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 journalpeer-review

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Author(s)

  • Chamil JAYASUNDARA
  • Ampalavanapillai NIRMALATHAS
  • Elaine WONG
  • Chathurika RANAWEERA
  • Bill MORAN

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number8959235
Pages (from-to)19934-19947
Number of pages14
Journal / PublicationIEEE Access
Volume8
Online published14 Jan 2020
Publication statusPublished - 2020

Link(s)

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.

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 journalpeer-review

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