The analogies of highway and computer network traffic

Simon, Gábor; Csabai, István

doi:10.1016/s0378-4371(02)00603-9

Cited by 24 publications

(11 citation statements)

References 28 publications

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“…In this paper, Csabai noted the presence of a 1/f power spectrum for the RTT times for pings between two computers where the fitted slope is -1.15 (about an H of 1.08). He also is among the first to compare Internet data traffic with vehicle traffic [74]. It must be noted that the RTT from ICMP echoes is not always equivalent to the RTT in TCP since many gateways give preferential forwarding to TCP packets.…”

Section: Network Layer Cause: Theories Of Phase Transitions and Critimentioning

confidence: 99%

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

Smith

2011

Advs. Complex Syst.

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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, selforganization, 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.

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Section: Network Layer Cause: Theories Of Phase Transitions and Critimentioning

confidence: 99%

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

Smith

2011

Advs. Complex Syst.

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“…Huisinga et al [28] presented a microscopic description of packet transport in the Internet by using a simple CA model, while Ren et al [23] proposed a simplified 1-D CA for computer network, namely the NaSch network model, which is originated at the NaSch model of road traffic [24]. A more extensive analysis of the analogies of highway and computer network traffic can be found in [29]. Liu et al [30] used a simple 1-D CA model of data packets transport in the Internet in order to study the travel times of data packets and transport rates of routers along a fixed path.…”

Section: Cellular Automatamentioning

confidence: 99%

A CAD System for Modeling and Simulation of Computer Networks Using Cellular Automata

Mardiris

Mizas

Karafyllidis

et al. 2008

IEEE Trans. Syst., Man, Cybern. C

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The increasing complexity of computer networks calls for the development of new models for their simulation. Cellular automata (CAs) are a well-known and successful model for complex systems. This paper presents a system for modeling and simulation of computer networks based on CAs. More specifically, a 2-D NaSch CA computer network model has been developed, and several networks were simulated. Algorithms for connectivity evaluation, system reliability evaluation, and shortest path computation in a computer network have also been implemented. Our system, called Net_CA system, was designed and developed as an interactive tool that offers automated modeling with the assistance of a dynamic and user-friendly graphical environment. The proposed system also produces automatically synthesizable very high speed integrated circuits hardware description language code leading to the parallel hardware implementation of the aformentioned CA algorithms. In terms of circuit design and layout, ease of mask generation, silicon area utilization, and maximization of achievable clock speed, CAs are perhaps the computational structures best suited for a fully parallel very large scale integrated realization. The simulation algorithms developed in the present paper offer high flexibility. Furthermore, connection reliability and other important parameters are inputs to the algorithms, rendering Net_CA a very reliable and fast simulator for wireless networks, ad hoc networks, and generally, for low connection reliability networks.

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“…The speed-density relationship serves as the basis to understand system dynamics in various disciplines [2]. It can be used as a tool to study on moving objects (or particles) in many scientific areas: pedestrians [3,4], conveyors, network information packages [5], crowd dynamics [6], molecular motors, and biological systems [7]. It has been almost 75 years since Greenshields' seminal paper Study of Traffic Capacity in 1935 [8].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Possibility of Presenting a Semi-Stochastic Speed-Density Function

2016

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Abstract. Underlying a fundamental diagram is a relation between traffic speed and density which roughly corresponds to drivers' speed choices under varying car-following distances. Stochastic and deterministic models are mainly two different categories of speed-density models. The advantages of deterministic models are their mathematical simplicity and analytical tractability though their results will show just the average parameters. Although the stochastic models may represent more accurate results taking uncertainty into account, they are often hard to use and analytically not tractable. The aim of this paper is to investigate the possibility of presenting a model which is neither completely deterministic nor completely stochastic but easy to use and understand which incorporates different traffic conditions and speed distributions. Monte Carlo Method has been used to generate different speed distributions based on different traffic conditions and consequently generating their relevant densities. Surveying the relation between the mentioned speed distributions and the obtained densities kept the chance of presenting a model which is neither completely deterministic nor completely stochastic but easy to use and understand which incorporates different traffic conditions and speed distributions.

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The analogies of highway and computer network traffic

Cited by 24 publications

References 28 publications

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

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

A CAD System for Modeling and Simulation of Computer Networks Using Cellular Automata

Assessing the Possibility of Presenting a Semi-Stochastic Speed-Density Function

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