Abstract
Traffic Engineering (TE) approaches are increasingly important in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to minimize network congestion. In both tasks, the optimization considers scenarios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came naturally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.
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
Altin, A., Fortz, B., Thorup, M., Ümit, H.: Intra-domain traffic engineering with shortest path routing protocols. Annals of Operations Research 204(1), 56–95 (2013)
Awduche, D., Malcolm, J., Agogbua, J., O’Dell, M., McManus, J.: Requirements for Traffic Engineering Over MPLS. RFC 2702 (Informational), September 1999
Claise, B.: RFC 3954 - Cisco Systems NetFlow Services Export Version 9, October 2004
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolutionary Computation 6(2), 182–197 (2002)
Dijkstra, E.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959)
Evangelista, P., Maia, P., Rocha, M.: Implementing metaheuristic optimization algorithms with jecoli. In: Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications, ISDA ’09, pp. 505–510. IEEE Computer Society, Washington, DC, USA (2009)
Fortz, B.: Internet traffic engineering by optimizing ospf weights. In: Proceedings of IEEE INFOCOM, pp. 519–528 (2000)
Fortz, B., Thorup, M.: Optimizing ospf/is-is weights in a changing world. IEEE Journal on Selected Areas in Communications 20(4), 756–767 (2002)
Iannaccone, G., Chuah, C., Mortier, R., Bhattacharyya, S., Diot, C.: Analysis of link failures in an ip backbone. In: Proceedings of the 2Nd ACM SIGCOMM Workshop on Internet Measurment, IMW ’02, pp. 237–242. ACM, New York, NY, USA (2002)
Medina, A., Lakhina, A., Matta, I., Byers, J.: Brite: Universal topology generation from a users perspective. Technical report, Boston, MA, USA (2001)
Moy, J.: OSPF Version 2. RFC 2328 (Standard), April 1998. Updated by RFC 5709
Oran, D.: OSI IS-IS Intra-domain Routing Protocol. Technical report, IETF, February 1990
Pereira, Vitor, Rocha, Miguel, Cortez, Paulo, Rio, Miguel, Sousa, Pedro: A Framework for Robust Traffic Engineering Using Evolutionary Computation. In: Doyen, Guillaume, Waldburger, Martin, Čeleda, Pavel, Sperotto, Anna, Stiller, Burkhard (eds.) AIMS 2013. LNCS, vol. 7943, pp. 1–12. Springer, Heidelberg (2013)
Rocha, M., Sousa, P., Cortez, P., Rio, M.: Quality of Service Constrained Routing Optimization Using Evolutionary Computation. Applied Soft Computing 11(1), 356–364 (2011)
Tan, K.C., Lee, T.H., Khor, E.F.: Evolutionary algorithms for multi-objective optimization: Performance assessments and comparisons. Artif. Intell. Rev. 17(4), pp. 251–290, June 2002
Zitzler, E., Laumanns, M., Thiele, L.: Spea 2: Improving the strength pareto evolutionary algorithm. Technical report (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Pereira, V., Sousa, P., Cortez, P., Rio, M., Rocha, M. (2015). Comparison of Single and Multi-objective Evolutionary Algorithms for Robust Link-State Routing. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9019. Springer, Cham. https://doi.org/10.1007/978-3-319-15892-1_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-15892-1_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15891-4
Online ISBN: 978-3-319-15892-1
eBook Packages: Computer ScienceComputer Science (R0)