Skip to main content
SpringerLink
Log in

A comparison of multi-objective optimization algorithms for weight setting problems in traffic engineering

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

Traffic engineering approaches are increasingly important in network management to allow an optimized configuration and resource allocation. In link-state routing, setting appropriate weights to the links is an important and challenging optimization task. Different approaches have been put forward towards this aim, including evolutionary algorithms (EAs). This work addresses the evaluation of 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 network, with (1) changes in the traffic demand matrices, and (2) link failures. The methods will simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach. Since this leads to a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came naturally; those are compared to a single-objective EA previously proposed by the authors. The results show a remarkable performance and scalability of NSGA-II in the proposed tasks presenting itself as the most promising option for TE.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Altin A, Fortz B, Ümit H (2012) Oblivious OSPF routing with weight optimization under polyhedral demand uncertainty. Networks 60(2):132–139

    Article  MathSciNet  Google Scholar 

  • Altin A, Fortz B, Thorup M, Ümit H (2013) Intra-domain traffic engineering with shortest path routing protocols. Ann Oper Res 204(1):65–95

    Article  MathSciNet  Google Scholar 

  • Awduche D, Malcolm J, Agogbua J, O’Dell M, McManus J (1999) Requirements for traffic engineering over MPLS. RFC 2702 (Informational)

  • Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming. Genetic algorithms. Oxford University Press, Oxford

    Book  Google Scholar 

  • Ben-Ameur W, Gourdin E, Liau B, Michel N (2000) Optimizing administrative weights for efficient single-path routing. In: Proceedings of networks

  • Bley A, Fortz B, Gourdin E, Holmberg K, Klopfenstein O, Pióro M, Tomaszewski , Ümit H (2010)ptimization of OSPF routing in IP networks. In: Koster AMCA, Mñuoz X (eds) Graphs and algorithms in communication networks: studies in broadband, optical, wireless and ad hoc networks, Springer, Berlin, Chap. 8, pp 199–240

  • Cisco customer case study, maximizing return on network infrastructure investment with Cisco MATE design

  • Coello C, Pulido G, Lechuga M (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279

    Article  Google Scholar 

  • Corne D, Jerram N, Knowles J, Oates M (2001) PESA-II: region-based selection in evolutionary multiobjective optimization. In: Proceedings of the genetic and evolutionary computation conference (GECCO-2001), San Francisco, California. Morgan Kaufmann Publishers, pp 283–290

  • Dahai X, Mung C, Jennifer R (2011) Link-state routing with hop-by-hop forwarding can achieve optimal traffic engineering. IEEE/ACM Trans Netw 19(6):1717–1730

    Article  Google Scholar 

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York

    MATH  Google Scholar 

  • Deb K, Agrawal S, Pratap A, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):82–97

    Article  Google Scholar 

  • Dijkstra E (1959) A note on two problems in connexion with graphs. Numer Math 1(1):269–271

    Article  MathSciNet  Google Scholar 

  • Ericsson M, Resende M, Pardalos P (2002) A genetic algorithm for the weight setting problem in OSPF routing. J Combin Optim 6:299–333

    Article  MathSciNet  Google Scholar 

  • Evangelista P, Maia P, Rocha M (2009) Implementing metaheuristic optimization algorithms with jecoli. In Proceedings of the 2009 ninth international conference on intelligent systems design and applications, ISDA ’09, Washington, DC, USA. IEEE Computer Society, pp 505–510

  • Floyd S, Fall K (1999) Promoting the use of end-to-end congestion control in the internet. IEEE/ACM Trans Netw 7(4):458–472

    Article  Google Scholar 

  • Fortz B (2000) Internet traffic engineering by optimizing OSPF weights. In: Proceedings of IEEE INFOCOM, pp 519–528

  • Fortz B, Thorup M (2003) Robust optimization of OSPF/IS-IS weights. In: Ben-Ameur W, Petrowski A (eds) Proceedings of INOC, pp 225–230

  • Goldberg E (1989) Genetic algorithms in search, optimization and machine learning, 1st edn, Addison-Wesley

  • Gredler H, Goralski W (2005) The complete IS-IS routing protocol. Springer, Berlin

    MATH  Google Scholar 

  • Hopps C (2000) Analysis of an equal-cost multi-path algorithm, internet engineering task force , IETF , RFC 2992 (Informational)

  • Iannaccone G, Chuah C, Mortier R, Bhattacharyya S, Diot C (2002) Analysis of link failures in an ip backbone. In: Proceedings of the 2nd ACM SIGCOMM workshop on internet measurment, IMW ’02, New York, NY, USA, ACM, pp 237–242

