ATDPS: An Adaptive Time-Dependent Push Strategy in Hybrid CDN-P2P VoD System

Online video service has become an emerging application of Internet, playing an important and indispensable role in our daily life. Aiming at relieving the heavy burden on CDN servers, many video service providers deploy a hybrid CDN-P2P system. To minimize the cost of the "last mile delivery" of CDN servers, the system should be able to dispatch video files that receive a large amount of view requests during peak hours to P2P network in advance. However, to accurately predict whether a video will gain popularity is difficult due to the uncertainty of people's viewing tendency. In this paper, we reveal the correlation between the popularity of videos in the daytime and the popularity in peak hours at night based on statistics collected in a real-world large commercial VoD system. Based on our findings, we propose Adaptive Time-Dependent Push Strategy (ATDPS), a scalable push strategy that implements adaptive time-dependent techniques in the hybrid CDN-P2P system. A deep neural network is leveraged to predict the demand of hot videos during peak hours at night. Besides, the system adaptively adjusts the time-dependent strategy of pushing the content of hot videos to the P2P network according to the seed scarcity of the videos in different time periods. Simulations over historical data and pilot deployment on smart routers show that our ATDPS can significantly decrease the addressed bandwidth consumption at peak hours.