2 edition of **Data network traffic modeling and engineering using stable and fractal processes.** found in the catalog.

Data network traffic modeling and engineering using stable and fractal processes.

Fotios C. Harmantzis

- 251 Want to read
- 34 Currently reading

Published
**2002**
.

Written in English

The Physical Object | |
---|---|

Pagination | 138 leaves. |

Number of Pages | 138 |

ID Numbers | |

Open Library | OL19066019M |

ISBN 10 | 0612746348 |

Accurate traffic flow data is crucial for traffic control and management in an intelligent transportation system (ITS), and thus traffic flow prediction research attracts significant attention in the transportation community. Previous studies have suggested that raw traffic flow data may be contaminated by noises caused by unexpected reasons (e.g., loop detector damage, . A traffic trace gives insight about a particular traffic source, but a traffic model gives insight about all traffic sources of that type. Traffic models have three major uses. One important use of traffic models is to properly dimension network resources for a target level of QoS.

Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner. Adopting a data-driven approach, the book covers: DNA sequence analysis. EEG analysis. Heart rate variability analysis. Neural information processing. Network traffic modeling. Economic time series analysis. And Cited by: The model uses a time series data of daily quotes of the euro/dollar exchange rate in order to calculate the probability of the trend prediction as far as exchange rate. The data is divided into the training data, checking data and testing data. The model is trained using the training data and then the testing data is used for model validation.

Modeling results •A crude model performs well! –As simpler/simpler than an M/M/1 queue •Use effective link bandwidth –account for encapsulation •Small gap between router performance and queuing theory! •The model defines Busy Periods: time between the arrival of a packet to the empty system and the time when the system becomes empty. network trafﬁc across a range of cyber scenarios (e.g., benign trafﬁc, SYN ﬂood and low-volume DoS attacks, and multi-method cyber attack). Additionally, we show using an analyzed scenario that for the maximum-accuracy case, the stable distribution permits the use of smaller data windows and counting periods than the Gaussian distribution and.

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Abstract. Several recent Internet traffic measurement studies reported that traffic in modern high-speed networks is a self-similar process. If the stochastic self-similar network traffic models do not accurately represent the real traffic, then the network performance may be over estimated or underestimated, and it causes degradation of Internet router by: 2.

Burstiness describes the tendency of traffic to occur in clusters. A traffic burst affects buffer occupancy and leads to network congestion and data loss. Data burstiness is manifested by the autocorrelation function which describes the relation between packet arrivals at different times.

It was recently discovered that network traffic exhibits Cited by: 1. The network traffic exhibits highly irregular fractal-like structure and long term correlations.

Various stochastic processes such as fractional Gaussian noise, multiplicative cascades, linear fractional stable motion have been proposed to model network by: We demonstrate that Ethernet local area network (LAN) traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal.

traffic data shown in Figure 2 is representative of the bursty nature of network traffic. The data is clearly not exponentially distributed. The failure of Poisson processes to model network traffic is outlined in (Paxson and Floyd ).

For this reason, a hybrid approach to modeling network traffic is indicated. Total Packets Transmitted Seconds. By re-estimating the model on HD spiking data we discover network parameter modifications are consistent across three very different types of HD mutant mouse models (YAC, Q, R6/2).

This book demonstrates how to use self-similar processes for designing new telecommunications systems and optimizing existing networks so as to achieve maximum efficiency and serviceability.

The approach is rooted in theory, describing the algorithms (the logical arithmetical or computational procedures that define how a task is performed) for.

Adaptive Real-Time Traffic Engineering using Software Defined Networks, BSCS Projects. Introduction In continues development environment the traditional network cannot meet the requirements of today’s dynamic and real-time application.

Programmable Software Defined Networking (SDN) is a developing design that is dynamic. including data collection, analysis, and design. Traffic engineering studies, traffic control devices, capacity and level of service analysis of freeways and urban streets.

