Inference and Labeling of Metric-Induced Network Topologies

  • Authors:
  • Azer Bestavros;John W. Byers;Khaled A. Harfoush

  • Affiliations:
  • IEEE;-;IEEE

  • Venue:
  • IEEE Transactions on Parallel and Distributed Systems
  • Year:
  • 2005

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Abstract

The development and deployment of distributed network-aware applications and services require the ability to compile and maintain a model of the underlying network resources with respect to one or more characteristic properties of interest. To be manageable, such models must be compact; and to be general-purpose, should enable a representation of properties along temporal, spatial, and measurement resolution dimensions. In this paper, we propose MINT驴a general framework for the construction of such metric-induced models using end-to-end measurements. We present the basic theoretical underpinnings of MINT for a broad class of performance metrics, and describe Periscope, a Linux embodiment of MINT constructions. We instantiate MINT and Periscope for a specific metric of interest驴namely, packet loss rates驴and present results of simulations and Internet measurements that confirm the effectiveness and robustness of our constructions over a wide range of network conditions.