Popularity prediction in content delivery networks

Content delivery networks (CDNs) face a large and continuously increasing number of users solicitations for video contents. In this paper, we focus on the prediction of popularity evolution of video contents. Based on the observation of past solicitations of individual video contents, individual future solicitations are predicted. We compare different prediction strategies: SES, DES and Basic. The best tuning of each strategy is determined, depending on the considered phase of the solicitation curve. Since DES and Basic experts outperform the SES expert, our method combines DES and Basic experts to predict the number of solicitations within a phase and automatically detect the phase changes, respectively. This self-learning and prediction method can be applied to optimize resources allocation in service oriented architectures and self-adaptive networks, more precisely for the CDN cache nodes management.