att_abstract={IPTV service providers offering Video-on-Demand (VoD) typically have many servers at each metropolitan office to store all the videos in the library. With the rapid increase in the VoD library size, 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 VoD 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 the skew in content popularity. To overcome the challenges of scale, we employ a Lagrangian relaxation-based decomposition technique that can find 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 cache replacement policy. We also investigate the trade-off between disk space and network bandwidth.},
	att_authors={vg7777, sl1858, kr2812, da1287, aa1327},
	att_copyright_notice={(c) ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM CoNext 2010 {{, 2010-12-01}}},
	att_tags={VoD,  content placement,  caching,  Mixed integer Progamming},
	author={ David Applegate AND Aaron Archer AND Vijay Gopalakrishnan AND Seungjoon Lee AND Kadangode Ramakrishnan},
	booktitle={Proceedings of ACM CoNext 2010},
	institution={{Submitted to ACM CoNext 2010}},
	title={{Optimal Content Placement for a Large-Scale VoD System}},