Commenced in January 2007
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Q-Net: A Novel QoS Aware Routing Algorithm for Future Data Networks
Abstract:The expectation of network performance from the early days of ARPANET until now has been changed significantly. Every day, new advancement in technological infrastructure opens the doors for better quality of service and accordingly level of perceived quality of network services have been increased over the time. Nowadays for many applications, late information has no value or even may result in financial or catastrophic loss, on the other hand, demands for some level of guarantee in providing and maintaining quality of service are ever increasing. Based on this history, having a QoS aware routing system which is able to provide today's required level of quality of service in the networks and effectively adapt to the future needs, seems as a key requirement for future Internet. In this work we have extended the traditional AntNet routing system to support QoS with multiple metrics such as bandwidth and delay which is named Q-Net. This novel scalable QoS routing system aims to provide different types of services in the network simultaneously. Each type of service can be provided for a period of time in the network and network nodes do not need to have any previous knowledge about it. When a type of quality of service is requested, Q-Net will allocate required resources for the service and will guarantee QoS requirement of the service, based on target objectives.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084708Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1031
 D. Chalmers, M. Sloman: Survey of Quality of Service in Mobile Computing Environments, - IEEE Communications surveys, (1999).
 G. Bochmann, A. Hafid: Some principles for quality of service management, Distrib. Syst. Engng. l4, 16-27 (1997).
 X. Masip-Bruin et al: Research challenges in QoS routing, Computer Communications. 29, 563-581 (2006).
 Z. Wang, J. Crowcroft: Quality of service routing for supporting multimedia applications, IEEEJSAC. 14, 7 (1996).
 G. D. Caro, M. Dorigo: AntNet: Distributed stigmergetic control for communications networks, J. Artif. Intell. Res. 9, 317-365 (1998).
 G. D. Caro, M. Dorigo: AntNet: A Mobile Agents Approach to Adaptive Routing, Univ. Libre de Bruxelles, Brussels, Belgium, Tech. Rep. IRIDIA/9712 (1997).
 G. D. Caro, M. Dorigo: Mobile agents for adaptive routing, In Proc. 31st Hawaii Int. Conf. Systems Sciences, Kohala Coast, HI, Jan. ,pp. 74-83 (1998).
 G. D. Caro, M. Dorigo: An adaptive multi-agent routing algorithm inspired by ants behavior, In Proc. 5th Annual Australasian Conf. Parallel Real-Time Systems 261-272 (1998).
 N. Varela, M. C. Sinclair: Ant colony optimization for virtualwavelength- path routing and wavelength allocation, In Proc. Congress Evolutionary Computation, Washington, DC, pp. 1809-1816 (1999).
 S. Fenet, S. Hassas: An ant based system for dynamic multiple criteria balancing, In Proc. 1st Int.Workshop Ants Systems, Brussels, Belgium (1998).
 K. Oida, M. Sekido: ARS: an efficient agent-based routing system for QoS guarantees, Computer Communications. 23, 1437-1447 (2000).
 K. M. Sim, W. H. Sun: Multiple Ant Colony Optimization for Load Balancing, In Proc. 4th Int. Conf. Intelligent Data Engineering Automated Learning, Hong Kong, vol. 2690, pp. 467-471 Springer,Heidelberg (2003).
 M. Dorigo, G. D. Caro: Ant Algorithms for Discrete Optimization. Artificial Life, MIT Press 137-172 (1999)
 T. Stutzle, H. H. Hoos: MAX-MIN Ant System. Future Generation Computer System, 889-914 (2000).