Application of Stochastic Model on Routing Technique in Multi Class Queueing Network

  • K. Sivaselvan
  • C. Vijayalakshmi Seshathri
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 132)


In communication networks, the network size is growing hasty and the computation effort to finds a path between the source –destination pairs is increased massively. Multiple paths may exists between the source-destination nodes which direct that traffic load variations, overhead, response time take place. Routing plays a vital role on the performance and functionality of computer networks. Routing networks means identifying a path in the network that optimizes a certain criterion which is called as Quality of Service (QoS) routing and it is failure in the environment of large scale networks The storage and updating cost of routing procedure is prohibitive as the number of nodes in the network gets large. Stochastic techniques have assumed a prominent role in computer graphics, because of their success in modeling a variety of complex and natural phenomena. The usefulness of a particular stochastic model depends on both its computational advantages and on the extent to which can be adjusted to describe different phenomena. Network isolation is a key solution for improving the scalability problem in large networks. The main aim of isolation is minimizing the computation effort by maximizing the probability of having a path between source-destination pairs in the network. This paper deals with the specification and analysis of routing procedures that are effective for large hoard and promote packet switched computer networks. The new concept of stochastic isolation method introduced to resolve the scalability in Quality of Service routing algorithm. Graphical representation shows that how the new method improves the performance measure in terms of reduction in computational effort.


Destination Node Connection Request IEEE INFOCOM Destination Pair Packet Switching Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Amitabh Mishra, I.: Scalability in communication network. IEEE Networks 16(4), 10 (2002)CrossRefGoogle Scholar
  2. Orda, A., Sprintson, A.: Precomputation schemes for QoS routing. IEEE/ACM Trans. Netw. 11(4), 578–591 (2003)CrossRefGoogle Scholar
  3. Orda, A., Sprinston, A.: A scalable approach to the partition of QoS requirements in Unicast and Mulitcast. In: IEEE INFOCOM, vol. (1), pp. 685–694 (2002)Google Scholar
  4. Leonardi, E., Mellia, M., Ajmone Marsan, M., Neri, F.: Joint optimal scheduling and routing for maximum network throughput. In: Proc. IEEE INFOCOM 2005, Miami, FL, pp. 819–830 (June 2005)Google Scholar
  5. Hao, F., Zegura, E.W.: On Scalable QoS Routing Performance Evaluation of Topology Aggregation. In: IEEE INFOCOM 2002, vol. (1), pp. 147–156 (March 2002)Google Scholar
  6. Bettahar, H., Bouabdallah, A.: A New approach for Delay-Constrained routing. Elsevier Publication-Computer Communication 25, 1751–1764 (2002)Google Scholar
  7. Zhang, K.A.S., Kelly, T., Stewart, C.: Operational analysis of processor Speed scaling. In: SPAA (June 2008)Google Scholar
  8. Siva Selvan, K., Vijayalakshmi, C.: Algorithmic Approach For the Design Markovian Queueing Network with Multiple Closed Chains. In: International Conference on TRENDZ Information Sciences and Computing. IEEE xplore, Sathyabama University TISC (2010)Google Scholar
  9. Younis, O., Fahmy, S.: Constraint-based routing in the internet: basic principle and recent research. IEEE Communication Society Surveys & Tutorials 5, Xg3, 42–56 (2003)Google Scholar
  10. Gupta, P., Stolyar, A.L.: Optimal throughput allocation in general random access networks. In: Proceedings of 40th Annual Conf. Inf. Sci. Systems, pp. 1254–1259 (2006)Google Scholar
  11. Halabi, S., McPherson, D.: Internet routing architectures, 2nd edn. Cisco Press (2000)Google Scholar
  12. Mao, S., Panwar, S.S., Hou, Y.T.: On minimizing end-to-end delay with optimal traffic partitioning. IEEE Transactions on Vehicular Technology 55(2), 681–690 (2006)CrossRefGoogle Scholar
  13. Bhatti, S.N., Crowcroft, J.: QoS-sensitive flows: Issues in IP packet handling. IEEE Internet Comput. 4, 48–57 (2000)CrossRefGoogle Scholar
  14. Sinha Deb, S., Woodward, M.E.: A New Approach to Scale Quality of Service Routing Algorithms. In: Globecom (2004)Google Scholar
  15. Korkmaz, T., Krunz, M.: Multi-Constrained Optimal Path Selection. In: Proceedings of the IEEE INFOCOM, pp. 834–843 (2001)Google Scholar
  16. Ching, W.-K., Choi, S.-M., Huang, M.: Optimal Service Capacities in a Competitive Multiple-Server Queueing Environment. In: Zhou, J. (ed.) Complex 2009. LNICST, vol. 4, pp. 66–77. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. Liu, W., Lou, W., Fang, Y.: An efficient quality of service routing algorithm for delay-sensitive applications. Elsevier Publication-Computer Networks 47, 87–104 (2005)CrossRefGoogle Scholar
  18. Lin, X., Shroff, N.B.: An optimization based approach for quality of service routing in high-bandwidth networks. In: Presented at the IEEE INFOCOM, Hong Kong, China (March 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • K. Sivaselvan
    • 1
  • C. Vijayalakshmi Seshathri
    • 2
  1. 1.Department of MathematicsJeppiaar Engineering CollegeChennaiIndia
  2. 2.Department of MathematicsSathyabama UniversityChennaiIndia

Personalised recommendations