University of Wollongong Research Online University of Wollongong Thesis Collection 1954-2016 University of Wollongong Thesis Collections 1997 On the effect of the ethernet protocol in LAN Traffic modelling I. Gusti Paramajaya University of Wollongong Follow this and additional works at: https://ro.uow.edu.au/theses University of Wollongong Copyright Warning You may print or download ONE copy of this document for the purpose of your own research or study. The University does not authorise you to copy, communicate or otherwise make available electronically to any other person any copyright material contained on this site. You are reminded of the following: This work is copyright. Apart from any use permitted under the Copyright Act 1968, no part of this work may be reproduced by any process, nor may any other exclusive right be exercised, without the permission of the author. Copyright owners are entitled to take legal action against persons who infringe their copyright. 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For further information contact the UOW Library: [email protected] A Thesis entitled On the Effect of the Ethernet Protocol in LAN Traffic Modelling Submitted to the University of Wollongong in partial fulfilment of the requirements for the degree of Master of Engineering in Telecommunications Engineering (Honours) by I Gusti Putu Bagus Paramajaya Saijana Teknik Elektro Bandung Institute of Technology 1991 Department of Electrical and Computer Engineering March 1997 Abstract There are two groups of traffic models that usually used in telecommunication network design, planning and performance evaluation. They are aggregate and individual models. In case of individual models, measurement-based studies are the most common approach used to obtain an accurate traffic model. The common assumption used in this approach is that the data link layer protocol (i.e., layer 2 of OSI Reference Model) does not significantly affect traffic characteristics of its higher layer where the traffic characteristics are of interest. In other words, this hypothesis assumes that traffic characteristics at the input of the data link protocol is statistically the same as the traffic characteristics at its output, i.e., the physical layer where the measurement can be easily done. This hypothesis is addressed as the null hypothesis. This thesis is concerned with testing the validity and investigating the sensitivity of . the null hypothesis against several parameters. For these purposes, an Ethernet network simulation model is developed and several statistical methods are utilised. Several simulation network configurations are considered as test configurations which represent different combination of test parameters, namely network parameters, protocol parameters and user parameters. Those configurations are used l to test the null hypothesis under two different traffic source models, i.e., Poisson and Pareto traffic models. Statistical test results indicated that the validity of the null hypothesis mostly depends on traffic model under consideration and is sensitive to the activity level of monitored station. The comprehensive statistical test results concluded that the null hypothesis is valid for relatively low-level activity stations (stations having packet departure rate up to 10 packets/second) and sensitive only to the user parameter of user activity level. In addition, this thesis also presents a case study that demonstrates the liability of applying the null hypothesis in measurement-based traffic modelling without further formal validation. 11 Acknowledgements I would like to thank my supervisor Mr. David J. Atkinson for his helpful support, guidance and encouragement. I would also like to thank all staff in Department of Electrical and Computer Engineering University of Wollongong, whose supports made it possible for me to finish my study. In particular I would like to acknowledge the helpful statistical discussion and suggestion given by Dr. Pam J. Davy from Department of Applied Statistics through Statistical Consulting Services. I am very grateful to my company PT TELEKOMUNIKASI INDONESIA for the financial support. I also thank my friends and colleagues Kalamullah Ramli, Chamilla Fernando, Narasimhan Sriram, Nagaraja, Li Xue, Ali Yazdian Vaijani, Ery Yusuf, Widi Nugroho and Winahyo Pratomo. My special