Analyzing internet packet traces using Lindley's Recursion
Pages 2195 - 2201
Abstract
Internet trace packet data for a given network link contains information on each packet's arrival time and size. An important problem is to model the congestion packets experienced over the collection period. Recent research has utilized a relationship from Queueing Theory known as Lindley's Recursion to model packet congestion. This relationship has existed for 50 years and has been quite beneficial in analyzing these traces. We report on our use of Lindley's Recursion to analyze publicly-available link data from the Abilene Network, an Internet2 backbone network. We extend the use of Lindley's Recursion and include a discussion of the computational problems, numerical evaluation of trace packet performance and potential modeling issues, and a statistical investigation of the independence of packet interarrival times. In addition, we show how Lindley's Recursion can be used to extend the baseline analysis to interject Voice over Internet Protocol (VoIP) packets into the trace.
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Published In
December 2006
2429 pages
ISBN:1424405017
- Editors:
- L. Felipe Perrone,
- Barry G. Lawson,
- Jason Liu,
- Frederick P. Wieland,
- General Chair:
- David Nicol,
- Program Chair:
- Richard Fujimoto
Sponsors
- IIE: Institute of Industrial Engineers
- ASA: American Statistical Association
- IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
- IEEE-CS\DATC: The IEEE Computer Society
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
- NIST: National Institute of Standards and Technology
- (SCS): The Society for Modeling and Simulation International
- INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation
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Winter Simulation Conference
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Published: 03 December 2006
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- ASA
- IEICE ESS
- IEEE-CS\DATC
- SIGSIM
- NIST
- (SCS)
- INFORMS-CS
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WSC '06 Paper Acceptance Rate 177 of 252 submissions, 70%;
Overall Acceptance Rate 3,413 of 5,075 submissions, 67%
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