Skip to main content
Log in

Video server scheduling using random early request migration

  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract.

Video request migration among servers to achieve effective video-on-demand (VoD) services is investigated in this work. Our study is focused on the design and analysis of a random early migration (REM) scheme for user requests. When a new request is dispatched to a video server, the REM-based scheduler decides whether request migration is needed with a certain probability, which is a function of the service load. To analyze the request migration process, we introduce a state matrix representation that stores the service load information of each video server and plays an important role in the determination of migration paths. Based on this representation, we develop two methods to calculate performance metrics: the service failure rate and the system delay in service migration. Simulation results show that the REM scheme outperforms both the DASD dancing algorithm [1] and the traditional migration scheme adopted in [2,3] with shorter service delay and lower failure rates. It is also confirmed that our theoretical results match well with experimental results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Wolf JL, Yu PS, Shachnai H (1997) Disk load balancing for video-on-demand systems. Multimedia Syst 5:358-370

    Article  Google Scholar 

  2. Tsao S, Chen MC, Ko M, Ho J, Huang YM (1999) Data allocation and dynamic load balancing for distributed video storage server. J Vis Commun Image Represent 10:197-218

    Google Scholar 

  3. Mundur P, Simon R, Sood AK (2004) End-to-end analysis of distributed video-on-demand systems. IEEE Trans Multimedia 6:129-141

    Article  Google Scholar 

  4. Tewari R, Dias D, Mukherjee R, Vin H (1995) High availability in clustered video server. Technical Report RC20108, IBM TJ Watson Research Center

  5. Tewari R, Mukherjee R, Dias D, Vin H (1996) Design and performance tradeoffs in clustered video servers. In: 3rd IEEE international conference on multimedia computing and systems, pp 144-150

  6. Bolosky WJ, Barrera JS, Draves RP, Fitzgerald RP, Gibson GA, Jones MB, Levi SP, Myhrvold RF, Rashid NP (1996) The tiger video file server. In: Proceedings of the 6th international workshop on network and operating system support for digital audio and video

  7. Lee JYB (1998) Parallel video servers: a tutorial. IEEE Multmedia 5:20-28

    Google Scholar 

  8. Chen P, Lee E, Gibson G, Katz R, Patterson D (1994) Raid: high-performance, reliable secondary storage. ACM Comput Surv 26:145-185

    Article  Google Scholar 

  9. Little TDC, Venkatesh D (1994) Probability-based assignment of videos to storage devices in a video-on-demand system. Multimedia Syst 2:280-287

    Google Scholar 

  10. Serpanos DN, Georagiadis L, Bouloutas T (1996) Mmpacking: a load and storage balancing algorithm for distributed multimedia servers. Technical Report RC20410, IBM TJ Watson Research Center

  11. Bisdikian CC, Patel BV (1995) Issues on movie allocation in distributed video-on-demand systems. In: Proceedings of the international conference on communications (ICC ‘95), pp 250-255

  12. Venkatasubramanian N, Ramanthan S (1997) Load management in distributed video servers. In: Proceedings of the 17th international conference on distributed computing systems, pp 528-535

  13. Lougher P, Lougher R, Shepherd D, Pegler D (1996) A scalable hierarchical video storage architecture. In: SPIE conference on multimedia computing and networking, pp 18-29

  14. Guo J, Taylor PG, Zukerman M, Chan S, KS Tang, Wong EWM (2003) On the efficient use of video-on-demand storage facility. In: IEEE international conference on multimedia and expo (ICME’03), 2:329-332

  15. Doganata YN, Tantawi AN (1994) Making a cost-effective video server. IEEE Multimedia 1:22-30

    Article  Google Scholar 

  16. Schaffa F, Nussbaumer J-P (1995) On bandwidth and storage tradeoffs in multimedia distribution networks. In: INFOCOM ‘95, 14th annual joint conference of the IEEE computer and communications societies, 3:1020-1026

  17. Barnett SA, Anido, GJ (1996) A cost comparison of distributed and centralized approaches to video-on-demand. IEEE J Select Areas Commun 14:1173-1183

    Article  Google Scholar 

  18. Chan S-HG, Tobagi F (2001) Distributed servers architecture for networked video services. IEEE/ACM Trans Network 9:125-136

    Google Scholar 

  19. L VOK, Liao W, Qiu X, Wong EWM (1996) Performance model of interactive video-on-demand systems. IEEE J Select Areas Commun 14:1099-1109

    Google Scholar 

  20. Shu W, Wu M (2004) Resource requirements of closed-loop video delivery services. IEEE Multimedia 11:24-37

    MathSciNet  Google Scholar 

  21. Floyd S, Jacobson V (1993) Random early detection gateways for congestion avoidance. IEEE/ACM Trans Network 1:397-413

    Google Scholar 

  22. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1:269-271

    Article  MATH  MathSciNet  Google Scholar 

  23. Zipf G (1949) Human behavior and the principle of least effort. Addison-Wesley, Reading, MA

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yinqing Zhao.

Additional information

Revised: 24 October 2004, Published online: 8 April 2005

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhao, Y., Kuo, CC.J. Video server scheduling using random early request migration. Multimedia Systems 10, 302–316 (2005). https://doi.org/10.1007/s00530-004-0164-1

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-004-0164-1

Keywords:

Navigation