Based on distribution of interarrival times, a renewal process can generate counts which can exhibit short-range or long-range dependence. If the tail of interarrival time distribution
2.7 Renewal Processes and Long-Range Dependence 41
decays quickly like the exponential distribution, then the resulting counts will be short- range dependent. whereas, if the interarrival time distribution is heavy-tailed, then the corresponding counts will exhibit long-range dependence [Daley et al., 2000]. Such renewal processes are also termed as fractal renewal processes in [Ryu & Lowen,1996]. The autocorrelation function of a fractal renewal process decays slower than that of the Poisson process.
An example of a fractal renewal process is the Pareto renewal process in which interarrival times are drawn from Pareto distribution with infinite mean and infinite variance (that is, tail index range of 0<α <1. Such a process is fully fractal as the corresponding counting process exhibits second-order self-similar behaviour at all time scales. If the interarrival times are Pareto distributed with finite mean and infinite variance (that is , the tail index range is 1 < α < 2), then the resulting counting process, again, exhibits second-order self-similar behaviour or scaling over a range of time scales.
The renewal processes based on an interarrival time distribution with infinite mean can have a zero rate of arrivals. This means that once such a process reaches its steady state, the probability of no arrival in fixed time intervals will be 1, irrespective of the value of the scale parameter (the minimum value). Therefore, for such a renewal process, the autocorrelation function will be zero at all lags, and hence will be summable. This violates the condition for a process to be second-order self-similar or long-range dependent (see Equation2.14). Nevertheless, the Pareto renewal process with infinite or finite mean and infinite variance has been used as a model of self-similar traffic [Gordon,1995]. This can be justified using the following reasoning: That is, such a renewal process can be conditioned to start from an event, that is, there should be an event at time t =0. Therefore, a finite segment of such a renewal process contains a positive number of arrivals with the density of the number of renewals decreasing (page 373, [Feller,1971]) . In other words, such a renewal process should be considered over a finite time interval, that is, before it enters its steady state [Paxson & Floyd,1995].
The following theorem is due to [Daley,1999].
Theorem 2 A renewal process with interarrival time distribution function F that hasR∞
0 x2dF(x) =
∞,R∞
0 xdF(x) < ∞ and moment index α, is long-range dependent and has Hurst parameter (H) given as
H = 3−α
It should be noted that if the interarrival times follow the heavy-tailed Weibull distribution (that is, shape parameter less than one) then the resulting counting process also displays fractal like characteristics, for example, overdispersion and irregularity [Yannaros,1994]. The statistical characteristics of counts resulting from the heavy-tailed Weibull renewal process and their applicability in Internet traffic modelling will be discussed in Chapters 4 and 5.
2.8
Summary of the Chapter
The modelling of count data can be done independent of underlying interarrival time data. In case of Internet traffic, an example of such a model is fractional Gaussian noise, which models autocorrelation structure of count data. There can be various stochastically different realizations of interarrival times corresponding to such count models.
Renewal processes offers a joint modelling framework for interarrival times and the asso- ciated count data. Long-range dependence in count data refer to strong autocorrelations in count data or slow decay of its autocorrelation function. Certain renewal processes based on heavy-tailed interarrival times can generate counts which can exhibit long-range dependence. Therefore, renewal processes have a great potential in modelling Internet traffic.
Except Poisson processes, the superposition of renewal processes results in a non-renewal process due to the dependencies between interarrival times. Internet traffic is a superpo- sition of traffic streams from various users. Therefore, the theory of superposition can be used to study the stochastic behaviour of Internet traffic. Appropriate renewal processes can also be used to approximate the non-renewal superposed process.
Chapter 3
Interarrival Time Models for Internet
Traffic
“
If intervals between successive points are judged important, it will be naturalto look for renewal process, or extensions thereof.
”
D. R. Cox, 19803.1
Introduction
Internet traffic is essentially a superposition or an active mixture of various traffic streams which are stochastic in nature. These traffic streams can originate from various users subscribing to the Internet via different access networks or from various short haul links traversing to a backbone link. Structurally, the traffic in the Internet consists of packets, flows and sessions. The study of interarrival times of structural components of Internet traffic is interesting due to the following reasons:
• Specification of interarrival time distribution is an essential part of any network performance evaluation study based on queueing models.
• Models of interarrival time data can produce count data as a series of counts in any desired duration of time aggregation. The converse is not possible, of course.
• Investigating an appropriate interarrival time distribution which can model interarrival times of traffic belonging to a typical user can help us to invoke related superposition theorems to assess properties of the superposed process. The superposed process can be used as a model for interarrival times and counts of aggregated Internet traffic.
• A superposition model based on interarrival times can be parametrized to reflect properties of packets, flows and sessions in Internet traffic.
Nevertheless, the study of interarrival times is challenging due to the following rea- sons:
• Interarrival times in an Internet traffic stream may or may not be independent, depending on the intensity and stochastic behaviour of the component streams or user traffic.
• The dependencies in the interarrival times cannot be captured by simple renewal process (e.g., Poisson) based modelling. Nevertheless, renewal processes based on heavy-tailed distributions of interarrival times can display a similar dependence structure in counts as that exhibited by Internet traffic.
• Interarrival time processes do not produce summarized or time binned data like count processes. Compared to the data generated by count processes, the size of interarrival time data for analysis is huge and continuous valued.
• Self-similar traffic count models, like fractional Gaussian noise (FGN) and fractional autoregressive integrated moving average (FARIMA) models do not specify the distri- bution or stochastic behaviour of underlying interarrival times.
Our objective in this and the subsequent chapters is focused on developing a simple and unified traffic modelling framework that can model both interarrival time and count processes, at both access and ISP core tiers of Internet’s hierarchy. In this chapter we focus on:
• Theoretical and empirical analysis of a Weibull renewal approximation for the super- position of fractal renewal streams having infinite mean and infinite variance.
• Developing a non-renewal model for the superposition of fractal renewal streams1 with finite mean and infinite variance. We call it a superposed Pareto II model (that is,
1we use the word stream in the sense of a series of interarrival times values. For example, a Pareto renewal
stream (also sometimes called as simply Pareto stream in this thesis), generates a series of interarrival time values being Pareto distributed.