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Copyright © 2005 Department of Computer Science CPSC 641 Winter 20111 Self-Similar Network Traffic The original paper on network traffic self-similarity appeared at the 1993 ACM SIGCOMM Conference Authors: Will Leland, Murad Taqqu, Walter Willinger, and Daniel Wilson (Leland et al. 1993) Studied Ethernet LAN traffic Extended version appeared in IEEE/ACM Transactions on Networking, Vol. 2, No. 1, February 1994 One of the landmark papers of the 1990’s Highly regarded, influential, one of the most cited papers in the networking literature
Copyright © 2005 Department of Computer Science CPSC 641 Winter 20112 Main Contributions Identified presence of self-similarity property in aggregate Ethernet traffic Defined methodology for testing for the presence of self-similarity –autocorrelation function –variance-time plot –R/S statistic –periodogram (power spectrum) Proposed explanations/models for SS
Copyright © 2005 Department of Computer Science CPSC 641 Winter 20113 Measurement Study Detailed measurement study of very lengthy Ethernet packet traces, with high resolution timer, and lots of storage space One of the traces presented in their paper is a 27.5 hour trace Over 20 million packets
Copyright © 2005 Department of Computer Science CPSC 641 Winter 20114 Data Analysis Detailed statistical analysis: –aggregation, autocorrelation, R/S analysis, variance- time plot, periodograms, Whittle’s estimator, maximum likelihood... Very rigourous: confidence intervals, sophisticated statistical tests, sound methodology,... A wonderful paper to read (over and over)
Copyright © 2005 Department of Computer Science CPSC 641 Winter 20115 Main Results Aggregate Ethernet LAN traffic is self-similar Burstiness across many time scales Hurst parameter 0.7 < H < 0.9 H is larger when network utilization is higher (e.g., 0.9 when U = 15%) Self-similarity present on all LANs tested
Copyright © 2005 Department of Computer Science CPSC 641 Winter 20116 Conclusions Self-similarity is present in aggregate Ethernet LAN traffic Traffic does not aggregate well at all Law of large numbers may not hold! Poisson models (or Markovian models of any sort) do not capture reality at all Important to consider self-similar traffic
1 Self-Similar Ethernet LAN Traffic Carey Williamson University of Calgary.
Copyright © 2005 Department of Computer Science CPSC 641 Winter Self-Similarity in WAN Traffic A subsequent paper established the presence of network.
1 Self-Similar Wide Area Network Traffic Carey Williamson University of Calgary.
CMPT 855Module Network Traffic Self-Similarity Carey Williamson Department of Computer Science University of Saskatchewan.
On the Self-Similar Nature of Ethernet Traffic - Leland, et. Al Presented by Sumitra Ganesh.
1 FARIMA(p,d,q) Model and Application n FARIMA Models -- fractional autoregressive integrated moving average n Generating FARIMA Processes n Traffic Modeling.
1 Self Similar Traffic. 2 Self Similarity The idea is that something looks the same when viewed from different degrees of “magnification” or different.
Copyright © 2005 Department of Computer Science CPSC 641 Winter LAN Traffic Measurements Some of the first network traffic measurement papers were.
Self-Similar through High-Variability: Statistical Analysis of Ethernet LAN Traffic at the Source Level Walter Willinger, Murad S. Taqqu, Robert Sherman,
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.
COMPSAC'14 - N. Larrieu /07/ How to generate realistic network traffic? Antoine VARET and Nicolas LARRIEU COMPSAC – Vasteras – July the 23.
Doc.: IEEE /1216r1 Submission November 2009 BroadcomSlide 1 Internet Traffic Modeling Date: Authors: NameAffiliationsAddressPhone .
Network and Service Assurance Laboratory Analysis of self-similar Traffic Using Multiplexer & Demultiplexer Loaded with Heterogeneous ON/OFF Sources Huai.
2014 Examples of Traffic. Video Video Traffic (High Definition) –30 frames per second –Frame format: 1920x1080 pixels –24 bits per pixel Required rate:
CS 6401 Network Traffic Characteristics Outline Motivation Self-similarity Ethernet traffic WAN traffic Web traffic.
Self-Similarity of Network Traffic Presented by Wei Lu Supervised by Niclas Meier 05/
Self-Similarity in Network Traffic Kevin Henkener 5/29/2002.
