Congestion and decongestion in a communication network

Singh, Bhavna; Gupte, Neelima

doi:10.1103/physreve.71.055103

Cited by 64 publications

(43 citation statements)

References 15 publications

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“…On the other hand, when many messages travel simultaneously on the network, the finite capacity of the hubs can lead to the trapping of messages in their neighbourhoods, and a consequent congestion or jamming of the network. A crucial quantity which identifies these hubs is called the coefficient of betweenness centrality (CBC) [13], defined to be the ratio of the number of messages N k which pass through a given hub k to the total number of messages which run simultaneously i.e. CBC = N k N .…”

Section: Congestion and Decongestionmentioning

confidence: 99%

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Message transfer in a communication network

Mukherjee

Gupte

2008

Pramana - J Phys

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Abstract. We study message transfer in a 2 − d communication network of regular nodes and randomly distributed hubs. We study both single message transfer and multiple message transfer on the lattice. The average travel time for single messages travelling between source and target pairs of fixed separations shows q−exponential behaviour as a function of hub density with a characteristic power-law tail, indicating a rapid drop in the average travel time as a function of hub density. This power-law tail arises as a consequence of the log-normal distribution of travel times seen at high hub densities. When many messages travel on the lattice, a congestion-decongestion transition can be seen. The waiting times of messages in the congested phase show a Gaussian distribution, whereas the decongested phase shows a log-normal distribution. Thus, the congested or decongested behaviour is encrypted in the behaviour of the waiting time distributions.

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Section: Congestion and Decongestionmentioning

confidence: 99%

“…It was observed earlier that for low hub density messages get trapped [11] at some hubs which lead to congestion in the network. The network is decongested for higher hub densities [11,13].…”

Section: Congestion and Decongestionmentioning

confidence: 99%

Message transfer in a communication network

Mukherjee

Gupte

2008

Pramana - J Phys

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“…The study of the flow of traffic through a network is a important and frequently encountered dynamical process. The dynamics of the flow of information has been studied over a wide range of communication networks of various types [4][5][6][7][8][9][10][11], with the Internet being an important example. In the internet, computers of different computational processing capacities communicate by exchanging information in the form of discrete units, or "packets" which then find their destination by hopping from node to node via the links between them.…”

Section: Introductionmentioning

confidence: 99%

Transmission of packets on a hierarchical network: Statistics and explosive percolation

Kachhvah

Gupte

2012

Phys. Rev. E

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We analyze an idealized model for the transmission or flow of particles, or discrete packets of information, in a weight bearing branching hierarchical 2 − D networks, and its variants. The capacities add hierarchically down the clusters. Each node can accommodate a limited number of packets, depending on its capacity and the packets hop from node to node, following the links between the nodes. The statistical properties of this system are given by the Maxwell -Boltzmann distribution. We obtain analytical expressions for the mean occupation numbers as functions of capacity, for different network topologies. The analytical results are shown to be in agreement with the numerical simulations. The traffic flow in these models can be represented by the site percolation problem. It is seen that the percolation transitions in the 2−D model and in its variant lattices are continuous transitions, whereas the transition is found to be explosive (discontinuous) for the V − lattice, the critical case of the 2 − D lattice. The scaling behavior of the second order percolation case is studied in detail. We discuss the implications of our analysis.

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“…Many previous excellent works focus on the evolution of structure driven by the increment of traffic [6,7,8] and some explore how different topologies impact the traffic dynamics [12,13]. Some works [10,11] gave several models to mimic the traffic routing on complex networks by introducing randomly selected source as well as particles (packets) generating rate and destination of each particles [18,19,20].…”

Section: Introductionmentioning

confidence: 99%

“…Since the discovery of some common interesting features of many real networks such as small-world phenomena by Strogatz and Watts [1] and Scale-free phenomena by Albert and Barabási [2], processes of dynamics conducting on the network structure such as traffic congestion of information now have drew more and more attention from engineering and physical field Email address: bhwang@ustc.edu.cn (Bing-Hong Wang a, * ). [12,13,14,15,16,17,18,19,20,21,22], due to the importance of large communication networks such as WWW [4] and Internet [5] in modern society. Evolution of networks structure and interplay of traffic dynamics also play an important role in the research of the traffic system including Internet, highway network and so on , they want to understand and explain the process of dynamics on the underlying structure.…”

Section: Introductionmentioning

confidence: 99%

An Effective Local Routing Strategy on the Communication Network

Wang

Xi³

et al. 2009

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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In this paper, We propose a effective routing strategy on the basis of the so-called nearest neighbor search strategy by introducing a preferential delivering exponent α. we assume that the handling capacity of one vertex is proportional to its degree when the degree is smaller than a cut-off value K, and is infinite otherwise. It is found that by tuning the parameter α, the scale-free network capacity measured by the order parameter is considerably enhanced compared to the normal nearest-neighbor strategy. Traffic dynamics both near and far away from the critical generating rate R c are discussed. We also investigate R c as functions of m(connectivity density), K(cutoff value). Due to the low cost of acquiring nearest-neighbor information and the strongly improved network capacity, our strategy may be useful and reasonable for the protocol designing of modern communication networks.

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Congestion and decongestion in a communication network

Cited by 64 publications

References 15 publications

Message transfer in a communication network

Message transfer in a communication network

Transmission of packets on a hierarchical network: Statistics and explosive percolation

An Effective Local Routing Strategy on the Communication Network

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