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.
Highlights
In recent years, the popularity of Video-on-Demand (VoD) services has increased tremendously
We provide an in-depth performance comparison with four other dynamic popularity estimation methods, namely, Least Recency Used (LRU), Least Frequency Used (LFU), Least Recently/Frequently Used (LRFU), and Exponential Weighted Moving Average (EWMA)
We show that Minimal Inverted Pyramid Distance (MIPD) outperforms the other four popularity estimation methods in terms of Hit Ratio (HR) and the accuracy for estimating the rank of videos of given actual popularity in our designed VoD model
Summary
The popularity of Video-on-Demand (VoD) services has increased tremendously. The VoD system can have prior information about the popularity of the videos before they are introduced to the system Another crucial research area is an in-depth analysis of the dynamics of the video request arrival process [9]–[11], [13]. In this area, many methods have been proposed to predict popularity based on the request statistics.
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