PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Optimal Web-scale Tiering as a Flow Problem
G Leung, Novi Quadrianto, AJ Smola and K Tsioutsiouliklis
In: Advances in Neural Information Processing Systems (NIPS)(2011).


We present a fast online solver for large scale parametric max-flow problems as they occur in portfolio optimization, inventory management, computer vision, and logistics. Our algorithm solves an integer linear program in an online fashion. It exploits total unimodularity of the constraint matrix and a Lagrangian relaxation to solve the problem as a convex online game. The algorithm generates approximate solutions of max-flow problems by performing stochastic gradient descent on a set of flows. We apply the algorithm to optimize tier arrangement of over 84 million web pages on a layered set of caches to serve an incoming query stream optimal.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:7462
Deposited By:Wray Buntine
Deposited On:17 March 2011