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C. Andrieu, N. de Freitas, A. Doucet and M. I. Jordan, “An Introduction to MCMC for Machine Learning,” Machine Learning, Vol. 50, No. 1, 2003, pp. 5-43. doi:10.1023/A:1020281327116

has been cited by the following article:

• TITLE: Cognitive Congestion Control for Data Portals with Variable Link Capacity

  AUTHORS: Ershad Sharifahmadian, Shahram Latifi

  KEYWORDS: Available Bandwidth; Cognitive System; Data Portal; Network Congestion; Queue Length; Variable Link Capacity

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.5 No.8, August 28, 2012

  ABSTRACT: Network congestion, one of the challenging tasks in communication networks, leads to queuing delays, packet loss, or the blocking of new connections. In this study, a data portal is considered as an application-based network, and a cognitive method is proposed to deal with congestion in this kind of network. Unlike previous methods for congestion control, the proposed method is an effective approach for congestion control when the link capacity and information inquiries are unknown or variable. Using sufficient training samples and the current value of the network parameters, available bandwidth is adjusted to distribute the bandwidth among the active flows. The proposed cognitive method was tested under such situations as unexpected variations in link capacity and oscillatory behavior of the bandwidth. Based on simulation results, the proposed method is capable of adjusting the available bandwidth by tuning the queue length, and provides a stable queue in the network.