Survey of Network Traffic Models

Balakrishnan Chandrasekaran, bchandrasekaran@wustl.edu

Abstract
The need for communication networks capable of providing an ever increasing spectrum of services calls for efficient techniques for the analysis, monitoring, evaluation and design of the networks. Analysis are perpetually faced with incomplete & ever increasing user demands and uncertainty about the evolution of the network systems. To meet the requirements of users and to provide guarantees on reliability & affordability, system models must be developed that can capture the characteristics of the actual network load and yield acceptable precise predictions of performance of the system, in a reasonable amount of time. Traffic Analysis is a vital component to understand the requirements and capabilities of a network. The past years have seen innumerous traffic models proposed for understanding and analyzing the traffic characteristics of networks. Nevertheless, there is no single traffic model that can efficiently capture the traffic characteristics of all types of networks, under every possible circumstance. Consequently, the study of traffic models to understand the features of the models and identify eventually the best traffic model, for a concerned environment has become a crucial and lucrative task. Good traffic modeling is also a basic requirement for accurate capacity planning. This report attempts to provide an overview of some of the widely used network traffic models, highlighting the core features of the model and traffic characteristics they capture best.
Keywords: Traffic Models, Poisson, Pareto, Weibull, Markov, Markov Chain, ON-OFF model, Interrupted Poisson, Fluid Model, Alternating State Renewal Process, Autoregressive Models, Network, Queueing, Performance.

Table of Contents

  1. Introduction

  2. Need for Traffic Models

    2.1. Network Performance Management

    2.2. QOS Guarantees

  3. Traffic Models

    3.1. Poisson Distribution Model

    3.2. Pareto Distribution Process

    3.3. Weibull Distribution Process

    3.4. Markov and Embedded Markov Models

        3.4.1. ON-OFF and IPP Models

        3.4.2. Alternating State Renewal Process

        3.4.3. Markov Modulated Poisson Process

        3.4.4. Markov Modulated Fluid Models

    3.5 Autoregressive Models

  4. Summary

  5. References

  6. List of Acronyms

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