On the stability of network distance estimation

  • Authors:
  • Yan Chen;Khian Hao Lim;Randy H. Katz;Chris Overton

  • Affiliations:
  • University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;Keynote Systems, Inc.

  • Venue:
  • ACM SIGMETRICS Performance Evaluation Review
  • Year:
  • 2002

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Abstract

Overlay network distance monitoring and estimation system can benefit many new applications and services, such as peer-to-peer overlay routing and location. However, there is a lack of such scalable system with small overhead, good usability, and good distance estimation accuracy and stability. Thus we propose a scalable overlay distance monitoring system, Internet Iso-bar, which clusters hosts based on the similarity of their perceived network distance, with no assumption about the underlying network topology. The centers of each cluster are then chosen as monitors to represent their clusters for probing and distance estimation. We compare it with other network distance estimation systems, such as Global Network Positioning (GNP) [1]. Internet Iso-bar is easy to implement and use, and has good scalability and small communication and computation cost for online monitoring. Preliminary evaluation on real Internet measurement data also shows that Internet Iso-bar has high prediction accuracy and stability. Finally, by adjusting the number of clusters, we can smoothly trade off the measurement and management cost for better distance estimation accuracy and stability.