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Atraffic generation model is a stochastic model of thetraffic flows or data sources in acommunication network, for example a cellular network or a computer network. Apacket generation model is a traffic generation model of thepacket flows or data sources in apacket-switched network. For example, aweb traffic model is a model of the data that is sent or received by a user'sweb-browser. These models are useful during the development of telecommunication technologies, in view to analyse the performance and capacity of various protocols, algorithms and network topologies .
The network performance can be analyzed bynetwork traffic measurement in atestbed network, using anetwork traffic generator such asiperf,bwping andMausezahn. The traffic generator sends dummy packets, often with a unique packet identifier, making it possible to keep track of the packet delivery in the network.
Numerical analysis usingnetwork simulation is often a less expensive approach.
An analytical approach usingqueueing theory may be possible for a simplified traffic model but is often too complicated if a realistic traffic model is used.
A simplified packet data model is thegreedy source model. It may be useful in analyzing themaximum throughput forbest-effort traffic (without any quality-of-service guarantees). Many traffic generators are greedy sources.
Another simplified traditional traffic generation model for packet data, is thePoisson process, where the number of incoming packets and/or the packet lengths are modeled as anexponential distribution. When the packets interarrival time is exponential, with constant packet size it resembles an M/D/1 system. When both packet inter arrivals and sizes are exponential, it is an M/M/1 queue.
However, the Poisson traffic model is memoryless, which means that it does not reflect thebursty nature of packet data, also known as thelong-range dependency. For a more realistic model, aself-similar process such as thePareto distribution can be used as along-tail traffic model.
The actual content of the payload data is typically not modeled, but replaced by dummy packets. However, if the payload data is to be analyzed on the receiver side, for example regardingbit-error rate, aBernoulli process is often assumed, i.e. a random sequence of independent binary numbers. In this case, achannel model reflects channel impairments such as noise, interference and distortion.
One of the3GPP2 models is described in.[1] This document describes the following types of traffic flows:
The main idea is to partly implement HTTP, FTP and TCPprotocols. For example, an HTTP traffic generator simulates the download of a web-page, consisting of a number of small objects (like images). A TCP stream (that's why TCP generator is a must in this model) is used to download these objects according to HTTP1.0 or HTTP1.1 specifications. These models take into account the details of these protocols' work. The Voice, WAP and Mobile Network Gaming are modelled in a less complicated way.