Abstract
Routing packets is a relevant issue for maintaining good performance and successsfully operating in a web based systems. This problem is naturally formulated as a dynamig programming problem, which, however, is too complex to be solved exactly. We proposed here two adaptive routing algorithms based on reinforcement learning. In the first algorithm, we have used a neural network to approximate a reinforcement signal, allowing the learner to incorporate various parameters into its distance estimation such as local queue size. Moreover, each router uses an on line learning module to optimize the path in terms of average packet delivery time, by taking into account the waiting queue states of neighboring routers. In the second step, the exploration of paths is limited to N-Best non loop paths in term of hops number (number of routers in a path) leading to a substantial reduction of convergence time. The performances of the proposed algorithms are evaluated experimentally for different levels of traffic’s load and compared to standard shortest path and Q-routing algorithms. Our Approaches proves superior to a classical algorithms and are able to route efficiently even when critical aspects of the simulation, such as the network load, are allowed to vary dynamically.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
White, P.P.: RSVP and Integrated Services in the Internet: A Tutorial. IEEE Com. Mag. (May 1997)
Crawley, E., Nair, R., Rajagopalan, B., Sandick, H.: A Framework for QoS-based Routing in the Internet. In: RFC 2386, IETF (August 1998)
Stallings, W.: MPLS. Internet Protocol Journal 4(3) (September 2001)
Gallager, R.G.: A minimum delay routing algorithm using distributed computations. IEEE Transactions on Communications COM-25 (1977)
Ozdaglar, A.E., Bertsekas, D.P.: Optimal Solution of Integer Multicommodity Flow Problem with Application in Optical Networks. In: Proc. of Symposium on Global Optimisation (June 2003)
Sutton, R.S., Barto, A.G.: Reinforcement Learning. MIT Press, Cambridge (1997)
Boyan, J.A., Littman, M.L.: Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach. In: Advances in Neural Information Processing Systems, vol. 6 (1994)
Kumar, S., Miikkualainen, R.: Confidence-based Q-routing: an on-queue adaptive routing algorithm. In: Proceedings of Neural Networks in Engineering (1998)
Yanxia, J., Ioanis, N., Pawel, G.: Multiple path QoS Routing. In: Proc. Int. Conf. Communications (ICC 2001). IEEE, Los Alamitos (2001)
Mellouk, A., Gallinari, P.: Discriminative training for improved neural prediction system. In: IEEE Int. Acoustic, Speech and Signal Processing (1995)
Lemaire, V., Clérot, F.: Estimation of the Blocking probabilities in an ATM Network Node Using Artificial Neural Networks for Connection Admission Control. In: International Tel. traffic Congress, Edinburgh, vol. 16 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mellouk, A., Hoceini, S. (2005). A Reinforcement Learning Approach for QoS Based Routing Packets in Integrated Service Web Based Systems. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds) Advances in Web Intelligence. AWIC 2005. Lecture Notes in Computer Science(), vol 3528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11495772_47
Download citation
DOI: https://doi.org/10.1007/11495772_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26219-0
Online ISBN: 978-3-540-31900-9
eBook Packages: Computer ScienceComputer Science (R0)