  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
between20sampleson average, over synthetic data, to52samples, 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.
