## 14.8. 14.8 Modelling of traffic burstiness

Recent measurements of local area network traffic and wide-area network traffic have proved that the widely used Markovian process models cannot be applied for today's network traffic. If the traffic were a Markovian process, the traffic's burst length would be smoothed by averaging over a long time scale, contradicting the observations of today's traffic characteristics. Measurements of real traffic also prove that traffic burstiness is present on a wide range of time scales. Traffic that is bursty on many or all time scales can be characterised statistically using the concept of self-similarity. Selfsimilarity is often associated with objects in fractal geometry, objects that appear to look alike regardless of the scale at which they are viewed. In case of stochastic processes like time series, the term self-similarity refers to the process' distribution, which, when viewed at varying time scales, remains the same. Self-similar time series has noticeable bursts, which have long periods with extremely high values on all time scales. Characteristics of network traffic, such as packets/sec, bytes/sec, or length of frames, can be considered as stochastic time series. Therefore, measuring traffic burstiness is the same as characterising the self-similarity of the corresponding time series.

The self-similarity of network traffic has also been observed in studies in numerous papers. These and other papers show that packet loss, buffer utilisation, and response time are totally different when simulations use either real traffic data or synthetic data that include self-similarity.

#### Background.

Let be a covariance stationary stochastic process. Such a process has a constant mean , finite variance , and an autocorrelation function , that depends only on . It is assumed that has an autocorrelation function of the form:

where and is a positive constant. Let represent a new time series obtained by averaging the original series over nonoverlapping blocks of size . For each is specified by . Let denote the autocorrelation function of the aggregated time series .

#### Definition of self-similarity.

The process called exactly self-similar with self-similarity parameter if the corresponding aggregated processes have the same correlation structure as , i.e. for all .

A covariance stationary process is called asymptotically self-similar with self-similarity parameter , if for all large enough , as , .

#### Definition of long-range dependency.

A stationary process is called long-range dependent if the sum of the autocorrelation values approaches infinity: . Otherwise, it is called short-range dependent. It can be derived from the definitions that while short-range dependent processes have exponentially decaying autocorrelations, the autocorrelations of long-range dependent processes decay hyperbolically; i.e., the related distribution is heavy-tailed. In practical terms, a random variable with heavy-tail distribution generates extremely large values with high probability. The degree of self-similarity is expressed by the parameter or Hurst-parameter. The parameter represents the speed of decay of a process' autocorrelation function. As the extent of both self-similarity and long-range dependence increases. It can also be shown that for self-similar processes with long-range dependency .

#### Traffic models.

Traffic modelling originates in traditional voice networks. Most of the models have relied on the assumption that the underlying processes are Markovian (or more general, short-range dependent). However, today's high-speed digital packet networks are more complex and bursty than traditional voice traffic due to the diversity of network services and technologies.

Several sophisticated stochastic models have been developed as a reaction to new developments, such as Markov-modulated Poisson processes, fluid flow models, Markovian arrival processes, batched Markovian arrival process models, packet train models, and Transform-Expand-Sample models. These models mainly focus on the related queueing problem analytically. They are usually not compared to real traffic patterns and not proven to match the statistical property of actual traffic data.

Another category of models attempts to characterise the statistical properties of actual traffic data. For a long time, the area of networking research has lacked adequate traffic measurements. However, during the past years, large quantities of network traffic measurements have become available and collected in the Web and high-speed networks. Some of these data sets consist of high-resolution traffic measurements over hours, days, or weeks. Other data sets provide information over time periods ranging from weeks to months and years. Statistical analyses of these high time-resolution traffic measurements have proved that actual traffic data from packet networks reveal self-similarity. These results point out the difference between traditional models and measured traffic data. While the assumed processes in traditional packet traffic models are short-range dependent, measured packet traffic data show evidence of long-range dependency. Figure 14.22 illustrates the difference between Internet traffic and voice traffic for different numbers of aggregated users. As the number of voice flows increases, the traffic becomes more and more smoothed contrary to the Internet traffic.

Quite the opposite to the well developed field of short-range dependent queueing models, fewer theoretical results exist for queueing systems with long-range dependence. For some of the results. In terms of modelling, the two major groups of self-similar models are fractional Gaussian noises and fractional ARIMA processes. The Gaussian models accurately represent aggregation of many traffic streams. Another well-known model, the M/Pareto model has been used in modelling network traffic that is not sufficiently aggregated for the Gaussian model to apply.

