Abstract
The current QoS multicast routing model aims to solve a one-objective optimization problem with one or more bounded-constraints, such as delay, delay jitter, bandwidth, etc. To satisfy the individual requirement for users in multiple QoS networks, we analyze the limitation of the current model and propose a new QoS multicast routing model that supports multi-objective optimization. The new model considers the QoS guarantee as QoS optimization objectives rather than QoS constraints. It overcomes the limitations that exist in the traditional multicast routing model. Furthermore, a new routing algorithm to deal with the new model based on immune principles and Pareto concepts is given. In this algorithm, a gene library is introduced to speed up the algorithm to satisfy the real-time requirement of the routing problem. The initial experimental results have shown that the new algorithm can effectively produce more than one Pareto optimization solution compromising all QoS objectives within one single running.
This is a preview of subscription content, log in via an institution to check access.
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
Wang, Z., Crowcroft, J.: Quality of Service for Supporting Multimedia Applications. IEEE Journal on Selected in Communications 14(7), 1228–1234 (1996)
Sahasrabuddhe, L.H., Mukherjee, B.: Multicast Routing Algorithms and Protocols: A Tutorial. IEEE Network 14(1), 90–102 (2000)
Mcquillan, J.M., Richer, I., Rosen, E.C.: The New Routing Algorithm for the ARRANET. IEEE Trans. on. Comm. 28(5), 711–719 (1980)
Marwaha, S.: Evolutionary Fuzzy Multi-Objective Routing for Wireless Mobile Ad Hoc network. In: CEC, pp. 345–355 (2004)
Ji, Z.W.: Finding Multi-Objective Paths in Stochastic Network. In: CEC, pp. 1300–1307 (2005)
Qin, J., Kang, L.S.: A Novel Dynamic Population based Algorithm to Solve Multi-modal Function Optimization. In: Proceedings of World Congress on Intelligent Control and Automation, Hangzhou (2004)
Daid, A., Van, V., Gary, B.: Multi-Objective Evolutionary Algorithms: Analyzing the State-of-the-Art. Kalyanmoy Deb (2000)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms, pp. 34–76. John Wiley & Sons Ltd, Chichester (2001)
Dasgupta: Immune Systems and Their Applications. Springer, Heidelberg (1999)
Ada, G.L., Nossal, G.V.: The Clonal Selection Theory. Scientific American 257(2), 50–57 (1997)
de Castro, L.N., Von, Z.F.J.: Artificial Immune Systems: Part I-Basic Theory and Applications. Technical Report, TR-DCA (1999)
Forrest, S., Perelson, A.S.: Genetic Algorithms and the Immune System. In: Schwefel, H.-P., Maenner, R. (eds.) Parallel Problem Solving from Nature. LNCS, pp. 320–325. Springer, Berlin (1991)
Tanenbaum, A.S.: Computer Network, 3rd edn. Prentice Hall Inc., Englewood Cliffs (1996)
Hwang, F.K., Richards, D.S.: Steiner Tree Problems. Networks 22, 55–89 (1992)
Pan, Z.J., Kang, L.S.: Evolutionary Computation. Qinghua university publication, Beijing (1997)
Opera, M., Forrest, S.: How the Immune System Generates Diversity: Pathogen Space Converge with Random and Evolved Antibody Libraries. In: Genetic and Evolutionary Computation Conference, pp. 1651–1656 (1999)
Doar, M., Leslie, I.: How Bad is Naive Multicast Routing. In: Proceedings of the IEEE INFOCOM, pp. 82–89 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, J.Q., Qin, J., Kang, L.S. (2006). A New QoS Multicast Routing Model and Its Immune Optimization Algorithm. In: Ma, J., Jin, H., Yang, L.T., Tsai, J.JP. (eds) Ubiquitous Intelligence and Computing. UIC 2006. Lecture Notes in Computer Science, vol 4159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11833529_38
Download citation
DOI: https://doi.org/10.1007/11833529_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38091-7
Online ISBN: 978-3-540-38092-4
eBook Packages: Computer ScienceComputer Science (R0)