Admission control for media on demand services

  • Martin Bichler
  • Thomas SetzerEmail author
Original Paper


Admission control software is used to make accept or deny decisions about incoming service requests to avoid overload. Existing media streaming software includes only limited support for admission control by allowing for predefined static rules. Such rules limit for example the number of requests that are allowed to enter the system during a certain time or define thresholds concerning the utilization level of a single resource such as network bandwidth. In media streaming applications, however, the bottleneck resource (CPU, Disk I/O, network bandwidth, etc.) might change over time depending on the current demand for different types of audio or video files. This paper proposes a model for adaptive admission control in the presence of multiple scarce resources. Opportunity costs for a service request are determined at the moment of an incoming request and compared to the revenue of a request in order to make an accept/deny decision. Opportunity costs are based on resource utilization, service resource requirements, expected future demand for services, and the revenue per accepted service. The model allows rejection of service requests early to reserve capacity required to perform future service requests with higher revenues. We describe a number of experiments to illustrate the benefits of adaptive admission control models over static admission control rules.


Admission control IT service Management Media Streaming Service Level Management 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Frost&Sullivan (2005) World media streaming platform markets. Frost & Sullivan, Palo Alto, USAGoogle Scholar
  2. 2.
    Cherkasova L, Tang W, Vahdat A (2004) MediaGuard: a model-based framework for building QoS-aware streaming media services. HP Labs Report No. HPL-2004-25Google Scholar
  3. 3.
    Cherkasova L, Staley L (2003) Measuring the capacity of a streaming media server in a utility data center environment. Internet Systems and Storage Laboratory, HP Laboratories, Palo Alto, USAGoogle Scholar
  4. 4.
    OGC (2002) ITIL best practice for service delivery 4th edn. The Stationary Office, NorwichGoogle Scholar
  5. 5.
    OGC (2003) IT Infrastructure Library (ITIL). [World Wide Web Resource, cited 2005 2005-09-18]; Available from: Scholar
  6. 6.
    Xia Z et al (2006) An integrated admission control scheme for the delivery of streaming media. J Parallel Distrib Comput 66(3):334–344zbMATHCrossRefGoogle Scholar
  7. 7.
    Yubing Wang MC, Zheng Zuo (2001) An empirical study of realvideo performance across the Internet. In: ACM SIGCOMM internet measurement workshop, San Francisco, USAGoogle Scholar
  8. 8.
    Kim RY, Manas T, Pramod KSU (2005) Policy-based admission control and bandwidth reservation for future sessions. European Patent OfficeGoogle Scholar
  9. 9.
    Vin H, Goyal A, Goyal P (1994) A statistical admission control algorithm for multimedia servers. In: International multimedia conference, San Francisco, USAGoogle Scholar
  10. 10.
    Chen I -R, Chen C -M (1996) Threshold-based admission control policies for multimedia servers. Comput J 39(9):757–766CrossRefGoogle Scholar
  11. 11.
    Cheng S, Chen C, Chen I (2003) Performance evaluation of an admission control algorithm: dynamic threshold with negotiation. Perform Eval 52(1):1–13CrossRefGoogle Scholar
  12. 12.
    Acharya S, et al. (2000) Characterizing user access to videos on the world wide web. In: ACM / SPIE Multimedia Computing and NetworkingGoogle Scholar
  13. 13.
    Almeida JM, et al. (2001) Analysis of educational media server workloads. In: 11th international workshop on network and operating system suport for digital audio and videoGoogle Scholar
  14. 14.
    Cherkasova L, Gupta M (2002) Characterizing locality, evolution and life span of accesses in enterprise media server workloads. In: 12th international workshop on network and operating system support for digital audio and video ACM NOSSDAVGoogle Scholar
  15. 15.
    Chen X, Mohapatra P, Chen H (2001) An admission control scheme for predictable server response time for web accesses. In: World Wide Web conferenceGoogle Scholar
  16. 16.
    Wang Y, Claypool M, Zuo Z (2001) An empirical study of realvideo performance across the Internet. In: ACM SIGCOMM internet measurement workshop, San Francisco, USAGoogle Scholar
  17. 17.
    Ge Z, Ping J, Shenoy P (2002) A demand adaptive and locality aware (DALA) streaming media server cluster architecture. In: International workshop on network and operating system support for digital audio and video. Miami, USAGoogle Scholar
  18. 18.
    Cherkasova L, Staley L (2003) Building a performance model of streaming media applications in utility data center environments. In: International symposium on cluster computing and the grid (IEEE Computer Society)Google Scholar
  19. 19.
    Apple Computer (2003) QuickTime streaming server 5.0 administration. Apple Computer, IncGoogle Scholar
  20. 20.
    RealNetworks (2005) Helix server administration guide [cited 2006 September, 26, available from: Scholar
  21. 21.
    Microsoft (2006) Windows media services 9 series [cited 2006 September, 12] available from: Scholar
  22. 22.
    Adobe (2006) Flash media server 2 documentation [cited 2006 September, 15] available from: Scholar
  23. 23.
    Kwon J, Yeom H (2000) An admission control scheme for continuous media servers using caching. In: Int’l performance, computing and communication conference (IPCCC), Phoenix, USAGoogle Scholar
  24. 24.
    Vin HM, Goyal A, Goyal P (1994) An observation-based admission control algorithm for multimediaservers. In: Multimedia computing and systems, Boston, USAGoogle Scholar
  25. 25.
    Welsh M, Culler D, Brewer E (2001) SEDA: An architecture for well-conditioned, scalable Internet services. In: 18th symposium on operating systems principles, Chateau Lake Louise, CanadaGoogle Scholar
  26. 26.
    Welsh M, Culler D (2003) Adaptive overload control for busy internet servers. In: 4th usenix conference on internet technologies and systems (USITS)Google Scholar
  27. 27.
    Welsh M, Culler D (2002) Overload management as a fundamental service design primitive. In: Tenth ACM SIGOPS European workshop, Saint-Emilion, FranceGoogle Scholar
  28. 28.
    Williamson (1992) Airline network seat inventory control—methodologies and revenue impacts. In: Department of Aeronautics and Astronautics, MIT, USAGoogle Scholar
  29. 29.
    Talluri KT, van Ryzin GJ (1999) An analysis of bid-price controls for network revenue management. Manage Sci 44:1577–1593CrossRefGoogle Scholar
  30. 30.
    Brandl R (2006) Reinraum-Messungen zur Verrechnung von IT-Anwendungen. In: Multikonferenz Wirtschaftsinformatik, Passau, GermanyGoogle Scholar
  31. 31.
    Nagaprabhanjan B, Apte V (2005) A tool for automated resource consumption profiling of distributed transactions. In: Second international conference, ICDCIT, Bhubaneswar, IndiaGoogle Scholar
  32. 32.
    Kounev S, Buchmann A (2003) Performance modelling and evaluation of large-scale J2EE applications. In: 29th international conference of the computer measurement group (CMG)Google Scholar
  33. 33.
    Microsoft (2006) Windows media load simulator for windows media services 9 series. [cited 2006 Oktober, 9] available from: Scholar
  34. 34.
    Yu H, et al. (2006) Understanding user behavior in large-scale video-on-demand systems. In: EuroSysGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  1. 1.Roland Berger and o2 GermanyTechnische Universität MünchenGarchingGermany

Personalised recommendations