#### Black box vs. structural models.

We share the opinion calling the approach of traditional time series analysis as black box modelling as opposite to the structural modelling that concentrates on the environment in which the models' data was collected; i.e., the complex hierarchies of network components that make up today's communications systems. While the authors admit that black box models can be and are useful in other contexts, they argue that black box models are of no use for understanding the dynamic and complex nature of the traffic in modern packet networks. Black box models have not much use in designing, managing and controlling today's networks either. In order to provide physical explanations for empirically observed phenomena such as long-range dependency, we need to replace black box models with structural models. The attractive feature of structural traffic models is that they take into account the details of the layered architecture of today's networks and can analyse the interrelated network parameters that ultimately determine the performance and operation of a network. Time series models usually handle these details as black boxes. Because actual networks are complex systems, in many cases, black box models assume numerous parameters to represent a real system accurately. For network designers, who are important users of traffic modelling, black box models are not very useful. It is rarely possible to measure or estimate the model's numerous parameters in a complex network environment. For a network designer, a model ought to be simple, meaningful in a particular network. It can relay on actual network measurements, and the result ought to be relevant to the performance and the operation of a real network.

For a long time, traffic models were developed independently of traffic data collected in real networks. These models could not be applied in practical network design. Today the availability of huge data sets of measured network traffic and the increasing complexity of the underlying network structure emphasise the application of the Ockham' Razer in network modelling. (Ockham's Razor is a principle of the mediaeval philosopher William Ockham. According to his principle, modellers should not make more assumptions than the minimum needed. This principle is also called the Principle of Parsimony and motivates all scientific modelling and theory building. It states that modellers should choose the simplest model among a set of otherwise equivalent models of a given phenomenon. In any given model, Ockham's Razor helps modellers include only those variables that are really needed to explain the phenomenon. Following the principle, model development will become easier, reducing the possibilities for inconsistencies, ambiguities and redundancies.)

Structural models are presented, for instance in different papers, which demonstrate how the self-similar nature of aggregated network traffic of all conversations between hosts explains the details of the traffic dynamics at the level generated by the individual hosts. The papers introduce structural traffic models that have a physical meaning in the network context and underline the predominance of long-range dependence in the packet arrival patterns generated by the individual conversations between hosts. The models provide insight into how individual network connections behave in local and wide area networks. Although the models go beyond the black box modelling methodology by taking into account the physical structure of the aggregated traffic patterns, they do not include the physical structure of the intertwined structure of links, routers, switches, and their finite capacities along the traffic paths.

Crovella and Stavros demonstrated that World Wide Web traffic shows characteristics that are consistent with self-similarity. They show that transmission times may be heavy tailed, due to the distribution of available file sizes in the Web. It is also shown that silent times may also be heavy-tailed; primarily due to the effect of user “think time”. Similarly to the structural models due to Willinger at al., their paper lacks of analysing the impact of selfsimilar traffic on the parameters of the links and the routers' buffers that ultimately determine a network's performance.

This chapter describes a traffic model that belongs to the structural model category above. We implement the M/Pareto model within the discrete event simulation package COMNET that allows the analysis of the negative impact of self-similar traffic on not just one single queue, but on the overall performance of various interrelated network components, such as link, buffers, response time, etc. The commercially available package does not readily provide tools for modelling self-similar, long-range dependent network traffic. The model-generated traffic is based on measurements collected from a real ATM network. The choice of the package emphasises the need for integrated tools that could be useful not just for theoreticians, but also for network engineers and designers. Our paper intends to narrow the gap between existing, well-known theoretical results and their applicability in everyday, practical network analysis and modelling. It is highly desirable that appropriate traffic models should be accessible from measuring, monitoring, and controlling tools. Our model can help network designers and engineers, the ultimate users of traffic modelling, understand the dynamic nature of network traffic and assist them to design, measure, monitor, and control today's complex, high-speed networks in their everyday's practice.

#### Implications of burstiness on high-speed networks.

Various papers discuss the impact of burstiness on network congestion. Their conclusions are:

• Congested periods can be quite long with losses that are heavily concentrated.

• Linear increases in buffer size do not result in large decreases in packet drop rates.

• A slight increase in the number of active connections can result in a large increase in the packet loss rate.

