Hiroyuki Kimiyama
ABSTRACT
We propose a method of predicting the number of requests for video titles in an on-demand video delivery system considering their time dependency. To handle many heterogeneous video requests in a video streaming delivery network system using video servers, it is effective to introduce video caching systems in the network system. For the cache system design in video delivery network systems, an effective cache algorithm for predicting how many requests will occur in the future is needed. Since almost all such proposed algorithms are for WWW systems, they do not consider the time dependency of the number of requests, so they are not applicable to a video system with time-dependent requests because a customer in the system watches one video title just one time. In our method, the number of requests is represented by a function of time by considering how a customer obtains information about a video and requests it and by considering that a customer requests each video only once. We have developed a method of calculating the coefficients of the function from the series of the number of requests and for estimating the number of requests in the future. We evaluated the convergence and calculation time. The results show that the calculations converge and the calculation time is 31 ms. They also show that the calculation cost is acceptable for a general video delivery system.
- J. Beran, R. Sherman, M. S. Taqqu, and W. Willinger. Long-range dependence in variable bit rate video traffic. IEEE Trans. Commun., 43(234):1566--1579, 1995.Google Scholar
Cross Ref
- P. J. Brockwell and R. A. Davis. Introduction to time series and forecasting. Springer, Berlin, second edition, 2002.Google Scholar
- S. H. G. Chan and F. A. Tobagi. Caching schemes for distributed video services. In Proceeding of 1999 IEEE International Conference on Communications, pages 994--999, Vancouver, Canada, June 5--10 1999.Google ScholarCross Ref
- K.-M. Ho, W.-F. Poon, and K.-T. Lo. Performance study of large-scale video streaming service in highly heterogeneous environment. IEEE Trans. Broadcasting, 53(4):763--773, 2006.Google ScholarCross Ref
- Y. Ji. Multi-scale internet traffic analysis using piecewise self-similar process. IEICE Trans. Commun., E89-B(8):2125--2133, Aug. 2006.Google Scholar
- O. Kwon, H. Bahn, and K. Koh. Popularity and prefix aware interval caching for multimedia streaming. In Proceeding of 2008 8th IEEE International Conference on Computer and Information Technology, pages 555--560, Sydny, Australia, July 8--11 2008.Google ScholarCross Ref
- D. M. P. Ng, W. M. Wong, K. T. Ko, and K. S. Tang. Trend analysis and prediction with historical request data for multimedia-on-demand system. IEICE Trans. Commun., E86-B(6):2001--2011, June 2003.Google Scholar
- S. Podlipnig and L. Boszormenyi. A survey of web cache replacement strategies. ACM Comput. Surv., 35(4):374--398, Dec. 2002. Google ScholarDigital Library
- Z. Su, J. Katto, T. Nishikawa, M. Murakami, and Y. Yasuda. Stream caching using hierarchically distributed proxies with adaptive segments assignment. IEICE Trans. Commun., E86-B(6):1859--1869, June 2003.Google Scholar
- T. Takakura. Dynamic contents relocation based on access load prediction. IPSJ Journal, 40(3):1285--1293, March 1999.Google Scholar
- W. Tavanapong, M. Tran, J. Zhou, and S. Krishnamohan. Video caching network for on-demand video streaming. In Proceeding of IEEE Global Telecommunications Conference 2002, pages 1726--1735, Taipei, Taiwan, R.O.C, Nov. 17--21 2002.Google ScholarCross Ref
- B. Wang, S. Sen, and D. Towsley. Optimal proxy cache allocation for efficient streaming media distribution. In Proceeding of IEEE INFOCOM 2002, pages 1726--1735, New York City, USA, June 23--27 2002.Google ScholarCross Ref
- J. Yu, C. T. Chou, Z. Yang, and X. Du. A dynamic caching algorithm based on internet popularity distribution of streaming media. Multimedia Syst., 12(2):135--149, 2006.Google ScholarDigital Library
- http://flets.com/english/opt/bb/hikaritv.html.Google Scholar
Index Terms
- Method of predicting number of on-demand video requests using time series data for video cache system
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