Online microblogging 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 microblogging site. The impact is fundamental — not
only are viewers in a video service directly coming from the microblog sharing
and recommendation, but also are the users in the microblogging 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
microblogs. Based on extensive traces from Youku and Tencent Weibo, a popular
video sharing site and a favored microblogging system, we explore how video
propagation patterns in the microblogging 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 PlanetLab-based experiments verify the
effectiveness of our design
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