Results show that packet traffic “spikes” (which cause actual losses) ride on longerterm “ripples”, which in turn ride on still longer-term “swells”.

Another area where burstiness can affect network performance is a link with priority scheduling between classes of traffic. In an environment, where the higher priority class has no enforced bandwidth limitations (other than the physical bandwidth), interactive traffic might be given priority over bulk-data traffic. If the higher priority class is bursty over long time scales, then the bursts from the higher priority traffic could obstruct the lower priority traffic for long periods of time.

The burstiness may also have an impact on networks where the admission control mechanism is based on measurements of recent traffic, rather than on policed traffic parameters of individual connections. Admission control that considers only recent traffic patterns can be misled following a long period of fairly low traffic rates.

### 14.8.1. 14.8.1 Model parameters

Each transaction between a client and a server consists of active periods followed by inactive periods. Transactions consist of groups of packets sent in each direction. Each group of packets is called a burst. The burstiness of the traffic can be characterised by the following time parameters:

• Transaction Interarrival Time (TIAT): The time between the first packet in a transaction and the first packet of the next immediate transaction.

• Burst Interarrival Time, , arrival rate of bursts: The time between bursts.

• Packet Interarrival Time, , : arrival rate of packets: The time between packets in a burst.

#### 14.8.1.1.  The Hurst parameter.

It is anticipated that the rapid and ongoing aggregation of more and more traffic onto integrated multiservice networks will eventually result in traffic smoothing. Once the degree of aggregation is sufficient, the process can be modelled by Gaussian process. Currently, network traffic does not show characteristics that close to Gaussian. In many networks the degree of aggregation is not enough to balance the negative impact of bursty traffic. However, before traffic becomes Gaussian, existing methods can still provide accurate measurement and prediction of bursty traffic.

Most of the methods are based on the estimate of the Hurst parameter - the higher the value of H, the higher the burstiness, and consequently, the worse the queueing performance of switches and routers along the traffic path. Some are more reliable than others. The reliability depends on several factors; e.g., the estimation technique, sample size, time scale, traffic shaping or policing, etc. Based on published measurements we investigated methods with the smallest estimation error*.

Footnote. Variance, Aggregated Variance, Higuchi, Variance of Residuals, Rescaled Adjusted Range (R/S), Whittle Estimator, Periodogram, Residuals of Regression.

Among those, we chose the Rescaled Adjusted Range (R/S) method because we found it implemented in the Benoit package. The Hurst parameter calculated by the package is input to our method.

#### 14.8.1.2.  The M/Pareto traffic model and the Hurst parameter.

Recent results have proven that the M/Pareto model is appropriate for modelling long-range dependent traffic flow characterised by long bursts. Originally, the model was introduced and applied in the analysis of ATM buffer levels. The M/Pareto model was also used to predict the queueing performance of Ethernet, VBR video, and IP packet streams in a single server queue. We apply the M/Pareto model not just for a single queue, but also for predicting the performance of an interconnected system of links, switches and routers affecting the individual network elements' performance.

The M/Pareto model is a Poisson process of overlapping bursts with arrival rate . A burst generates packets with arrival rate . Each burst, from the time of its interval, will continue for a Pareto-distributed time period. The use of Pareto distribution results in generating extremely long bursts that characterise long-range dependent traffic.

The probability that a Pareto-distributed random variable exceeds threshold is:

The mean of , the mean duration of a burst and its variance is infinite. Assuming a time interval, the mean number of packets in the time interval is:

where

The M/Pareto model is asymptotically self-similar and it is shown that for the Hurst parameter the following equation holds:

### 14.8.2. 14.8.2 Implementation of the Hurst parameter

We implemented the Hurst parameter and a modified version of the M/Pareto model in the discrete event simulation system COMNET. By using discrete event simulation methodology, we can get realistic results in measuring network parameters, such as utilisation of links and the queueing performance of switches and routers. Our method can model and measure the harmful consequences of aggregated bursty traffic and predict its impact on the overall network's performance.

