전기학회논문지 (The Transactions of The Korean Institute of Electrical Engineers)

  • 제66권8호
  • /
  • Pages.1257-1264
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  • 2017
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  • 1975-8359(pISSN)
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  • 2287-4364(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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다운로드 기반의 주문형 비디오 서비스에서 다중 지수를 고려한 동영상 프리페칭 기법

Multi-index Prefetching Mechanism for Download-based Video on Demand Services

  • Moon, YangChan (Dept. of Internet & Multimedia Engineering, Konkuk University) ;
  • Lim, Mingyu (Dept. of Smart ICT Convergence, Konkuk University)
  • 투고 : 2017.03.08
  • 심사 : 2017.07.09
  • 발행 : 2017.08.01
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초록

In video content watching service, when a user requests video content, the content server has to transmit the entire video to the client for watching. This transmission delay increases as the size of video content increases. In order to solve the transmission delay problem, a prefetching technique can be used in which a video content to be watched by a user is predicted and transmitted to a client before the user requests it. In this paper, we propose a prefetching system considering multiple indices for video content. In the proposed method, video content to be prefetched is selected by comprehensively analyzing the order relation index indicating the order of viewing the videos of the users, the similarity index between the video contents, and the popularity index reflecting the viewing frequency of the video content. Experimental results show that the maximum accuracy is achieved when prefetching uses only the order relation index for movie contents.

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참고문헌

  1. YangChan Moon, "A prefetching system using order relation among video content", Masters thesis, Konkuk University, 2015.
  2. Pazzani, Michael J, and Daniel Billsus, "Content-based recommendation systems", The adaptive web. Springer Berlin Heidelberg, pp.325-341, 2007.
  3. Davidson, James, et al, "The YouTube video recommendation system", In Proceedings of the fourth ACM conference on Recommender systems, ACM, pp. 293-296, 2010.
  4. Schafer, J. Ben, et al, "Collaborative filtering recommender systems", The adaptive web. Springer Berlin Heidelberg, pp. 291-324, 2007.
  5. Adomavicius, Gediminas, and Alexander Tuzhilin. "Context-aware recommender systems", Recommender systems handbook, Springer US, pp.217-253, 2011.
  6. Domnech, Josep, et al, "Web prefetching performance metrics: A survey", Performance Evaluation, pp. 988- 1004, 2006.
  7. FAN, Li, et al, "Web prefetching between lowbandwidth clients and proxies potential and performance", ACM SIGMETRICS Performance Evaluation Review, pp.178-187, 1999.
  8. F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872.