2 Something “feels the same” regardless of scale 4 What is that???
Copyright © 2005 Department of Computer Science CPSC 641 Winter Network Traffic Measurement A focus of networking research for 20+ years Collect.
Copyright © 2005 Department of Computer Science CPSC 641 Winter WAN Traffic Measurements There have been several studies of wide area network traffic.
1 Chapters 9 Self-SimilarTraffic. Chapter 9 – Self-Similar Traffic 2 Introduction- Motivation Validity of the queuing models we have studied depends on.
A Nonstationary Poisson View of Internet Traffic T. Karagiannis, M. Molle, M. Faloutsos University of California, Riverside A. Broido University of California,
An Analytical Model for Network Flow Analysis Ernesto Gomez, Yasha Karant, Keith Schubert Institute for Applied Supercomputing Department of Computer Science.
1 LAN Traffic Measurements Carey Williamson Department of Computer Science University of Calgary.
Notices of the AMS, September Internet traffic Standard Poisson models don’t capture long-range correlations. Poisson Measured “bursty” on all time.
Link Dimensioning for Fractional Brownian Input Chen Jiongze PhD student, Electronic Engineering Department, City University of Hong Kong Supported by.
1 Self Similar Video Traffic Carey Williamson Department of Computer Science University of Calgary.
無線區域網路中自我相似交通流量之 成因與效能評估 The origin and performance impact of self- similar traffic for wireless local area networks 報 告 者：林 文 祺 指導教授：柯 開 維 博士.
CSE 561 – Traffic Models David Wetherall Spring 2000.
Self-Similar Traffic COMP5416 Advanced Network Technologies.
A Nonstationary Poisson View of Internet Traffic Thomas Karagiannis joint work with Mart Molle, Michalis Faloutsos, Andre Broido.
1 Interesting Links
Copyright © 2005 Department of Computer Science CPSC 641 Winter Data Analysis and Presentation There are many “tricks of the trade” used in data.
ABSTRACT We consider the problem of computing information theoretic functions such as entropy on a data stream, using sublinear space. Our first result.
SELF-SIMILAR INTERNET TRAFFIC AND IMPLICATIONS FOR WIRELESS NETWORK PERFORMANCE IN SUDAN Presented By HUDA M. A. EL HAG University Of Khartoum – Faculty.
October 14, 2002MASCOTS Workload Characterization in Web Caching Hierarchies Guangwei Bai Carey Williamson Department of Computer Science University.
THE TITLE OF YOUR PAPER Your Name Communication Networks Laboratory School of Engineering Science Simon Fraser University.
Hung X. Nguyen and Matthew Roughan The University of Adelaide, Australia SAIL: Statistically Accurate Internet Loss Measurements.
Internet Traffic Modeling Poisson Model vs. Self-Similar Model By Srividhya Chandrasekaran Dept of CS University of Houston.
Small scale analysis of data traffic models B. D’Auria - Eurandom joint work with S. Resnick - Cornell University.
DOWNLINK SCHEDULING IN CDMA NETWORKS GUIDE : Mrs. S.Malarvizhi Group : A5 G.R Brijesh ( ) Deepu K. Pillai ( ) Regi Thomas George ( )
Network Traffic Modeling Punit Shah CSE581 Internet Technologies OGI, OHSU 2002, March 6.
1 Network Traffic Measurement and Modeling Carey Williamson Department of Computer Science University of Calgary.
1 WAN Measurements Carey Williamson Department of Computer Science University of Calgary.
Internet Analysis - Performance Models - G.U. Hwang Next Generation Communication Networks Lab. Division of Applied Mathematics KAIST.
1 Network Simulation and Testing Polly Huang EE NTU
Variance of Aggregated Web Traffic Robert Morris MIT Laboratory for Computer Science IEEE INFOCOM 2000’
Risk Analysis Workshop April 14, 2004 HT, LRD and MF in teletraffic1 Heavy tails, long memory and multifractals in teletraffic modelling István Maricza.
Conceptual Differences Between Cube Analyst and Cube Analyst Drive Austen C. Duffy, Ph.D. Computational Mathematician, Citilabs.
ECEN4533 Data Communications Lecture #2125 February 2013 Dr. George Scheets n Read 11.4 n Problems: Chapter 11.2, 4, & 5 n Quiz #2, 25 March (Live) < 1.
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