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Activity Number:
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16
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 6, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #305812 |
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Title:
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Fourier Domain Estimation for Network Tomography
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Author(s):
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Jin Cao*+ and Aiyou Chen and Tian Bu
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Companies:
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Bell Labs, Lucent Technologies and Bell Labs, Lucent Technologies and Bell Labs, Lucent Technologies
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Address:
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700 Mountain Ave., Murray Hill, NJ, 07974,
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Keywords:
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network tomography ; Fourier domain inference ; general method of moments ; inverse problem ; delay tomography ; mixture model
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Abstract:
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Network tomography is a promising methodology for inferring unobservable network behaviors from directly measurable metrics that does not require cooperation between the network internal elements and the end users. In this talk, we will present a novel estimation approach for the network tomography problem based on Fourier domain inference. In addition, we also will obtain identifiability results that apply to general distribution models. We will focus on network delay tomography and develop a Fourier domain inference algorithm based on flexible mixture models of link delays. Through extensive model simulation and simulation using real internet trace, we are able to demonstrate that the new algorithm is computationally more efficient and yields more accurate estimates than previous methods, especially for a network with heterogeneous link delays.
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