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.
- Y. Bard, "A Model of Shared DASD and Multipathing", Comm. ACM 23, 10 (Oct. 1980), pp. 564-572.]] Google ScholarDigital Library
- Y. Bard, "A Simple Approach to System Modeling", Performance Evaluation 1, 3 (Aug. 1981), pp. 225-248.]]Google ScholarCross Ref
- 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 Scholar
- 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 ScholarDigital Library
- S. Carter and D. Long, "Improving Video-on-Demand Server Efficiency Through Stream Tapping", Proc. ICCCN '97, Las Vegas, NV, Sept. 1997.]] Google ScholarDigital Library
- Y. Cai, K. A. Hua, and K. Vu, "Optimizing Patching Performance", Proc. MMCN '99, San Jose, CA, Jan. 1999.]]Google Scholar
- 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 Scholar
- 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 ScholarDigital Library
- D. L. Eager and M. K. Vernon, "Dynamic Skyscraper Broadcasts for Video-on-Demand", Proc. MIS '98, Istanbul, Turkey, Sept. 1998.]] Google ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- L. Gao, J. Kurose, and D. Towsley, "Efficient Schemes for Broadcasting Popular Videos", Proc. NOSSDAV '98, Cambridge, UK, July 1998.]]Google Scholar
- L. Gao and D. Towsley, "Supplying Instantaneous Video-on-Demand Services Using Controlled Multicast", Proc. ICMCS '99, Florence, Italy, June 1999.]] Google ScholarDigital Library
- A. Hu, "Video-on-Demand Broadcasting Protocols: A Comprehensive Study", Proc. IEEE Infocom 2001, Anchorage, AL, Apr. 2001.]]Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- L. Kleinrock, Queueing Systems Volume 1: Theory, John Wiley and Sons, New York, NY, 1975.]] Google ScholarDigital Library
- E. D. Lazowska, J. Zahorjan, G. S. Graham, and K. C. Sevcik, Quantitative System Performance, Prentice-Hall, Englewood Cliffs, NJ, 1984.]] Google ScholarDigital Library
- 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 Scholar
- P. Schweitzer, "Approximate Analysis of Multiclass Closed Networks of Queues", International Conference on Stochastic Control and Optimization, Amsterdam, Netherlands, 1979.]]Google Scholar
- 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 Scholar
- S. Viswanathan and T. Imielinski, "Metropolitan Area Video-on-Demand Service using Pyramid Broadcasting", Multimedia Systems 4, 4 (Aug. 1996), pp. 197-208.]] Google ScholarDigital Library
Recommendations
Quality of service evaluations of multicast streaming protocols
Measurement and modeling of computer systemsRecently 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 ...
Best-Effort Patching for Multicast True VoD Service
A multicast Video-on-Demand (VoD) system allows clients to share a server stream by batching their requests, and hence, improves channel utilization. However, it is very difficult to equip such a VoD system with full support for interactive VCR ...
Multicast protocols for scalable on-demand download
Previous scalable protocols for downloading large, popular files from a single server include batching and cyclic multicast. With batching, clients wait to begin receiving a requested file until the beginning of its next multicast transmission, which ...
Comments