Abstract
Video on demand, particularly with user-generated content, is emerging as one of the most bandwidth-intensive applications on the Internet. Owing to content control and other issues, some video-on-demand systems attempt to prevent downloading and peer-to-peer content delivery. Instead, such systems rely on server replication, such as via third-party content distribution networks, to support video streaming (or pseudostreaming) to their clients. A major issue with such systems is the cost of the required server resources.
By synchronizing the video streams for clients that make closely spaced requests for the same video from the same server, server costs (such as for retrieval of the video data from disk) can be amortized over multiple requests. A fundamental trade-off then arises, however, with respect to server selection. Network delivery cost is minimized by selecting the nearest server, while server cost is minimized by directing closely spaced requests for the same video to a common server.
This article compares classes of server selection policies within the context of a simple system model. We conclude that: (i) server selection using dynamic system state information (rather than only proximities and average loads) can yield large improvements in performance, (ii) deferring server selection for a request as late as possible (i.e., until just before streaming is to begin) can yield additional large improvements, and (iii) within the class of policies using dynamic state information and deferred selection, policies using only “local” (rather than global) request information are able to achieve most of the potential performance gains.
- Aggarwal, C., Wolf, J., and Yu, P. 1996. On optimal batching policies for video-on-demand storage servers. In Proceedings of the International Conference on Multimedia Computing and Systems (ICMCS'96). 253--258. Google Scholar
Digital Library
- Almeida, J. M., Eager, D. L., Vernon, M. K., and Wright, S. J. 2004. Minimizing delivery cost in scalable streaming content distribution systems. IEEE Trans. Multimedia 6, 2, 356--365. Google Scholar
Digital Library
- Almeida, J. M., Krueger, J., Eager, D. L., and Vernon, M. K. 2001. Analysis of educational media server workloads. In Proceedings of the International Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV'01). 21--30. Google Scholar
Digital Library
- Carlsson, N., Eager, D. L., and Vernon, M. K. 2006. Multicast protocols for scalable on-demand download. Perform. Eval. 63, 8--9, 864--891. Google Scholar
Digital Library
- Carlsson, N. 2006. Scalable download protocols. Ph.D. thesis, University of Saskatchewan, Saskatoon, SK, Canada. Google Scholar
Digital Library
- Carter, R. L. and Crovella, M. E. 1997. Server selection using dynamic path characterization in wide-area networks. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom'97). 1014--1021. Google Scholar
Digital Library
- Chuang, J. and Sirbu, M. 2001. Pricing multicast communication: A cost based approach. Telecomm. Syst. 17, 3, 281--297.Google Scholar
Digital Library
- Costa, C. P., Cunha, Í. S., Vieira, A. B., Ramos, C. V., Rocha, M. M., Almeida, J. M., and Ribeiro-Neto, B. A. 2004. Analyzing client interactivity in streaming media. In Proceedings of the International World Wide Web Conference (WWW'04). 534--543. Google Scholar
Digital Library
- Dan, A., Shahabuddin, P., Sitaram, D., and Towsley, D. 1995. Channel allocation under batching and vcr control in video-on-demand systems. J. Parall. Distrib. Comput. (Special Issue on Multimedia Processing and Technology), 30, 2, 168--179. Google Scholar
Digital Library
- Dan, A., Sitaram, D., and Shahabuddin, P. 1994. Scheduling policies for an on-demand video server with batching. In Proceedings of the ACM International Conference on Multimedia (MM'94). 15--23. Google Scholar
Digital Library
- Dykeman, H. D., Ammar, M. H., and Wong, J. W. 1986. Scheduling algorithms for videotex systems under broadcast delivery. In Proceedings of the IEEE International Conference on Communications (ICC'86).Google Scholar
- Eager, D. L., Vernon, M. K., and Zahorjan, J. 2000. Bandwidth skimming: A technique for cost-effective video-on-demand. In Proceedings of the Annual Multimedia Computing and Networking Conference (MMCN'00). 206--215.Google Scholar
- Fahmy, S. and Kwon, M. 2007. Characterizing overlay multicast networks and their costs. IEEE/ACM Trans. Netw. 15, 2, 373--386. Google Scholar
Digital Library
- Fei, Z., Ammar, M. H., and Zegura, E. W. 2002. Multicast server selection: Problems, complexity and solutions. IEEE J. Select. Areas Comm. 20, 7, 1399--1413. Google Scholar
Digital Library
- Guo, M., Ammar, M. H., and Zegura, E. W. 2002. Selecting among replicated batching video-on-demand servers. In Proceedings of the International Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV'02).155--163. Google Scholar
Digital Library
- Jamin, S., Jin, C., Jin, Y., Raz, D., Shavitt, Y., and Zhang, L. 2000. On the placement of Internet instrumentation. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom'00). 295--304.Google Scholar
- Jamin, S., Jin, C., Kurc, A., Raz, D., and Shavitt, Y. 2001. Constrained mirror placement on the Internet. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom'01). 31--40.Google Scholar
- Johnsen, F. T., Hafsøe, T., Griwodz, C., Halvorsen, P. 2007. Workload characterization for news-on-demand streaming services. In Proceedings of the IEEE International Performance, Computing, and Communications Conference (IPCCC'07). 314--323.Google Scholar
Cross Ref
- Johnson, K. L., Carr, J. F., Day, M. S., and Kaashoek, F. 2006. The measured performance of content distribution networks. Comput. Comm. 24, 2, 202--206. Google Scholar
Digital Library
- Lee, G. 2006. Will all of us get our 15 minutes on a YouTube video? The Wall St. J. Online, 8/30/06.Google Scholar
- Qiu, L., Padmanabhan, V. N., and Voelker, G. M. 2001. On the placement of web server replicas. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom'01). 1587--1596.Google Scholar
- Phillips, G. Shenker, S., Tangmunarunkit, H. 1999. Scaling of multicast trees: Comments on the Chuang-Sirbu scaling law. In Proceedings of the ACM SIGCOMM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication. (SIGCOMM'99). 41--51. Google Scholar
Digital Library
- Ratnasamy, S., Handley, M., Karp, R., and Shenker, S. 2002. Topologically-aware overlay construction and server selection. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom'02). 1190--1199.Google Scholar
- Rost, S., Byers, J., and Bestavros, A. 2001. The cyclone server architecture: Streamlining delivery of popular content. In Proceedings of the International Workshop on Web Content Caching and Distribution (WCW '01). 147--163.Google Scholar
- Tan, H., Eager, D. L., and Vernon, M. K. 2002. Delimiting the range of effectiveness of scalable on-demand streaming. In Proceedings of IFIP W. G. 7.3 International Symposium on Computer Performance Modeling, Measurement and Evaluation (Performance'02). 387--410.Google Scholar
- USA Today. 2006. YouTube serves up 100 million videos a day online. USA Today, 8/16/06.Google Scholar
- Wong, J. W. 1988. Broadcast delivery. IEEE 76, 12, 1566--1577.Google Scholar
Cross Ref
- Zegura, E. W., Ammar, M. H., Fei, Z., and Bhattacharjee, S. 2000. Application-layer anycasting: A server selection architecture and use in a replicated Web service. IEEE/ACM Trans. Netw. 8, 4, 455--466. Google Scholar
Digital Library
- Zegura, E. W., Calvert, K., and Bhattacharjee, S. 1996. How to model an Internetwork. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies (InfoCom'96). 594--602. Google Scholar
Digital Library
Index Terms
Server selection in large-scale video-on-demand systems
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