#### 14.8.2.1.  Traffic measurements.

In order to build the baseline model, we collected traffic traces in a large corporate network by the Concord Network Health network analyser system. We took measurements from various broadband and narrow band links including 45Mbps ATM, 56Kbps, and 128 Kbps frame relay connections. The Concord Network Health system can measure the traffic in certain time intervals at network nodes, such as routers and switches. We set the time intervals to 6000 seconds and measured the number of bytes and packets sent and received per second, packet latency, dropped packets, discard eligible packets, etc. Concord Network Health cannot measure the number of packets in a burst and the duration of the bursts as it is assumed in the M/Pareto model above. Due to this limitation of our measuring tool, we slightly modify our traffic model according to the data available. We took snapshots of the traffic in every five minutes from a narrow band frame relay connection between a remote client workstation and a server at the corporate headquarters as traffic destination in the following format:

The mean number of bytes, the message delay from the client to server, the input buffer level at the client's local router, the number of blocked packets, the mean utilisations of the 56Kbps frame relay, the DS-3 segment of the ATM network, and the 100Mbps Ethernet link at the destination are summarised in Figure 14.24.

COMNET represents a transaction by a message source, a destination, the size of the message, communication devices, and links along the path. The rate at which messages are sent is specified by an interarrival time distribution, the time between two consecutive packets. The Poisson distribution in the M/Pareto model generates bursts or messages with arrival rate , the number of arrivals, which are likely to occur in a certain time interval. In simulation, this information is expressed by the time interval between successive arrivals . For this purpose, we use the Exponential distribution. Using the Exponential distribution for interarrival time will result in an arrival pattern characterised by the Poisson distribution. In COMNET, we implemented the interarrival time with the function Exp(). The interarrival time in the model is set to one second matching the sampling time interval set in Concord Network Health and corresponding to an arrival rate /sec.

In the M/Pareto model, each burst continues for a Pareto-distributed time period. The Concord Network Health cannot measure the duration of a burst; hence, we assume that a burst is characterised by the number of bytes in a message sent or received in a second. Since the ATM cell rate algorithm ensures that equal length messages are processed in equal time, then longer messages require longer processing time. So we can say that the distribution of the duration of bursts is the same as the distribution of the length of bursts. Hence, we can modify the M/Pareto model by substituting the Pareto-distributed duration of bursts with the Pareto-distributed length of bursts. We derive of the Pareto distribution not from the mean duration of bursts, but from the mean length of bursts.

The Pareto distributed length of bursts is defined in COMNET by two parameters- the location and the shape. The location parameter corresponds to the , the shape parameter corresponds to the parameter of the M/Pareto model in (1) and can be calculated from the relation (4) as

The Pareto distribution can have infinite mean and variance. If the shape parameter is greater than 2, both the mean and variance are finite. If the shape parameter is greater than 1, but less than or equal to 2, the mean is finite, but then the variance is infinite. If the shape parameter is less than or equal to 1, both the mean and variance are infinite.

From the mean of the Pareto distribution we get:

The relations (5) and (6) allow us to model bursty traffic based on real traffic traces by performing the following steps:

• a. Collect traffic traces using the Concord Network Health network analyser.

• b. Compute the Hurst parameter by making use of the Benoit package with the traffic trace as input.

• c. Use the Exponential and Pareto distributions in the COMNET modelling tool with the parameters calculated above to specify the distribution of the interarrival time and length of messages.

• d. Generate traffic according to the modified M/Pareto model and measure network performance parameters.

The traffic generated according to the steps above is bursty with parameter H calculated from real network traffic.

### 14.8.3. 14.8.3 Validation of the baseline model

We validate our baseline model by comparing various model parameters of a 56Kbps frame relay and a 6Mbps ATM connection with the same parameters of a real network as the Concord Network Health network analyser traced it. For simplicity, we use only the “Bytes Total/sec” column of the trace, i.e., the total number of bytes in the “Bytes Total/sec” column is sent in one direction only from the client to the server. The Hurst parameter of the real traffic trace is calculated by the Benoit package. The topology is as follows:

The “Message sources” icon is a subnetwork that represents a site with a token ring network, a local router, and a client sending messages to the server in the “Destination” subnetwork:

The interarrival time and the length of messages are defined by the Exponential and Pareto functions Exp (1) and Par (208.42, 1.9) respectively. The Pareto distribution's location (208.42) and shape (1.9) are calculated from formulas (5) and (6) by substituting the mean length of bursts (440 bytes from Table 2.) and .

