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Title: Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis
Authors: Skaperas, Sotiris
Mamatas, Lefteris
Chorti, Arsenia
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Issue Date: 2019
Source: IEEE Access
Volume: 7
First Page: 142246
Last Page: 142260
Abstract: Video content is responsible for more than 70% of the global IP traffic. Consequently, it is important for content delivery infrastructures to rapidly detect and respond to changes in content popularity dynamics. In this paper, we propose the employment of on-line change point (CP) analysis to implement real-time, autonomous and low-complexity video content popularity detection. Our proposal, denoted as real-time change point detector (RCPD) , estimates the existence, the number and the direction of changes on the average number of video visits by combining: (i) off-line and on-line CP detection algorithms; (ii) an improved time-series segmentation heuristic for the reliable detection of multiple CPs; and (iii) two algorithms for the identification of the direction of changes. The proposed detector is validated against synthetic data, as well as a large database of real YouTube video visits. It is demonstrated that the RCPD can accurately identify changes in the average content popularity and the direction of change. In particular, the success rate of the RCPD over synthetic data is shown to exceed 94% for medium and large changes in content popularity. Additionally, the dynamic time warping distance, between the actual and the estimated changes, has been found to range between 20 samples on average, over synthetic data, to 52 samples, in real data. The rapid responsiveness of the RCPD is instrumental in the deployment of real-time, lightweight load balancing solutions, as shown in a real example.
ISSN: 2169-3536
Other Identifiers: 10.1109/ACCESS.2019.2940816
Appears in Collections:Department of Applied Informatics

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