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.
Highlights
Video content is projected to account for 82% of the global Internet traffic by 2020, significantly increased from 72% in 2016 [1]
We have explicitly demonstrated the superiority of the modified binary segmentation (BS) over the standard BS algorithm and confirmed the validity of the proposed trend indicators
We have shown that the real-time change point detector (RCPD) algorithm achieves extremely high true alarm rates for larger values of μ, while increasing the length of the monitoring window l can significantly impact the performance for small values of μ
Summary
Video content is projected to account for 82% of the global Internet traffic by 2020, significantly increased from 72% in 2016 [1]. In the RCPD, we employ the change point (CP) detection theory and algorithms; their suitability is confirmed against a large number of synthetic as well as real YouTube video datasets. In this contribution, the early detection of changes in the average content popularity is addressed with a novel CP detection methodology, consisting of a training phase, using historical data, and, an on-line phase. We evaluate the proposed detector and its individual algorithmic components (i.e., the off-line / on-line test statistics, the time-series segmentation algorithm and the trend indicator), over synthetic and real YouTube content views data.
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