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Copyright © 2005 Department of Computer Science CPSC 641 Winter Self-Similarity in WAN Traffic A subsequent paper established the presence of network traffic self-similarity in wide area Internet traffic as well “Wide Area Traffic: The Failure of Poisson Modeling”, by Vern Paxson and Sally Floyd, ACM SIGCOMM 1994 Their original intent was to show that self-similarity is not present in WAN traffic, but they failed! Self-similarity is present in WAN traffic Identified where it appears and where it does not Identifies limitations of Poisson models
Copyright © 2005 Department of Computer Science CPSC 641 Winter Main Contributions Identified presence of self-similarity property in Internet traffic Defined methodology for testing for the presence of self-similarity, and for testing the goodness of Poisson models Identified importance of “heavy tails” Proposed explanations/models for SS Proposed complete model for telnet
Copyright © 2005 Department of Computer Science CPSC 641 Winter Measurement Study Detailed measurement study of very lengthy Internet packet traces, with high resolution timer, and lots of storage space Traces range from 1 hour to 30 days in duration Millions of TCP packets and connections Several different sites
Copyright © 2005 Department of Computer Science CPSC 641 Winter Data Analysis Detailed statistical analysis: –connection interarrivals, per application analysis, packet level, connection level, tests for Poisson- ness, models, evaluation,... Very rigourous: confidence intervals, sophisticated statistical tests, sound methodology,... A wonderful paper to read
Copyright © 2005 Department of Computer Science CPSC 641 Winter Main Results Connection arrivals for telnet appear to be Poisson, but... Packet arrivals are definitely not Poisson Connection arrivals for ftp and other applications do not appear to be Poisson Traffic exhibits long range dependence and other aspects of self-similarity
Copyright © 2005 Department of Computer Science CPSC 641 Winter Conclusions Self-similarity is present in aggregate WAN Internet traffic Poisson models (or Markovian models of any sort) do not capture reality at all (except possibly for telnet connection arrivals) Important to consider self-similar traffic
1 Self-Similar Wide Area Network Traffic Carey Williamson University of Calgary.
Copyright © 2005 Department of Computer Science CPSC 641 Winter Self-Similar Network Traffic The original paper on network traffic self-similarity.
1 Self-Similar Ethernet LAN Traffic Carey Williamson University of Calgary.
Copyright © 2005 Department of Computer Science CPSC 641 Winter WAN Traffic Measurements There have been several studies of wide area network traffic.
Copyright © 2005 Department of Computer Science CPSC 641 Winter LAN Traffic Measurements Some of the first network traffic measurement papers were.
1 WAN Measurements Carey Williamson Department of Computer Science University of Calgary.
Copyright © 2005 Department of Computer Science CPSC 641 Winter Network Traffic Measurement A focus of networking research for 20+ years Collect.
A Nonstationary Poisson View of Internet Traffic T. Karagiannis, M. Molle, M. Faloutsos University of California, Riverside A. Broido University of California,
1 LAN Traffic Measurements Carey Williamson Department of Computer Science University of Calgary.
CS 6401 Network Traffic Characteristics Outline Motivation Self-similarity Ethernet traffic WAN traffic Web traffic.
Origins of Long Range Dependence Myths and Legends Aleksandar Kuzmanovic 01/08/2001.
1 Network Traffic Measurement and Modeling Carey Williamson Department of Computer Science University of Calgary.
Internet Traffic Modeling Poisson Model vs. Self-Similar Model By Srividhya Chandrasekaran Dept of CS University of Houston.
Doc.: IEEE /1216r1 Submission November 2009 BroadcomSlide 1 Internet Traffic Modeling Date: Authors: NameAffiliationsAddressPhone .
Network Traffic Measurement and Modeling CSCI 780, Fall 2005.
1 Internet Traffic Measurement and Modeling Carey Williamson Department of Computer Science University of Calgary.
1 Chapters 9 Self-SimilarTraffic. Chapter 9 – Self-Similar Traffic 2 Introduction- Motivation Validity of the queuing models we have studied depends on.
Notices of the AMS, September Internet traffic Standard Poisson models don’t capture long-range correlations. Poisson Measured “bursty” on all time.
On the Constancy of Internet Path Properties ACM SIGCOMM Internet Measurement Workshop November, 2001 Yin Zhang Nick Duffield Vern Paxson Scott Shenker.
On the Self-Similar Nature of Ethernet Traffic - Leland, et. Al Presented by Sumitra Ganesh.
