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We consider the non-metric data placement problem and develop distributedalgorithms for computing or approximating its optimal integral solution. Wefirst show that the non-metric data placement problem is inapproximable up to alogarithmic factor. We then provide a game-theoretic decomposition of theobjective function and show that natural Glauber dynamics in which playersupdate their resources with probability proportional to the utility theyreceive from caching those resources will converge to an optimal globalsolution for a sufficiently large noise parameter. In particular, we establishthe polynomial mixing time of the Glauber dynamics for a certain range of noiseparameters. Finally, we provide another auction-based distributed algorithm,which allows us to approximate the optimal global solution with a performanceguarantee that depends on the ratio of the revenue vs. social welfare obtainedfrom the underlying auction. Our results provide the first distributedcomputation algorithms for the non-metric data placement problem.

Distributed Computation for the Non-metric Data Placement Problem using Glauber Dynamics and Auctions