  • Jain S, Kumar A, Mandal S, Ong J, Poutievski L, Singh A, Venkata S, Wanderer J, Zhou J, Zhu M, Zolla J, Hölzle U, Stuart S, Vahdat A(2013) B4: experience with a globally-deployed software defined wan. In: Proceedings of the ACM SIGCOMM conference on SIGCOMM (SIGCOMM ’13), ACM, New York, NY, USA, pp 3–14

  • Knowles J, Corne D (2000) Approximating the nondominated front using the Pareto archived evolution strategy. Evol Comput 8(2):149–172

    Article  Google Scholar 

  • Medina A, Lakhina A, Matta I, Byers J (2001) Brite: universal topology generation from a user’s perspective. Technical report, Boston, MA, USA

  • Moy J (1998) OSPF Version 2. RFC 2328 (Standard), April . Updated by RFC 5709

  • Pereira V, Rocha M, Cortez P, Rio M, and Sousa P (2013) A framework for robust traffic engineering using evolutionary computation. In: 7th International conference on autonomous infrastructure, management and security (AIMS 2013), Barcelona, Spain, Springer, LNCS 7943, pp 2–13

  • Pereira V, Sousa P, Cortez P, Rio M, Rocha M (2015) Comparison of single and multiobjective evolutionary algorithms for robust link-state routing. In: Proceedings of evolutionary multi-criterion optimization (EMO2015), Springer, Guimarães, Portugal, pp 573–87

  • Pereira V, Rocha M, Sousa P (2016)optimizing load balancing routing mechanisms with evolutionary computation. In: Proceedings of international workshop on future internet and smart networks (FI&SN 2016), IOS Press, Ambient Intelligence and Smart Environments, Intelligent Environments, vol 21

  • Razali N and Geraghty J (2011) Genetic algorithm performance with different selection strategies in solving TSP. In: Proceedings of the world congress on engineering 2011, Vol II WCE 2011, London, U.K, July 6–8

  • RFC 3954-Cisco Systems NetFlow services export version 9, B. Claise, Ed., Cisco Systems, October 2004

  • Rocha M, Sousa P, Cortez P, Rio M (2011) Quality of service constrained routing optimization using evolutionary computation. Appl Soft Comput 11(1):356–364

    Article  Google Scholar 

  • Schaffer J (1984) Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition), PhD thesis, Vanderbilt University

  • Schaffer J (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st international conference on genetic algorithms, L. Erlbaum Associates Inc., Hillsdale, NJ, USA, pp 93–100

  • Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–24

    Article  Google Scholar 

  • Suchara M, Xu D, Doverspike R, Johnson D, Rexford J (2011) Network architecture for joint failure recovery and traffic engineering. In: Proceedings of SIGMETRICS11 conference

  • Tan KC, Lee TH, Khor EF (2002) Evolutionary algorithms for multi-objective optimization: Performance assessments and comparisons. Artif Intell Rev 17(4):251–290

    Article  Google Scholar 

  • Technologies Cariden (2012) Building traffic matrices: introduction to MATE flow collection. White Paper-Version 2

  • Tune P, Roughan M (2014) Network design Sensitivity Analysis. In: The 2014 ACM international conference on measurement and modeling of computer systems, ACM, SIGMETRICS 14, pp 449–461

  • Villamizar C, Song C (1994) High performance TCP in ANSNET. Comput Commun Rev 24(5):45–60

    Article  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82

    Article  Google Scholar 

  • Zitzler E, Thiele L (1998) An evolutionary algorithm for multiobjective optimization: the strength Pareto approach. Technical report 43, Gloriastrasse 35, CH-8092 Zurich, Switzerland

  • Zitzler E, Laumanns M, Thiele L (2001) Spea2: improving the strength pareto evolutionary algorithm. Technical report

Download references

Acknowledgements

This work has been supported by FCT-Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020 and UIDB/04469/2020 units.

Author information

Authors and Affiliations

  1. Department of Informatics, Centre of Biological Engineering, University of Minho, Braga, Portugal

    Vítor Pereira & Miguel Rocha

  2. Department of Informatics, Centro Algoritmi, University of Minho, Braga, Portugal

    Pedro Sousa

Authors
  1. Vítor Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pedro Sousa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miguel Rocha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vítor Pereira.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pereira, V., Sousa, P. & Rocha, M. A comparison of multi-objective optimization algorithms for weight setting problems in traffic engineering. Nat Comput 21, 507–522 (2022). https://doi.org/10.1007/s11047-020-09807-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-020-09807-1

Keywords

Search

Navigation