On the Role of Flows and Sessions in Internet Traffic Modeling: An Explorative Toy-Model (original) (raw)

Internet Traffic Measurement: A Critical Study of Wavelet Analysis

IEEE Transactions on Instrumentation and Measurement, 2000

In communication networks, traffic measurements serve two main purposes: the characterization of traffic-load patterns and the monitoring of performances. This paper is a study of the applicability of traffic-analysis methods as a means of detecting malfunctions and performance changes in packet data networks through measurements of selected parameters. The main contribution is of a methodological nature and is motivated by the fact that wavelet analysis, which has come to be regarded as a standard approach, is by no means a straightforward method. Controversial results can be found that are not always attributable to the complex nature of the measured object. Careful study of the uncertainty of traffic-parameter estimates is also an important issue, which turns out to be somewhat neglected in the literature. The aim of this paper is twofold: on the one hand, to enhance the model validation process and, on the other hand, to provide for objective assessment of the feasibility of network-monitoring procedures that rely on measurement and model-based diagnostics.

The impact of the flow arrival process in Internet traffic

2003

Internet packet data is analysed to determine the relationship between the arrival process of packets, and of TCP flows of packets. Viewed as point processes, second order properties of the two processes are studied using wavelets, and each is found to have longrange dependence. A new result is given directly linking flow durations to the onset scale of the long range dependence in the flow process. Using this result a mechanism is described whereby the flow level structure could in principle influence the packet level structure, and it is shown and explained why this is not the case currently. The circumstances under which the flow structure could impact on the packet process, and therefore become important for the modeling of the packet level dynamics, are given.

The multiscale nature of network traffic: Discovery, analysis, and modelling

IEEE Signal …, 2002

The complexity and richness of telecommunications traffic is such that one may despair to find any regularity or explanatory principles. Nonetheless, the discovery of scaling behavior in tele-traffic has provided hope that parsimonious models can be found. The statistics of scaling behavior present many challenges, especially in non-stationary environments. In this paper, we overview the state of the art in this area, focusing on the capabilities of the wavelet transform as a key tool for unravelling the mysteries of traffic statistics and dynamics.

Dynamics of IP traffic

ACM SIGCOMM Computer Communication Review, 1999

Using the ns-2-simulator to experiment with different aspects of user- or session-behaviors and network configurations and focusing on the qualitative aspects of a wavelet-based scaling analysis, we present a systematic investigation into how and why variability and feedback-control contribute to the intriguing scaling properties observed in actual Internet traces (as our benchmark data, we use measured Internet traffic from an

A unified framework for understanding network traffic using independent wavelet models

2002

Abstract Properties of heterogeneous network traffic have been investigated from different aspects, resulting in different understanding. Specifically, one previous work discovers that the variance of network traffic exhibits a linear relationship with respect to the mean. Such a linear relation suggests that the traffic is" Poisson-like", and thus" smooth". On the other hand, prior work has shown that the heterogeneous traffic can be long-range dependent, and is thus bursty.

The changing nature of network traffic

ACM SIGCOMM Computer Communication Review, 1998

In this paper, we report on some preliminary results from an in-depth, wavelet-based analysis of a set of high-quality, packet-level tra c measurements, collected over the last 6-7 years from a number of di erent working wide-area networks (WANs). We rst validate and con rm an earlier nding, originally due to Paxson and Floyd 12], that actual WAN tra c is consistent with statistical self-similarity for su ciently large time scales. We then relate this large-time scaling phenomenon to the empirically observed characteristics of WAN tra c at the level of individual connections or applications. In particular, we present here original results about a detailed statistical analysis of Web-session characteristics, and report on an intriguing scaling property of measured WAN tra c at the transport layer (i.e., number of TCP connection arrivals per time unit). This scaling property of WAN tra c at the TCP layer was absent in the pre-Web period but has become ubiquitous in today's WWW-dominated WANs and is a direct consequence of the ever-increasing popularity of the Web (WWW) and its emergence as the major contributor to WAN tra c. Moreover, we show that this changing nature of WAN tra c can be naturally accounted for by self-similar tra c models, primarily because of their ability to provide physical explanations for empirically observed tra c phenomena in a networking context. Finally, we provide empirical evidence that actual WAN tra c traces also exhibit scaling properties over small time scales, but that the small-time scaling phenomenon is distinctly di erent from the observed large-time scaling property. We relate this newly observed characteristic of WAN tra c to the e ects that the dominant network protocols (e.g., TCP) and controls have on the ow of packets across the network and discuss the potential that multifractals have in this context for providing a structural modeling approach for WAN tra c and for capturing in a compact and parsimonious manner the observed scaling phenomena at large as well as small time scales.

Modelling computer network traffic using wavelets and time series analysis

2019

Modelling of network traffic is a notoriously difficult problem. This is primarily due to the ever-increasing complexity of network traffic and the different ways in which a network may be excited by user activity. The ongoing development of new network applications, protocols, and usage profiles further necessitate the need for models which are able to adapt to the specific networks in which they are deployed. These considerations have in large part driven the evolution of statistical profiles of network traffic from simple Poisson processes to non-Gaussian models that incorporate traffic burstiness, non-stationarity, self-similarity, long-range dependence (LRD) and multi-fractality. The need for ever more sophisticated network traffic models has led to the specification of a myriad of traffic models since. Many of these are listed in [91, 14]. In networks comprised of IoT devices much of the traffic is generated by devices which function autonomously and in a more deterministic fa...

Modeling network traffic in wavelet domain

This work discovers that although network traffic has a complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a “short-range” dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for fractional Gaussian noise traffic. Any model which captures additional correlations in the wavelet domain only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N) for developing such wavelet models and generating synthesised traffic of length N, which is among the lowest attained.