We study content placement in a content delivery network (CDN)
where a large number of front-end servers, each with fixed storage
and service capacity, serve a correspondingly large volume of
content requests. We consider the algorithmic task of content placement:
determining which types of content should be on which server at any given
time, in the setting where the demand statistics (i.e. the relative
popularity of each type of content) are not known a-priori, but have to be
inferred from the very demands we are trying to satisfy. This is the
high-dimensional regime because this scaling prevents consistent estimation
of demand statistics; it models many modern settings where large numbers of
users, servers and videos/webpages interact in this way.
We characterize the performance of any scheme that separates learning and placement (i.e. which use a portion of the demands to gain some estimate of the demand statistics, and then uses the same for the remaining demands), showing it is order-wise strictly suboptimal. We then study a simple adaptive scheme - which myopically attempts to store the most recently requested content on idle servers - and show it outperforms schemes that separate learning and placement. Our results also generalize to the setting where the content catalog as well as the demand statistics change with time. Overall, our results demonstrate that separating the estimation of demand, and the subsequent use of the same, is strictly suboptimal.
Sharayu Moharir is a Visiting Fellow in the School of Technology and Computer Science at the Tata Institute of Fundamental Research, Mumbai, India. She received her Ph.D. in Electrical and Computer Engineering at the University of Texas at Austin in 2014, her M.Tech. in Communication and Signal Processing and her B.Tech. in Electrical Engineering from the Indian Institute of Technology, Bombay in 2009. Her research interests include modeling and design of scalable algorithms for large-scale systems including content delivery networks, communication networks and crowd-sourcing.