An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks

doi:10.1088/1674-1056/22/5/058902

Abstract In this paper, we propose an adaptive strategy based on linear-prediction of queue length to minimize congestion in Barabási-Albert (BA) scale-free networks. This strategy uses local knowledge of traffic conditions and allows nodes to be able to self-coordinate their accepting probability to the incoming packets. We show that the strategy can delay remarkably the onset of congestion and systems avoiding the congestion can benefit from hierarchical organization of accepting rates of nodes. Furthermore, with the increase of prediction orders, we achieve larger values for the critical load together with a smooth transition from free-flow to congestion.

Keywords: linear prediction congestion networks

Received: 27 August 2012
Revised: 16 October 2012
Accepted manuscript online:

PACS:	89.75.Hc	(Networks and genealogical trees)
	02.50.-r	(Probability theory, stochastic processes, and statistics)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 60672095) and the Youth Science and Technology Innovation Fund of Nanjing Agricultural University, China (Grant No. KJ2010024).

Corresponding Authors: Shen Yi
E-mail: shen_yi1979@njau.edu.cn

Cite this article:

Shen Yi (沈毅) An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks 2013 Chin. Phys. B 22 058902

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An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks

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