The corresponding heavy-tailed Pareto probability distribution and cumulative distribution functions are illustrated in Figure 14.28 (The represents the number of bytes):

The “Frame Relay” icon represents a frame relay cloud with 56K committed information rate (CIR). The “Conc” router connects the frame relay network to a 6Mbps ATM network with variable rate control (VBR) as shown in Figures 14.29 and 14.30:

The “Destination” icon denotes a subnetwork with server :

The results of the model show almost identical average for the utilisation of the frame relay link () and the utilisation of the real measurements (3.1%):

The message delay in the model is also very close to the measured delay between the client and the server (78 msec):

The input buffer level of the remote client's router in the model is almost identical with the measured buffer level of the corresponding router:

Similarly, the utilisations of the model's DS-3 link segment of the ATM network and the Ethernet link in the destination network closely match with the measurements of the real network:

It can also be shown from the model's traffic trace that for the model generated messages the Hurst parameter , i.e., the model generates almost the same bursty traffic as the real network. Furthermore, the number of dropped packets in the model was zero similarly to the number of dropped packets in the real measurements. Therefore, we start from a model that closely represents the real network.

### 14.8.4. 14.8.4 Consequences of traffic burstiness

In order to illustrate our method, we developed a COMNET simulation model to measure the consequences of bursty traffic on network links, message delays, routers' input buffers, and the number of dropped packets due to the aggregated traffic of large number of users. The model implements the Hurst parameter as it has been described in Section 3. We repeated the simulation for 6000 sec, 16000 sec and 18000 sec to allow infrequent events to occur a reasonable number of times. We found that the results are very similar in each simulation.

#### 14.8.4.1.  Topology of bursty traffic sources.

The “Message Source” subnetworks transmit messages as in the baseline model above, but with different burstiness: , and with fixed size. Initially, we simulate four subnetworks and four users per subnetwork each sending the same volume of data (mean 440 bytes per second) as in the validating model above:

#### 14.8.4.2.  Link utilisation and message delay.

First, we are going to measure and illustrate the extremely high peaks in frame relay link utilisation and message delay. The model traffic is generated with message sizes determined by various Hurst parameters and fixed size messages for comparison. The COMNET modelling tool has a trace option to capture its own model generated traffic. It has been verified that for the model-generated traffic flows with various Hurst parameters the Benoit package computed similar Hurst parameters for the captured traces.

The following table shows the simulated average and peak link utilisation of the different cases. The utilisation is expressed in the [0, 1] scale not in percentages:

The enclosed charts in Appendix A clearly demonstrate that even though the average link utilisation is almost identical, the frequency and the size of the peaks increase with the burstiness, causing cell drops in routers and switches. We received the following results for response time measurements:

The charts in the Appendix A graphically illustrate the relation between response times and various Hurst parameters.

#### 14.8.4.3.  Input buffer level for large number of users.

We also measured the number of cells dropped at a router's input buffer in the ATM network due to surge of bursty cells. We simulated the aggregated traffic of approximately 600 users each sending the same number of bytes in a second as in the measured real network. The number of blocked packets is summarised in the following table:

### 14.8.5. 14.8.5 Conclusion

Theis chapter presented a discrete event simulation methodology to measure various network performance parameters while transmitting bursty traffic. It has been proved in recent studies that combining bursty data streams will also produce bursty combined data flow. The studies imply that the methods and models used in traditional network design require modifications. We categorise our modelling methodology as a structural model contrary to a black box model. Structural models focus on the environment in which the models' data was collected; i.e., the complex hierarchies of network components that make up today's communications systems. Although black box models are useful in other contexts, they are not easy to use in designing, managing and controlling today's networks. We implemented a well-known model, the M/Pareto model within the discrete event simulation package COMNET that allows the analysis of the negative impact of self-similar traffic on not just one single queue, but on the overall performance of various interrelated network components as well. Using real network traces, we built and validated a model by which we could measure and graphically illustrate the impact of bursty traffic on link utilisation, message delays, and buffer performance of Frame Relay and ATM networks. We illustrated that increasing burstiness results in extremely high link utilisation, response time, and dropped packets, and measured the various performance parameters by simulation.

The choice of the package emphasises the need for integrated tools that could be useful not just for theoreticians, but also for network engineers and designers. Our paper intends to narrow the gap between existing, well-known theoretical results and their applicability in everyday, practical network analysis and modelling. It is highly desirable that appropriate traffic models should be accessible from measuring, monitoring, and controlling tools. Our model can help network designers and engineers, the ultimate users of traffic modelling, understand the dynamic nature of network traffic and assist them in their everyday practice.