Estimating Video Popularity From Past Request Arrival Times in a VoD System

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,...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.19934-19947
Main Authors: Wang, Tianjiao, Jayasundara, Chamil, Zukerman, Moshe, Nirmalathas, Ampalavanapillai, Wong, Elaine, Ranaweera, Chathurika, Xing, Chang, Moran, Bill
Format: Article
Language:English
Subjects:
Analytical models
Australia
Estimation
Frequency estimation
IP networks
Literature reviews
non-homogeneous Poisson process
Object recognition
Popularity prediction
pre-placement
request statistic
Servers
Simulation
Video on demand
Zipf distribution
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2966495