Estimating Video Popularity From Past Request Arrival Times in a VoD System
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Article number | 8959235 |
Pages (from-to) | 19934-19947 |
Number of pages | 14 |
Journal / Publication | IEEE Access |
Volume | 8 |
Online published | 14 Jan 2020 |
Publication status | Published - 2020 |
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85081088911&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(e8b16871-97e9-4f80-a919-0ccd9695652f).html |
Abstract
Efficient provision of Video-on-Demand (VoD) services requires that popular videos are stored in a cache close to users. Video popularity (defined by requested count) prediction is, therefore, important for optimal choice of videos to be cached. The popularity of a video depends on many factors and, as a result, changes dynamically with time. Accurate video popularity estimation that can promptly respond to the variations in video popularity then becomes crucial. In this paper, we analyze a method, called Minimal Inverted Pyramid Distance (MIPD), to estimate a video popularity measure called the Inverted Pyramid Distance (IPD). MIPD requires choice of a parameter, k, representing the number of past requests from each video used to calculate its IPD. We derive, analytically, expressions to determine an optimal value for k, given the requirement on ranking a certain number of videos with specified confidence. In order to assess the prediction efficiency of MIPD, we have compared it by simulations against four other prediction methods: Least Recency Used (LRU), Least Frequency Used (LFU), Least Recently/Frequently Used (LRFU), and Exponential Weighted Moving Average (EWMA). Lacking real data, we have, based on an extensive literature review of real-life VoD system, designed a model of VoD system to provide a realistic simulation of videos with different patterns of popularity variation, using the Zipf (heavy-tailed) distribution of popularity
and a non-homogeneous Poisson process for requests. From a large number of simulations, we conclude that the performance of MIPD is, in general, superior to all of the other four methods.
and a non-homogeneous Poisson process for requests. From a large number of simulations, we conclude that the performance of MIPD is, in general, superior to all of the other four methods.
Research Area(s)
- Popularity prediction, video-on-demand, pre-placement, request statistic, Zipf distribution, non-homogeneous Poisson process
Citation Format(s)
Estimating Video Popularity From Past Request Arrival Times in a VoD System. / WANG, Tianjiao; JAYASUNDARA, Chamil; ZUKERMAN, Moshe; NIRMALATHAS, Ampalavanapillai; WONG, Elaine; RANAWEERA, Chathurika; XING, Chang; MORAN, Bill.
In: IEEE Access, Vol. 8, 8959235, 2020, p. 19934-19947.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
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