On the Constancy of Internet Path Properties Yin Zhang, Nick Duffield AT&T Labs Vern Paxson, Scott Shenker ACIRI Internet Measurement Workshop 2001 Presented.
Applications of Poisson Process Wang C. Ng. Telephone traffic Pure chance traffic: Independent random events (memoryless). Stationary: Busy/peak hours.
Estimation and identification of long-range dependence in Internet traffic Thomas Karagiannis University of California,
A Nonstationary Poisson View of Internet Traffic Thomas Karagiannis joint work with Mart Molle, Michalis Faloutsos, Andre Broido.
CMPT 855Module Network Traffic Self-Similarity Carey Williamson Department of Computer Science University of Saskatchewan.
1 Self Similar Video Traffic Carey Williamson Department of Computer Science University of Calgary.
Self-Similarity of Network Traffic Presented by Wei Lu Supervised by Niclas Meier 05/
Spatio-Temporal Modeling of Traffic Workload in a Campus WLAN Felix Hernandez-Campos 3 Merkouris Karaliopoulos 2 Maria Papadopouli 1,2,3 Haipeng Shen 2.
An Empirical Study of Real Audio Traffic A. Mena and J. Heidemann USC/Information Sciences Institute In Proceedings of IEEE Infocom Tel-Aviv, Israel March.
2014 Examples of Traffic. Video Video Traffic (High Definition) –30 frames per second –Frame format: 1920x1080 pixels –24 bits per pixel Required rate:
Björn Landfeldt School of Information Technologies Investigating a theoretical model Bjorn Landfeldt University of Sydney.
Self-Similarity in Network Traffic Kevin Henkener 5/29/2002.
Copyright © 2005 Department of Computer Science CPSC 641 Winter Simulation Validation Plan: –Discuss verification and validation –Define concepts.
Self-Similar through High-Variability: Statistical Analysis of Ethernet LAN Traffic at the Source Level Walter Willinger, Murad S. Taqqu, Robert Sherman,
Networking Basics: A Review Carey Williamson iCORE Chair and Professor Department of Computer Science University of Calgary.
A First Look at Modern Enterprise Traffic Ruoming Pang, Princeton University Mark Allman (ICSI), Mike Bennett (LBNL), Jason Lee (LBNL), Vern Paxson (ICSI/LBNL),
On Efficient On-line Grouping of Flows with Shared Bottlenecks at Loaded Servers by O. Younis and S. Fahmy Department of Computer Sciences, Purdue University.
E2E Routing Behavior in the Internet Vern Paxson Sigcomm 1996 Slides are adopted from Ion Stoica’s lecture at UCB.
Assignment #3 Probability Ideas & Graphing Tools Sunday Sept 17, 11:55pm.
SELF-SIMILAR INTERNET TRAFFIC AND IMPLICATIONS FOR WIRELESS NETWORK PERFORMANCE IN SUDAN Presented By HUDA M. A. EL HAG University Of Khartoum – Faculty.
1 Interesting Links. On the Self-Similar Nature of Ethernet Traffic Will E. Leland, Walter Willinger and Daniel V. Wilson BELLCORE Murad S. Taqqu BU Analysis.
Fast Portscan Detection Using Sequential Hypothesis Testing Authors: Jaeyeon Jung, Vern Paxson, Arthur W. Berger, and Hari Balakrishnan Publication: IEEE.
1 CS 268: Lecture 14 Internet Measurements Scott Shenker and Ion Stoica Computer Science Division Department of Electrical Engineering and Computer Sciences.
Prentice HallHigh Performance TCP/IP Networking, Hassan-Jain Chapter 4 TCP/IP Network Simulation.
Connection-level Analysis and Modeling of Network Traffic understanding the cause of bursts control and improve performance detect changes of network state.
Exponential and Chi-Square Random Variables. Recall Poisson R. V. In a fixed time interval of length T, if there are an average of arrivals, then “number.
Report by: Loizos Konomou EL933 Fall 2005 Prof: Yong Liu Ruoming Pang, Mark Allman, Mike Bennett, Jason Lee, Vern Paxson, Brian Tierney Princeton University,
End-to-End Routing Behavior in the Internet Vern Paxson Presented by Zhichun Li.
Observed Structure of Addresses in IP Traffic CSCI 780, Fall 2005.
DiFMon Distributed Flow Monitor Dario Salvi Consorzio Interuniversitario Nazionale per l’Informatica (CINI) Naples, Italy.
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