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.
L. Xu, K. Harfoush and I. Rhee, “Binary Increase Congestion Control (BIC) for Fast Long-Distance Networks,” INFOCOM 23rd Annual Joint Conference of the IEEE Computer and Communications Societies, Hong Kong, 7-11 March 2004, pp. 2514-2524.
S. Ekelin, M. Nilsson, E. Hartikainen, A. Johnsson, J. E. Mangs, B. Melander and M. Bjorkman, “Real-Time Measurement of End-to-End Available Bandwidth using Kalman Filtering,” Proceedings of the 10th IEEE/IFIP Network Operations and Management Symposium, Vancouver, 3-7 April 2006, pp. 73-84.
R. J. Gibbens and F. P. Kelly, “Distributed Connection Acceptance Control for a Connectionless Network,” Proceedings of the 16th International Teletraffic Congress, Edinburgh, 7-11 June 1999, pp. 941-952.
N. Dukkipati, M. Kobayashi, R. Zhang-Shen and N. McKeown, “Processor Sharing Flows in the Internet,” In: H. de Meer and N. Bhatti, Eds., IEEE International Workshop Quality of Service, IFIP International Federation for Information Processing, 2005, pp. 267-281.
A. Afanasyev, N. Tilley, P. Reiher and L. Kleinrock, “Host-to-Host Congestion Control for TCP,” IEEE Communications Surveys & Tutorials, Vol. 12, No. 3, 2010, pp. 304-342.
B. Wydrowski, L. L. H. Andrew and M. Zukerman, “MaxNet: A Congestion Control Architecture for Scalable Networks,” IEEE Communications Letters, Vol. 7, No. 10, 2003, pp. 511-513.
V. Misra, W.-B. Gong and D. Towsley, “Fluid-Based Analysis of a Network of AQM Routers Supporting TCP Flows with an Application to RED,” Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, New York, 28 August-1 September 2000, pp. 151-160.
G. Quer, H. Meenakshisundaram, B. R. Tamma, B. S. Manoj, R. Rao and M. Zorzi, “Using Bayesian Networks for Cognitive Control of Multi-Hop Wireless Networks,” IEEE Military Communications Conference, San Jose, 31 October-3 November 2010, pp. 201-206.
T. Ellman, J. Keane and M. Schwabacher, “Intelligent Model Selection for Hill Climbing Search in Computer-Aided Design,” Proceedings of the 11th National Conference on Artificial Intelligence, Washington, 11-15 July 1993, pp. 594-599.
S. Yang and K.-C. Chang, “Comparison of Score Metrics for Bayesian Network Learning,” IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, Vol. 32, No. 3, 2002, pp. 419-428.