• The objective of this course is to introduce students to traffic engineering fundamentals for highways and freeways. Emphasis is on the safe and efficient operations of. In this paper, we discuss recent results in traffic modeling from a view of the generalized Cauchy (GC) process.

The GC process is indexed by two parameters D and H. The parameter D in the GC model is independent of H. Hence, it provides a more flexible way to describe the multi-fractal phenomena of traffic in addition to accurately modeling traffic for both short-term Cited by: analysis of key network activities involving fractal traffic, which include interaction among multiple traffic streams and queueing of fractal processes.

In Section 5, we develop exten-sions of our multiscale framework to allow us to design control policies for a number of fractal queues, including optimal server and flow control. traffic flow for a given network and traffic is addressed. As stated, a multi-objective model using two objective functions namely link utilization and the node utilization by increasing in-flow traffic to a node is defined and formulated.

It is then solved using an optimization technique known as Goal Programming method. Figure A highway network, with each edge labeled by its travel time (in minutes) when there are x cars using it. When cars need to get from A to B, they divide evenly over the two routes at equilibrium, and the travel time is 65 minutes.

travel time (in minutes) when there are x cars using the edge. In this simpliﬁed example,File Size: KB. The only modern treatment of signal processing with chaos and random fractals unified, this is an essential book for researchers and graduate students in electrical engineering, computer science, bioengineering, and many other fields.

processes which show properties of -similarity. There are many methods for self estimating the Hurst exponent using time series. The aim of this research is to carry out the comparative analysis of statistical properties ofthe the Hurst exponent estimators obtained by different methods using model stationary and nonstationary fractal time series.

Network traffic modeling ; Economic time series analysis ; And more. Additionally, the book illustrates almost every concept presented through applications and a dedicated Web site is available with source codes written in various languages, including Java, Fortran, C, and MATLAB, together with some simulated and experimental data.

Contributions to Modelling of Internet Trafﬁc by Fractal Renewal Processes Muhammad Asad Arfeen Willig, “Internet Trafﬁc Modeling: From Superposition to Scaling”, IET Networks, volume 3, Special Issue on Teletrafﬁc Engineering in Communications Systems, We present efficient methods for simulation, using the Fast Fourier Transform (FFT) algorithm, of two classes of processes with symmetric α-stable (SαS) distributions.

Namely, (i) the linear fractional stable motion (LFSM) process and (ii) the fractional autoregressive moving average (FARIMA) time series with SαS by: Two key properties of such processes are LRD that is characterized by the Hurst parameter H and self-similarity (SS) that is measured by the fractal dimension D.

However, in the popular traffic model using fractional Gaussian noise (fGn), these two parameters are linearly related. This may be regarded as a limitation of fGn in traffic modeling. Internet Trafﬁc Matrices: A Primer Paul TuneandMatthew Roughan importance of network operations management, planning, provisioning and trafﬁc engineering.

A key input into these processes is the trafﬁc matrix, and this is the focus of this chapter. as will be clearer below, are utilised for a variety of network engineering goals Cited by:. A General Description of Evolutionarily Stable Strategies Relationship Between Evolutionary and Nash Equilibria Evolutionarily Stable Mixed Strategies Chapter 8.

Modeling Network Traffic using Game Theory. Traffic at Equilibrium Braess's Paradox Advanced Material: The Social Cost of Traffic at Equilibrium Chapter 9.The application of the GC process to network traffic modeling refers to, and Li and Zhao.

Recently, Lim and Teo extended the GC model to describe the Gaussian fields and Gaussian sheets. Vengadesh et al. applied it to the analysis of bacteriorhodopsin in material science. Alpha-Stable ProcessesCited by: () A fractal roughness model for the transport of fractional non-Newtonian fluid in microtubes.

Chaos, Solitons & Fractals() Building a puzzle to solve a riddle: A multi-scale disaggregation approach for multivariate stochastic processes with any marginal distribution and correlation by: