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Dynamic Resource Allocation in Quality of Service Networks

  • Antonio Capone
  • Jocelyne Elias
  • Fabio Martignon
  • Guy Pujolle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3883)

Abstract

Efficient dynamic resource provisioning algorithms are necessary to the development and automation of Quality of Service (QoS) networks. The main goal of these algorithms is to offer services that satisfy the QoS requirements of individual users while guaranteeing at the same time an efficient utilization of network resources. In this paper we introduce a new service model that provides quantitative per-flow bandwidth guarantees, where users subscribe for a guaranteed rate; moreover, the network periodically individuates unused bandwidth and proposes short-term contracts where extra-bandwidth is allocated and guaranteed exclusively to users who can exploit it to transmit at a rate higher than their subscribed rate. To implement this service model we propose a dynamic provisioning architecture for intra-domain Quality of Service networks. We develop an efficient bandwidth allocation algorithm that takes explicitly into account traffic statistics to increase the users’ benefit and the network revenue simultaneously. We demonstrate through simulation in realistic network scenarios that the proposed dynamic provisioning model is superior to static provisioning in providing resource allocation both in terms of total accepted load and network revenue.

Keywords

Allocation Algorithm Bandwidth Allocation Policy Decision Point Dynamic Bandwidth Allocation Dynamic Resource Allocation 
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.

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References

  1. 1.
    Campbell, A.T., Liao, R.R.-F.: Dynamic Core Provisioning for Quantitative Differentiated Services. IEEE/ACM Transactions on Networking 12(3), 429–442 (2004)CrossRefGoogle Scholar
  2. 2.
    Schulzrinne, H., Wang, X.: Incentive-Compatible Adaptation of Internet Real-Time Multimedia. IEEE Journal on Selected Areas in Communications 23(2), 417–436 (2005)CrossRefGoogle Scholar
  3. 3.
    Campbell, A.T., Liao, R.R.-F.: Dynamic Edge Provisioning for Core IP Networks. In: Proc. IEEE/IFIP Int’l Workshop on Quality of Service IWQOS, Pittsburgh, USA (June 2000)Google Scholar
  4. 4.
    Ahmed, T., Boutaba, R., Mehaoua, A.: A Measurement-Based Approach for Dynamic QoS Adaptation in DiffServ Network. Journal of Computer Communications, Special issue on End-to-End Quality of Service Differentiation, Elsevier Science (2004)Google Scholar
  5. 5.
    de Meer, H., O’Hanlon, P.: Segmented Adaptation of Traffic aggregates. In: Proc. 9th International Workshop on Quality of Service IWQOS, Karlsruhe, Germany, June 6-8, pp. 342–353 (2001)Google Scholar
  6. 6.
    Mahajan, M., Parasharand, M., Ramanathan, A.: Active Resource Management for the Differentiated Services Environment. International Journal of Network Management 14(3), 149–165 (2004)CrossRefGoogle Scholar
  7. 7.
    Cao, Z., Zegura, E.: Utility Max-Min: An Application-Oriented Bandwidth Allocation Scheme. In: Proc. IEEE Infocom 1999, New York, USA (March 1999)Google Scholar
  8. 8.
    Kelly, F.: Charging and rate control for elastic traffic. European Transactions on Telecommunications 8, 33–37 (1997)CrossRefGoogle Scholar
  9. 9.
    Aweya, J., Ouellette, M., Montuno, D.Y.: A simple, scalable and provably stable explicit rate computation scheme for flow control in computer networks. Int. J. Commun. Syst. 14(6), 593–618 (2001)CrossRefzbMATHGoogle Scholar
  10. 10.
    Aweya, J., Ouellette, M., Montuno, D.Y.: Design and stability analysis of a rate control algorithm using the Routh-Hurwitz stability criterion. IEEE/ACM Transactions on Networking 12(4), 719–732 (2004)CrossRefGoogle Scholar
  11. 11.
    Bertsekas, D., Gallager, R.: Data Networks, 2nd edn. Prentice-Hall, Englewood Cliffs (1992)zbMATHGoogle Scholar
  12. 12.
    Breslau, L., Shenker, S.: Best-Effort versus Reservations: A Simple Comparative Analysis. In: Proc. ACM SIGCOMM, September 1998, pp. 3–16 (1998)Google Scholar
  13. 13.
    Lee, J.W., Mazumdar, R.R., Shroff, N.B.: Non-Convex Optimization and Rate Control for Multi-Class Services in the Internet. IEEE/ACM Transactions on Networking 13(4), 827–840 (2005)CrossRefGoogle Scholar
  14. 14.
    Schulzrinne, H., Wang, X.: RNAP: A resource negotiation and pricing protocol. In: Int. Workshop Netw. Oper. Syst. Support Digital Audio Video, Basking Ridge, NJ, June 1999, pp. 77–93 (1999)Google Scholar
  15. 15.
    J-Sim. Ohio State University, Available at: www.j-sim.org

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Antonio Capone
    • 1
  • Jocelyne Elias
    • 2
  • Fabio Martignon
    • 3
  • Guy Pujolle
    • 2
  1. 1.Department of Electronics and InformationPolitecnico di MilanoItaly
  2. 2.LIP6 LaboratoryUniversity of Paris 6ParisFrance
  3. 3.Department of Management and Information TechnologyUniversity of BergamoItaly

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