Q-Net: A Novel QoS Aware Routing Algorithm for Future Data Networks
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.
[1] D. Chalmers, M. Sloman: Survey of Quality of Service in Mobile
Computing Environments, - IEEE Communications surveys, (1999).
[2] G. Bochmann, A. Hafid: Some principles for quality of service
management, Distrib. Syst. Engng. l4, 16-27 (1997).
[3] X. Masip-Bruin et al: Research challenges in QoS routing, Computer
Communications. 29, 563-581 (2006).
[4] Z. Wang, J. Crowcroft: Quality of service routing for supporting
multimedia applications, IEEEJSAC. 14, 7 (1996).
[5] G. D. Caro, M. Dorigo: AntNet: Distributed stigmergetic control for
communications networks, J. Artif. Intell. Res. 9, 317-365 (1998).
[6] G. D. Caro, M. Dorigo: AntNet: A Mobile Agents Approach to Adaptive
Routing, Univ. Libre de Bruxelles, Brussels, Belgium, Tech. Rep.
IRIDIA/9712 (1997).
[7] 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).
[8] 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).
[9] 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).
[10] S. Fenet, S. Hassas: An ant based system for dynamic multiple criteria
balancing, In Proc. 1st Int.Workshop Ants Systems, Brussels, Belgium
(1998).
[11] K. Oida, M. Sekido: ARS: an efficient agent-based routing system for
QoS guarantees, Computer Communications. 23, 1437-1447 (2000).
[12] 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).
[13] M. Dorigo, G. D. Caro: Ant Algorithms for Discrete Optimization.
Artificial Life, MIT Press 137-172 (1999)
[14] T. Stutzle, H. H. Hoos: MAX-MIN Ant System. Future Generation
Computer System, 889-914 (2000).
[1] D. Chalmers, M. Sloman: Survey of Quality of Service in Mobile
Computing Environments, - IEEE Communications surveys, (1999).
[2] G. Bochmann, A. Hafid: Some principles for quality of service
management, Distrib. Syst. Engng. l4, 16-27 (1997).
[3] X. Masip-Bruin et al: Research challenges in QoS routing, Computer
Communications. 29, 563-581 (2006).
[4] Z. Wang, J. Crowcroft: Quality of service routing for supporting
multimedia applications, IEEEJSAC. 14, 7 (1996).
[5] G. D. Caro, M. Dorigo: AntNet: Distributed stigmergetic control for
communications networks, J. Artif. Intell. Res. 9, 317-365 (1998).
[6] G. D. Caro, M. Dorigo: AntNet: A Mobile Agents Approach to Adaptive
Routing, Univ. Libre de Bruxelles, Brussels, Belgium, Tech. Rep.
IRIDIA/9712 (1997).
[7] 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).
[8] 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).
[9] 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).
[10] S. Fenet, S. Hassas: An ant based system for dynamic multiple criteria
balancing, In Proc. 1st Int.Workshop Ants Systems, Brussels, Belgium
(1998).
[11] K. Oida, M. Sekido: ARS: an efficient agent-based routing system for
QoS guarantees, Computer Communications. 23, 1437-1447 (2000).
[12] 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).
[13] M. Dorigo, G. D. Caro: Ant Algorithms for Discrete Optimization.
Artificial Life, MIT Press 137-172 (1999)
[14] T. Stutzle, H. H. Hoos: MAX-MIN Ant System. Future Generation
Computer System, 889-914 (2000).
@article{"International Journal of Information, Control and Computer Sciences:64263", author = "Maassoumeh Javadi Baygi and Abdul Rahman B Ramli and Borhanuddin Mohd Ali and Syamsiah Mashohor", title = "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.", keywords = "Quality of Service, Routing, Ant Colony
Optimization, Ant-based algorithms.", volume = "4", number = "11", pages = "1794-5", }