Load balancing in IP networks by optimising link weights

Mulyana, Eueung; Killat, Ulrich

doi:10.1002/ett.1051

Cited by 9 publications

(7 citation statements)

References 5 publications

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“…The quantities to be transported can either be of a material nature such as power or goods, or of a non-material nature such as information packets, which are transported on the Internet, or influence, which is transported on social networks [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. Optimization of network transport is thus an important problem for a variety of fields in science and technology.…”

Section: Introductionmentioning

confidence: 99%

Transport optimization on complex networks

Danila

Marsh

et al. 2007

Chaos: An Interdisciplinary Journal of Nonlinear Science

101

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We present a comparative study of the application of a recently introduced heuristic algorithm to the optimization of transport on three major types of complex networks. The algorithm balances network traffic iteratively by minimizing the maximum node betweenness with as little path lengthening as possible. We show that by using this optimal routing, a network can sustain significantly higher traffic without jamming than in the case of shortest path routing. A formula is proved that allows quick computation of the average number of hops along the path and of the average travel times once the betweennesses of the nodes are computed. Using this formula, we show that routing optimization preserves the small-world character exhibited by networks under shortest path routing, and that it significantly reduces the average travel time on congested networks with only a negligible increase in the average travel time at low loads. Finally, we study the correlation between the weights of the links in the case of optimal routing and the betweennesses of the nodes connected by them.Keywords: complex networks, scaling laws, transport * dbogdan@mail.uh.edu † marshj@ainfosec.com ‡ bassler@uh.edu 1 One of the most important problems in the study of complex networks is how to best route transport on the networks. This problem is important because transport is the main function of many natural and human-made networks. Often, the transport routes used on networks are the so-called shortest-path routes, which are the routes with the minimum number of hops between any two nodes. However, this approach, which is currently used to route transport of information packets on the Internet, typically leads to congestion and eventually jamming of highly connected nodes of the networks called hubs. For this reason and in light of recent research, interest has developed in finding the routing rules that allow a given network to bear the maximum possible traffic. Specifically, the problem can be stated as follows. Given a complex network and a set of processing power and traffic demand constraints for its nodes, find the set of routing rules which allow the network to bear the highest possible amount of traffic without jamming. This problem is known to be NP -hard, meaning that the time required for the computation of an exact solution increases with the number of nodes faster than any polynomial. In this paper we argue that a heuristic transport routing optimization algorithm recently published by us achieves near-optimal transport routing in polynomial time and show this to be true for three important types of complex networks. Of course, any optimized routing when compared to shortest-path routing occurs at the expense of increasing the average number of hops between the nodes. We show that with our algorithm the average number of hops after optimization increases with the number of nodes no faster than logarithmically and that optimization significantly decreases the average travel time on congested networks.

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Section: Introductionmentioning

confidence: 99%

Transport optimization on complex networks

Danila

Marsh

et al. 2007

Chaos: An Interdisciplinary Journal of Nonlinear Science

101

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“…In the case of the Internet, link weights are typically assigned manually by operators according to simple rules based on experience [1]. Recently, a series of heuristic algorithms have also been proposed for network traffic optimization [1,2,3,4,5]. These rules and algorithms are aimed at avoiding or reducing link overload by a judicious link weight assignment.…”

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confidence: 99%

Optimal transport on complex networks

Danila

Marsh³

et al. 2006

Phys. Rev. E

222

187

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We present a novel heuristic algorithm for routing optimization on complex networks. Previously proposed routing optimization algorithms aim at avoiding or reducing link overload. Our algorithm balances traffic on a network by minimizing the maximum node betweenness with as little path lengthening as possible, thus being useful in cases when networks are jamming due to queuing overload. By using the resulting routing table, a network can sustain significantly higher traffic without jamming than in the case of traditional shortest path routing.

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“…48,49 So, here we propose to optimize the link weight incrementally by minimizing the B max of the network. The optimizing process is described as follows:…”

Section: New Routing Strategy and Simulationsmentioning

confidence: 99%

An incremental optimal routing strategy for scale-free networks

Jiang

2014

Int. J. Mod. Phys. C

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The link congestion based tra±c model can more accurately reveal the tra±c dynamics of many real complex networks such as the Internet, and heuristically optimizing each link's weight for the shortest path routing strategy can strongly improve the tra±c capacity of network. In this work, we propose an optimal routing strategy in which the weight of each link is regulated incrementally to enhance the network tra±c capacity by minimizing the maximum link betweenness of any link in the network. We also estimate more suitable value of the tunable parameter for the e±cient routing strategy under the link congestion based tra±c model. The tra±c load of network can be signi¯cantly balanced at the expense of increasing a bit average path length or average tra±c load.

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Load balancing in IP networks by optimising link weights

Cited by 9 publications

References 5 publications

Transport optimization on complex networks

Transport optimization on complex networks

Optimal transport on complex networks

An incremental optimal routing strategy for scale-free networks

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