att_abstract={IPTV service providers offering Video-on-Demand currently use servers at each metropolitan office to store all the videos in their library. With the rapid increase in library sizes, it will soon become infeasible to replicate the entire library at each office. We present an approach for intelligent content placement that scales to large library sizes (e.g., 100Ks of videos). We formulate the problem as a mixed integer program (MIP) that takes into account constraints such as disk space, link bandwidth, and content popularity. To overcome the challenges of scale, we employ a Lagrangian relaxation-based decomposition technique combined with integer rounding. Our technique finds a near-optimal solution (e.g., within 1-2%) with orders of magnitude speedup relative to solving even the LP relaxation via standard software. We also present simple strategies to address practical issues such as popularity estimation, content updates, short-term popularity fluctuation, and frequency of placement updates.
Using traces from an operational system, we show that our approach significantly outperforms simpler placement strategies. For instance, our MIP-based solution can serve all requests using only half the link bandwidth used by LRU or LFU cache replacement policies. We also investigate the trade-off between
disk space and network bandwidth. },
	att_authors={vg7777, da1287},
	att_copyright_notice={This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2015. {{, Volume 24}}{{, Issue 4}}{{, 2016-08-01}}
	author={David Applegate and Aaron Archer and Vijay Gopalakrishnan and Seungjoon Lee and K.K. Ramakrishnan},
	institution={{IEEE/ACM Transactions on Networking}},
	journal={IEEE/ACM Transactions on Networking},
	title={{Optimal Content Placement for a Large-Scale VoD System}},