Enhancing Internet-Scale Video Service Deployment Using Microblog-Based Prediction

Online micro blogging has been very popular in today’s Internet, where users follow other people they are interested in and exchange information between themselves. Among these exchanges, video links are a representative type on a micro blogging site. The impact is fundamental-not only are viewers in a video service directly coming from the micro blog sharing and recommendation, but also are the users in the micro blogging site representing a promising sample to all the viewers. It is intriguing to study a proactive service deployment for such videos, using the propagation patterns of micro blogs.

Based on extensive traces from Youku and Tencent Weibo, a popular video sharing site and a favored micro blogging system, we explore how video propagation patterns in the micro blogging system are correlated with video popularity on the video sharing site. Using influential factors summarized from the measurement studies, we further design a neural network-based learning framework to predict the number of potential viewers and their geographic distribution. We then design proactive video deployment algorithms based on the prediction framework, which not only determines the upload capacities of servers in different regions, but also strategically replicates videos to these regions to serve users. Our Planet Lab-based experiments verify the effectiveness of our design.