Response Time Comparison in Multi Protocol Label Switching Network Using Ant Colony Optimization Algorithm

  • E. R. Naganathan
  • S. Rajagopalan
  • S. Narayanan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)


Multi-Protocol Label Switching (MPLS) netwoks transfers the data whith the help of labels. MPLS creates "virtual links" between distant nodes. MPLS can encapsulate packets of various network protocols. In MPLS packet-forwarding decisions are made solely on the contents of this label, without the need to examine the packet itself. This allows to create end-to-end circuits using any protocol. Congestion Control and Congestion avoidence is the main task in Traffic Engineering. Slow Start, ECN, RED, AIMD are some of the techniques available for congestion management. This paper compares the response time in MPLS network using Ant Colony Optimization (ACO) technique to avoid congestion and gives good results in terms of response time.


Ant Colony Optimization MPLS Network Traffic Management 


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  1. 1.
    Chou, C.T.: Traffic engineering for MPLS-based virtual private networks. Computer Networks 44, 319–333 (2004)CrossRefMATHGoogle Scholar
  2. 2.
    Srivastava, S., van de Liefvoort, A., Medhi, D.: Traffic engineering of MPLS backbone networks in the presence of heterogeneous streams. Computer Networks 53, 2688–2702 (2009)CrossRefMATHGoogle Scholar
  3. 3.
    Palmieri, F.: An MPLS-based architecture for scalable QoS and traffic engineering in converged multiservice mobile IP networks. Computer Networks 47, 257–269 (2005)CrossRefGoogle Scholar
  4. 4.
    Boscoa, A., Bottab, A., Conteb, G., Iovannaa, P., Sabellaa, R., Salsanoc, S.: Internet like control for MPLS based traffic engineering: performance evaluation. Performance Evaluation 59, 121–136 (2005)CrossRefGoogle Scholar
  5. 5.
    Iovanna, P., Sabella, R., Settembre, M.: Traffic engineering strategy for multi-layer networks based on the GMPLS paradigm. IEEE Netw. 17(2), 28–37 (2003)CrossRefGoogle Scholar
  6. 6.
    Di Caro, G., Dorigo, M.: AntNet: A Mobile Agents Approach to Adaptive Routing. Tech. Rep. IRIDIA/97-12, Univ. Libre de Bruxelles, Brussels, Belgium (1997)Google Scholar
  7. 7.
    Schoonderwoerd, R., Holland, O., Bruten, J.: Ant like agents for load balancing in tele-communication networks. In: Proceedings of the First Int. Conf. on Autonomous Agents, pp. 209–216. ACM Press, New York (1997)CrossRefGoogle Scholar
  8. 8.
    Duan, H., Yu, X.: Hybrid Ant Colony Optimization Using Memetic Algorithm for Traveling Salesman Problem. In: Proceedings of the IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning, pp. 92–95 (2007)Google Scholar
  9. 9.
    Subramanian, D., Druschel, P., Chen, J.: Ants and reinforcement learning: A case study in routing in dynamic networks. In: Proceedings of the 15th Int. Joint Conf. on Artificial Intelligence, pp. 823–838. Morgan Kaufmann, San Francisco (1997)Google Scholar
  10. 10.
    Sim, K.M., Sun, W.H.: Ant Colony Optimization for Routing and Load-Balancing: Survey and New Directions. IEEE Transactions on Systems, Man, and Cybernetics 33(5), 560–572 (2003)CrossRefGoogle Scholar
  11. 11.
    Xing, L.-N., Chen, Y.-W., Wang, P., Zhao, Q.-S., Xiong, J.: A Knowl-edge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems. Applied Soft Computing 10, 888–896 (2010)CrossRefGoogle Scholar
  12. 12.
    Lopez-Ibanez, M., Blum, C.: Beam ACO for the traveling sales man problem with time windows. Computers & Operations Research 37, 1570–1583 (2010)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Chandra Mohan, B., Sandeep, R., Sridharan, D.: A Data Mining Approach for Predicting Reliable Path for Congestion Free Routing Using Self-motivated Neural Network. In: Lee, R. (ed.) Soft. Eng., Arti. Intel., Net. & Para./Distri. Comp., vol. 149, pp. 237–246. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Chandra Mohan, B., Baskaran, R.: Redundant Link Avoidance Algorithm for improving Network Efficiency. International Journal of Computer Science Issues 7(3) (May 2010)Google Scholar
  15. 15.
    Rajagopalan, S., Naganathan, E.R., Herbert Raj, P.: Ant Colony Optimization Based Congestion Control Algorithm for MPLS Network. In: Mantri, A., Nandi, S., Kumar, G., Kumar, S. (eds.) HPAGC 2011. CCIS, vol. 169, pp. 214–223. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • E. R. Naganathan
    • 1
  • S. Rajagopalan
    • 2
  • S. Narayanan
    • 2
  1. 1.Dept. of Computer ScienceHindustan UniversityChennaiIndia
  2. 2.Dept. of CSEAlagappa UniversityKaraikudiIndia

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