound Internet Measurement. In: Proceedings of IMC, October 2004, pp. 263–271. Taormina, Sicily (2004)CrossRefGoogle Scholar
  9. 9.
    Qiu, L., Bahl, P., Rao, A., Zhou, L.: Troubleshooting Wireless Mesh Networks. ACM SIGCOMM Computer Communication Review 36(5), 17–28 (2006)CrossRefGoogle Scholar
  10. 10.
    Sommers, J., Barford, P., Willinger, W.: SPLAT: A Visualization Tool for Mining Internet Measurements. In: Proceedings of PAM, Adelaide, Australia (March 2006)Google Scholar
  11. 11.
    Tukey, J.: Exploratory Data Analysis. Addison-Wesley, Menlo Park, CA (1977)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Amit P. Jardosh
    • 1
  • Panuakdet Suwannatat
    • 1
  • Tobias Höllerer
    • 1
  • Elizabeth M. Belding
    • 1
  • Kevin C. Almeroth
    • 1
  1. 1.Department of Computer ScienceUC Santa Barbara 

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