thank goes to my wife who has faithfully stood beside me during problems and inconvenience during the thesis finalisation process in our first year of marriage. My father, mother, brother and sisters and relatives in my country also deserve thanks for their support and encouraging me to do my best. Finally, I’d like to thank deeply all hidden supports given by my spiritual guidance Bhagawan Sri Sathya Sai Baba. Without Him, it is impossible for me to finish all my work. IGPB Paramajaya IV Statement of Originality The work described in this thesis is entirely my own, except where due reference is made in the text. No work in this thesis has been submitted for a degree to any other university or institution Signed IGPB Paramajaya m Table of Contents Abstract i Statement of Originality iii Acknowledgements iv Table of Contents v List of Abbreviations ix List of Figures xi List of Tables xiv 1. Introduction 1 1.1 Background 1 1.1.1 Link-Level (Aggregate) Model and Protocol-Specific (Individual) Model 1 1.1.2 Traffic Model and OSI Model 2 1.2 Thesis Outline 2 *4 * 1.3 Contributions 7 2. LAN Traffic Source Modelling 9 2.1 Outline 9 2.2 Measurement-Based LAN Traffic Modelling 10 2.3 The Null Hypothesis 11 v 2.4 Factors Affecting The Null Hypothesis in an Ethernet Network 13 2.4.1 Network Parameters 14 2.4.2 User Parameters 15 2.4.3 Protocol Parameters 16 2.5 Problems in LAN Traffic Measurement 17 2.6 Simulation as an Evaluation Technique 21 2.7 Related Analytical Studies and Self-Similarity Issues in Traffic Modelling 23 3. Simulation Model 24 3.1 Outline 24 3.2 Ethernet Simulation Model 25 3.2.1 Discrete Event-Driven Simulator 25 3.2.2 Ethernet Protocol Overview 27 3.2.2.1 Carrier Sensing 29 3.2.2.2 Collision Handling 31 3.2.2.3 Truncated Binary Exponential Back-off Algorithm 32 3.2.3 Packet Flow and Queuing Model 33 3.2.4 Modelling Assumptions 36 3.2.5 Model Development 39 3.2.6 Event Modelling and Statistic Probes 41 3.2.6.1 Event 1: Packet Arrival 42 3.2.6.2 Event 2 : Channel Seizure Attempt 47 3.2.6.3 Event 3: Collision Check 48 ^ 3.2.6.4 Event 4: Packet Departure 49 3.2.7 Simulation Unit Time 51 3.2.8 Random Number Generation 52 3.2.9 Simulation Parameters 53 3.2.10 Output Statistics 56 3.2. 11 Model Features and Limitations 57 3.3 Simulation Model Verification and Validation 60 vi 3.3.1 Simulation Model Verification 60 3.3.1.1 Top-Down Modular Design 61 3.3.1.2 Antibugging 61 3.3.1.3 Event Tracing 61 3.3.1.4 Consistency T est and S eed Independence 62 3.3.2 Simulation Model Validation 62 3.3.2.1 Validation Results 65 3.4 Transient Removal and Stopping Criteria 74 3.5 Summary 76 4. Statistical Methods 77 4.1 Introduction 77 4.2 Graphical Methods 78 4.2.1 Empirical Cumulative Distribution Function (EDF) 79 4.2.2 Q-Q Plot 81 4.3 Formal Method: Kolmogorov-Smimov Test with V Statistic 82 4.4 Second Order Statistics for Testing Self-Similarity in Aggregate Traffic 84 4.4.1 Self-Similar Stochastic Processes 84 4.4.2 Index of Dispersion for Count (IDC) 87 4.4.3 Variance Time Plot (VTP) 88 4.5 Summary 89 5. Testing The Null Hypothesis: Simulation and Analysis 91 5.1 Introduction 91 5.2 Experimental Methodology 92 5.3 Traffic Source Models and Packet Size Distributions 97 5.3.1 Packet Size Distributions 100 5.4 Characteristics of Simulation Aggregate Traffic Data 101 5.4.1 Characteristics of Simulation Aggregate Data with Poisson Traffic Model 101 vii 5.4.2 Characteristics of Simulation Aggregate Data with Pareto Traffic Model 104 5.5. The Null Hypothesis Test Results for the Poisson Traffic Model 107 5.5.1 Summary of General Statistics 109 5.5.2 Ho’s Sensitivity against Network Parameters 112 5.5.3 Ho’s Sensitivity against Protocol Parameter 118 5.5.4 Ho’ s Sensitivity against User Parameters 119 5.6. The Null Hypothesis Test Results for the Pareto Traffic Model 120 5.6.1 Summary of General Statistics 122 5.6.2 Ho’s Sensitivity against Network Parameters 124 5.6.3 Ho’s Sensitivity against Protocol Parameters 129 5.6.4 Ho’s Sensitivity against User Parameters 130 5.6.5 Ho’s Sensitivity against Pareto Shape Parameters 130 5.7 Summary 134 6. LAN Traffic Measurement and Modelling: A Case Study 137 6.1 Introduction 137 6.2 Previous Study Review 138 6.3 Traffic Measurement and Analysis 141 6.3.1 Self-similarity in Measured Traffic Data 144 6.3.2 Packet Size Distribution 146 6.4 Simulation and Analysis 148 6.5 Summary 153 7.