Traffic measurement and analysis

Measurement and analysis of real traffic is important to gain knowledge about the characteristics of the traffic. Without measurement, it is impossible to build realistic traffic models. It is recent that data traffic was found to have self-similar properties. In this thesis work traffic captured on the network at SICS and on the Supernet, is shown to have this fractal-like behaviour. The traffic is also examined with respect to which protocols and packet sizes are present and in what proportions. In the SICS trace most packets are small, TCP is shown to be the predominant transport protocol and NNTP the most common application. In contrast to this, large UDP packets sent between not well-known ports dominates the Supernet traffic. Finally, characteristics of the client side of the WWW traffic are examined more closely. In order to extract useful information from the packet trace, web browsers use of TCP and HTTP is investigated including new features in HTTP/1.1 such as persistent connections and pipelining. Empirical probability distributions are derived describing session lengths, time between user clicks and the amount of data transferred due to a single user click. These probability distributions make up a simple model of WWW-sessions

Self-similar traffic and network dynamics

Copyright © 2002 IEEEOne of the most significant findings of traffic measurement studies over the last decade has been the observed self-similarity in packet network traffic. Subsequent research has focused on the origins of this self-similarity, and the network engineering significance of this phenomenon. This paper reviews what is currently known about network traffic self-similarity and its significance. We then consider a matter of current research, namely, the manner in which network dynamics (specifically, the dynamics of transmission control protocol (TCP), the predominant transport protocol used in today's Internet) can affect the observed self-similarity. To this end, we first discuss some of the pitfalls associated with applying traditional performance evaluation techniques to highly-interacting, large-scale networks such as the Internet. We then present one promising approach based on chaotic maps to capture and model the dynamics of TCP-type feedback control in such networks. Not only can appropriately chosen chaotic map models capture a range of realistic source characteristics, but by coupling these to network state equations, one can study the effects of network dynamics on the observed scaling behavior. We consider several aspects of TCP feedback, and illustrate by examples that while TCP-type feedback can modify the self-similar scaling behavior of network traffic, it neither generates it nor eliminates it.Ashok Erramilli, Matthew Roughan, Darryl Veitch and Walter Willinge

Using Markov chains for modelling networks

The paper contains the review and the discussion on modelling communication networks with the use of queuing models and Markov chains. It shows how to take into account various characteristics of real systems - like some control mechanisms and the traffic self-similarity. There are presented two mechanisms modelled with Markov chains: the RED algorithm in TCP/IP and a self-similar traffic shaping

BPTraSha: A Novel Algorithm for Shaping Bursty Nature of Internet Traffic

Various researchers have reported that traffic measurements demonstrate considerable burstiness on several time scales, with properties of self-similarity. Also, the rapid development of technologies has widened the scope of network and Internet applications and, in turn, increased traffic. The self-similar nature of this data traffic may exhibit spikiness and burstiness on large scales with such behaviour being caused by strong dependence characteristics in data: that is, large values tend to come in clusters and clusters of clusters and so on. Several studies have shown that TCP, the dominant network (Internet) transport protocol, contributes to the propagation of self-similarity. Bursty traffic can affect the Quality of Service of all traffic on the network by introducing inconsistent latency. It is easier to manage the workloads under less bursty (i.e. smoother) conditions. In this paper, we introduce a novel algorithm for traffic shaping, which can smooth out the traffic burstiness. We name it the Bursty Packet Traffic Shaper (BPTraSha). Experimental results show that this approach allows significant traffic control by smoothing the incoming traffic. BPTraSha can be implemented on the distribution router buffer so that the traffic's bursty nature can be modified before it is transmitted over the core network (Internet)

Managing the Bursty Nature of Packet Traffic using the BPTraSha Algorithm

The rapid development of network technologies has widened the scope of Internet applications and, in turn, increased both Internet traffic and the need for its accurate measurement, modelling and control. Various researchers have reported that traffic measurements demonstrate considerable burstiness on several time scales, with properties of self-similarity. The self-similar nature of this data traffic may exhibit spikiness and burstiness on large scales with such behaviour being caused by strong dependence characteristics in data: that is, large values tend to come in clusters and clusters of clusters and so on. Several studies have shown that TCP, the dominant network (Internet) transport protocol, contributes to the propagation of self-similarity. Bursty traffic can affect the Quality of Service of all traffic on the network by introducing inconsistent latency. It is easier to manage the workloads under less bursty (i.e. smoother) conditions. In this paper, we examine the use of a novel algorithm, the Bursty Packet Traffic Shaper (BPTraSha), for traffic shaping, which can smooth out the traffic burstiness. Experimental results show that this approach allows significant traffic control by smoothing the incoming traffic. BPTraSha can be implemented on the distribution router buffer so that the traffic’s bursty nature can be modified before it is transmitted over the core network

Performance of TCP over ABR with Long-Range Dependent VBR Background Traffic over Terrestrial and Satellite ATM networks

Compressed video is well known to be self-similar in nature. We model VBR carrying Long-Range Dependent (LRD), multiplexed MPEG-2 video sources. The actual traffic for the model is generated using fast-fourier transform of generate the fractional gaussian noise (FGN) sequence. Our model of compressed video sources bears similarity to an MPEG-2 Transport Stream carrying video, i.e., it is long-range dependent and generates traffic in a piecewise-CBR fashion. We study the effect of such VBR traffic on ABR carrying TCP traffic. The effect of such VBR traffic is that the ABR capacity is highly variant. We find that a switch algorithm like ERICA+ can tolerate this variance in ABR capacity while maintaining high throughput and low delay. We present simulation results for terrestrial and satellite configurations.Comment: Proceedings of LCN `9

Self-generated Self-similar Traffic

Self-similarity in the network traffic has been studied from several aspects: both at the user side and at the network side there are many sources of the long range dependence. Recently some dynamical origins are also identified: the TCP adaptive congestion avoidance algorithm itself can produce chaotic and long range dependent throughput behavior, if the loss rate is very high. In this paper we show that there is a close connection between the static and dynamic origins of self-similarity: parallel TCPs can generate the self-similarity themselves, they can introduce heavily fluctuations into the background traffic and produce high effective loss rate causing a long range dependent TCP flow, however, the dropped packet ratio is low.Comment: 8 pages, 12 Postscript figures, accepted in Nonlinear Phenomena in Complex System

The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is the most complex system ever created in human history. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, large-scale effects of worm traffic, and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex System

A critical look at power law modelling of the Internet

This paper takes a critical look at the usefulness of power law models of the Internet. The twin focuses of the paper are Internet traffic and topology generation. The aim of the paper is twofold. Firstly it summarises the state of the art in power law modelling particularly giving attention to existing open research questions. Secondly it provides insight into the failings of such models and where progress needs to be made for power law research to feed through to actual improvements in network performance.Comment: To appear Computer Communication