NDLI: On the predictive power of shortest-path weight inference

Content Provider	ACM Digital Library
Author	Maennel, Olaf Roughan, Matthew Coyle, Andrew Kraetzl, Miro
Abstract	Reverse engineering of the Internet is a valuable activity. Apart from providing scientific insight, the resulting datasets are invaluable in providing realistic network scenarios for other researchers. The Rocketfuel project attempted this process, but it is surprising how little effort has been made to validate its results. This paper concentrates on validating a particular inference methodology used to obtain link weights on a network. There is a basic difficulty in assessing the accuracy of such inferences in that a non-unique set of link-weights may produce the same routing, and so simple measurements of accuracy (even where ground truth data are available) do not capture the usefulness of a set of inferred weights. We propose a methodology based on predictive power to assess the quality of the weight inference. We used this to test Rocketfuel's algorithm, and our tests suggest that it is reasonably good particularly on certain topologies, though it has limitations when its underlying assumptions are incorrect.
Starting Page	305
Ending Page	310
Page Count	6
File Format	PDF
ISBN	9781605583341
DOI	10.1145/1452520.1452556
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-10-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Inference Routing Topology
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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On the predictive power of shortest-path weight inference