Content Delivery Based on Popularity and Time Slot

Huang, Min-Chun; Chang, Chi-He; Tseng, Chao-Wei; Lee, Ru-Jen

doi:10.1109/pdcat.2017.00063

Cited by 1 publication

(1 citation statement)

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“…Service providers are exploiting several methods to keep up with the ever-increasing demand. One widely considered method to enhance the performance of video distribution networks is to pre-place popular objects in strategic locations within the network, based on the anticipated demand [2]- [6]. Given the enormous sizes of video objects and constrained capacities of storage, it is impossible to replicate all videos in all locations.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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. INDEX TERMS Popularity prediction, video-on-demand, pre-placement, request statistic, Zipf distribution, non-homogeneous Poisson process. BILL MORAN (Life Member, IEEE) received the B.Sc. degree (Hons.

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Section: Introductionmentioning

confidence: 99%