A Model and a Design Approach to Building QoS Adaptive Systems

  • Paul D. Ezhilchelvan
  • Santosh Kumar Shrivastava
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3069)


This chapter addresses the task of building Internet-based service provisioning systems where the quality of services (QoS) provided should not be perturbed due to changes in execution environments and user requirements. Specifically, it presents a system architecture and identifies a model appropriate for developing distributed programs that would implement the system. The model abstracts the network performance and dependability guarantees typically offered by the Internet service providers and is termed the probabilistic asynchronous model. The protocols for this model are shown to be derivable from those developed for the well-known classical models, namely: the synchronous and the asynchronous models. A protocol for reliable broadcast is derived from a synchronous protocol, together with QoS management algorithms. The system architecture prescribes the role of QoS management algorithms to be: feasibility evaluation on QoS requests from the end users, and adapting system protocols in response to changes in the environments.


Correct Process Failure Detector Delay Distribution Synchronous Model Reliable Broadcast 
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. 1.
    Arvind, K.: Probabilistic Clock Synchronisation in Distributed Systems. IEEE Transactions in Parallel and Distributed Systems 5(5), 475–487 (1994)Google Scholar
  2. 2.
    Birman, K., et al.: Bimodal Multicast. ACM Transactions on Computer Systems 17(2), 41–88 (1999)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Birman, K., Joseph, T.: Reliable Communication in the Presence of Failures. ACM Transactions on Computer Systems 5(1), 47–76 (1987)CrossRefGoogle Scholar
  4. 4.
    Bracha, G., Toueg, S.: Asynchronous consensus and Broadcast Protocols. The Journal of the ACM 32, 824–840 (1985)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Chandra, T.D., Toueg, S.: Unreliable Failure Detectors for Reliable Distributed Systems. Journal of the ACM 43(2), 225–267 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Chandra, T.D., Hadzilacos, V., Toueg, S.: The weakest Failure Detector for Solving Consensus. Journal of the ACM 43(4), 685–722 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Chen, W., Toueg, S., Aguilera, M.K.: On the Quality of Service of Failure Detectors. IEEE Transactions on Computers 51, 561–580 (2002)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Cherkasova, L., Fu, Y., Tang, W., Vahdat, A.: Measuring and Characterizing End-to-End Internet Service Performance. ACM Transactions on Internet Technology 3(4) (November 2003)Google Scholar
  9. 9.
    Cristian, F.: Probabilistic Clock Synchronisation. Distributed Computing 3(3), 146–158 (1989)zbMATHCrossRefGoogle Scholar
  10. 10.
    Cristian, F., Fetzer, C.: The Timed Asynchronous Distributed System Model. IEEE Transactions on Parallel and Distributed Systems 10(6), 642–657 (1999)CrossRefGoogle Scholar
  11. 11.
    Di Ferdinando, A., Ezhilchelvan, P.D., Mitrani, I.: Performance Evaluation of a QoSAdaptive Reliable Multicast Protocol. Technical Report CS-TR-833, School of Computing Science, University of Newcastle (April 2004)Google Scholar
  12. 12.
    Ezhilchelvan, P.D., Mostefaoui, A., Raynal, M.: Randomized Multivalued Consensus. In: The proceedings of the fourth International IEEE Symposium on Object oriented Real-time Computing (ISORC), May 2001, pp. 195–201 (2001)Google Scholar
  13. 13.
    Ezhilchelvan, P.D., Shrivastava, S.K.: rel/REL: A Family of Reliable Multicast Protocols for Distributed Systems. Distributed Systems Engineering 6, 323–331 (1994)CrossRefGoogle Scholar
  14. 14.
    Floyd, S., et al.: A reliable Multicast Framework for Light-Weight Sessions and Application Level Framing. SIGCOMM Computer Communications Review 25(4), 342–356 (1995)CrossRefGoogle Scholar
  15. 15.
    Gibbens, R., et al.: Fixed Point Models for the end-to-end performance analysis of IP Networks. In: Proceedings of the thirteenth ITC Specialist Seminar: IP Traffic Measurement Modelling and Management, Montrey, USA (September 2000)Google Scholar
  16. 16.
    Guerraoui, R.: Revisiting the relationship between Non-blocking Atomic Commitment and Consensus. In: Proceedings of the Ninth International Workshop on Distributed Algorithms, September 1995, Springer, Heidelberg (1995)Google Scholar
  17. 17.
    Gupta, I., Birman, K., Van Renesse, R.: Fighting Fire with Fire: Using a Randomised Gossip to Combat Stochastic Scalability Limits. Quality and Reliability Engineering International 18, 165–184 (2002)CrossRefGoogle Scholar
  18. 18.
    Hadzilacos, V., Toueg, S.: Fault-Tolerant Broadcasts and Related Problems. In: Mullender, S. (ed.) Distributed Systems, pp. 97–146. Addison-Wesley, Reading (1993)Google Scholar
  19. 19.
    Hermant, J.-F., Le Lann, G.: Fast Asynchronous Consensus in Real-Time Distributed Systems. IEEE Transactions on Computers 51(8), 931–944 (2002)CrossRefGoogle Scholar
  20. 20.
    Hiltunen, M., et al.: Real-Time Dependable Channels: Customising QoS Attributes for Distributed Systems. IEEE Trans. on Parallel and Distributed Systems 10(6), 600–612 (1999)CrossRefGoogle Scholar
  21. 21.
    Jacobson, V.: Congestion Avoidance and Control. In: The proceedings of the SIGCOMM symposium, August 1988, pp. 314–332 (1988)Google Scholar
  22. 22.
    Kopetz, H.: Real-Time Systems: Design Principles for Distributed Embedded Applications. Kluwer Academic Publishers, Dordrecht (1997) ISBN 0-7923-9894-7zbMATHGoogle Scholar
  23. 23.
    Miley, M.: Reinventing Business: Application Service Providers. ORACLE Magazine, pp. 48-52 (December 2000)Google Scholar
  24. 24.
    Mishra, S., Fetzer, C., Cristian, F.: The Timewheel Group Communication System. IEEE Transactions on Computers 51(8), 883–889 (2002)CrossRefGoogle Scholar
  25. 25.
    Park, K., Willinger, W.: Self-Similar Network Traffic and Performance Evaluation. John Wiley & Sons, Chichester (2000) ISBN 0-471-31974-0CrossRefGoogle Scholar
  26. 26.
    Pease, M., Shostak, R., Lamport, L.: Reaching Agreement in the Presence of Faults. Journal of the ACM 27(2), 228–234 (1980)zbMATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Pias, M., Wilbur, S.: EdgeMeter: Distributed Network metering. In: Proceedings of the IEEE Openarch 2001 conference, Anchorage, Alaska (April 2001)Google Scholar
  28. 28.
    Verissimo, P., Casimiro, A.: The Timely Computing Base Model and Architecture. IEEE Transaction on Computing Systems 51(8), 916–930 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Paul D. Ezhilchelvan
    • 1
  • Santosh Kumar Shrivastava
    • 1
  1. 1.School of Computing ScienceNewcastle UniversityUK

Personalised recommendations