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Quality of service evaluations of multicast streaming protocols

Published:01 June 2002Publication History

ABSTRACT

Recently proposed scalable on-demand streaming protocols have previously been evaluated using a system cost measure termed the "required server bandwidth". For the scalable protocols that provide immediate service to each client when the server is not overloaded, this paper develops simple analytic models to evaluate two client-oriented quality of service metrics, namely (1) the mean client waiting time in systems where clients are willing to wait if a (well-provisioned) server is temporarily overloaded, and (2) the fraction of clients who balk (i.e., leave without receiving their requested media content) in systems where the clients will tolerate no or only very low service delays during a temporary overload. The models include novel approximate MVA techniques that appear to extend the range of applicability of customized AMVA to include questions focussed on state probabilities rather than on mean values, and to systems in which the operating points of interest do not include substantial client queues. For example, the new AMVA models accurately estimate the server bandwidth needed to achieve a balking rate as low as one in ten thousand. The analytic models can easily be applied to determine the server bandwidth needed for a given number of media files, anticipated total client request rate and file access frequencies, and target balking rate or mean wait. Results show that (a) scalable media servers that are configured with the "required server bandwidth" defined in previous work have low mean wait but may have unacceptably high client balking rates (i.e., greater than one in twenty), (b) for high to moderate client load, only a 10 - 50% increase in the previously defined required server bandwidth is needed to achieve a very low balking rate (e.g., one in ten thousand), and (c) media server performance (either mean wait or balking rate) degrades rapidly if the actual client load is more than 10% greater than the anticipated load.

References

  1. Y. Bard, "A Model of Shared DASD and Multipathing", Comm. ACM 23, 10 (Oct. 1980), pp. 564-572.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Y. Bard, "A Simple Approach to System Modeling", Performance Evaluation 1, 3 (Aug. 1981), pp. 225-248.]]Google ScholarGoogle ScholarCross RefCross Ref
  3. A. Bar-Noy, G. Goshi, R. E. Ladner, and K. Tam, "Comparison of Stream Merging Algorithms for Media-on-Demand", Proc. MMCN 2002, San Jose, CA, Jan. 2002.]]Google ScholarGoogle Scholar
  4. F. Baskett, K. M. Chandy, R. R. Muntz, and F. G. Palacios, "Open, Closed and Mixed Networks of Queues with Different Classes of Customers", J. ACM 22, 2 (Apr. 1975), pp. 248-260.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. S. Carter and D. Long, "Improving Video-on-Demand Server Efficiency Through Stream Tapping", Proc. ICCCN '97, Las Vegas, NV, Sept. 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Y. Cai, K. A. Hua, and K. Vu, "Optimizing Patching Performance", Proc. MMCN '99, San Jose, CA, Jan. 1999.]]Google ScholarGoogle Scholar
  7. E. G. Coffman, Jr., P. Jelenkovic, and P. Momcilovic, "Provably Efficient Stream Merging", Proc. 6th Int'l. Workshop on Web Caching and Content Distribution, Boston, MA, June 2001.]]Google ScholarGoogle Scholar
  8. A. Dan, P. Shahabuddin, D. Sitaram, and D. Towsley, "Channel Allocation under Batching and VCR Control in Video-on-Demand Systems", J. Parallel and Distributed Computing 30, 2 (Nov. 1995), pp. 168-179.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. D. L. Eager and M. K. Vernon, "Dynamic Skyscraper Broadcasts for Video-on-Demand", Proc. MIS '98, Istanbul, Turkey, Sept. 1998.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. D. L. Eager, M. K. Vernon and J. Zahorjan, "Bandwidth Skimming: A Technique for Cost-Effective Video-on-Demand", Proc. MMCN 2000, San Jose, CA, Jan. 2000.]]Google ScholarGoogle Scholar
  11. D. L. Eager, M. K. Vernon and J. Zahorjan, "Minimizing Bandwidth Requirements for On-Demand Data Delivery", IEEE Trans. on Knowledge and Data Engineering 13, 5 (Sept./Oct. 2001), pp. 742-757.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. D. L. Eager, M. K. Vernon and J. Zahorjan, "Optimal and Efficient Merging Schedules for Video-on-Demand Servers", Proc. ACM MULTIMEDIA '99, Orlando, FL, Nov. 1999.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. L. Gao, J. Kurose, and D. Towsley, "Efficient Schemes for Broadcasting Popular Videos", Proc. NOSSDAV '98, Cambridge, UK, July 1998.]]Google ScholarGoogle Scholar
  14. L. Gao and D. Towsley, "Supplying Instantaneous Video-on-Demand Services Using Controlled Multicast", Proc. ICMCS '99, Florence, Italy, June 1999.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. A. Hu, "Video-on-Demand Broadcasting Protocols: A Comprehensive Study", Proc. IEEE Infocom 2001, Anchorage, AL, Apr. 2001.]]Google ScholarGoogle Scholar
  16. K. A. Hua, Y. Cai and S. Sheu, "Patching: A Multicast Technique for True Video-on-Demand Services", Proc. ACM MULTIMEDIA '98, Bristol, U.K., Sept. 1998.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. K. A. Hua and S. Sheu, "Skyscraper Broadcasting: A New Broadcasting Scheme for Metropolitan Video-on-Demand Systems", Proc. ACM SIGCOMM '97, Cannes, Sept. 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. L. Kleinrock, Queueing Systems Volume 1: Theory, John Wiley and Sons, New York, NY, 1975.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. E. D. Lazowska, J. Zahorjan, G. S. Graham, and K. C. Sevcik, Quantitative System Performance, Prentice-Hall, Englewood Cliffs, NJ, 1984.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J. F. Paris, S. W. Carter, and D. D. E. Long, "A Hybrid Broadcasting Protocol for Video on Demand", Proc. MMCN '99, San Jose, CA, Jan. 1999.]]Google ScholarGoogle Scholar
  21. P. Schweitzer, "Approximate Analysis of Multiclass Closed Networks of Queues", International Conference on Stochastic Control and Optimization, Amsterdam, Netherlands, 1979.]]Google ScholarGoogle Scholar
  22. S. Sen, L. Gao, J. Rexford, and D. Towsley, "Optimal Patching Schemes for Efficient Multimedia Streaming", Proc. NOSSDAV '99, Basking Ridge, NJ, June 1999.]]Google ScholarGoogle Scholar
  23. S. Viswanathan and T. Imielinski, "Metropolitan Area Video-on-Demand Service using Pyramid Broadcasting", Multimedia Systems 4, 4 (Aug. 1996), pp. 197-208.]] Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Conferences
    SIGMETRICS '02: Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
    June 2002
    299 pages
    ISBN:1581135319
    DOI:10.1145/511334
    • cover image ACM SIGMETRICS Performance Evaluation Review
      ACM SIGMETRICS Performance Evaluation Review  Volume 30, Issue 1
      Measurement and modeling of computer systems
      June 2002
      286 pages
      ISSN:0163-5999
      DOI:10.1145/511399
      Issue’s Table of Contents

    Copyright © 2002 ACM

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    Publication History

    • Published: 1 June 2002

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    SIGMETRICS '02 Paper Acceptance Rate23of170submissions,14%Overall Acceptance Rate459of2,691submissions,17%

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