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Activity Number:
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250
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #309141 |
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Title:
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Sample-Based Estimation of Internet Traffic Flow Characteristics
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Author(s):
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Lili Yang*+ and George Michailidis
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Companies:
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The University of Michigan and The University of Michigan
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Address:
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439 West Hall 1085 South University, Ann Arbor, MI, 48109-1107,
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Keywords:
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EM Algorithm ; Network ; nonparametric ; flow ; internet ; traffic
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Abstract:
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Understanding the characteristics of traffic flows is crucial for allocating the necessary resources (bandwidth) to accommodate users demand. In this paper, the problem of nonparametric estimation of network flow characteristics based on sampled flow data from single-stage Bernoulli sampling and two-stage sampling will be addressed. An adaptive Expectation-maximization (EM) algorithm is used for the flow length distribution, which in addition provides an estimate for the number of active flows. The estimation of the flow sizes (in bytes) is accomplished through a regression model. The proposed approaches are illustrated and compared on a number of synthetic and real datasets.
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