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In a large-scale information system such as a digital library or the web, a set of distributed caches can improve their effectiveness by coordinating their data placement decisions. Using simulation, we examine three practical cooperative placement algorithms including one that is provably close to optimal, and we compare these algorithms to the optimal placement algorithm and several cooperative and non-cooperative replacement algorithms. We draw five conclusions from these experiments: (1) cooperative placement can significantly improve performance compared to local replacement algorithms particularly when the size of individual caches is limited compared to the universe of objects; (2) although the 'Amortized Placement' algorithm is only guaranteed to be within 14 times the optimal, in practice it seems to provide an excellent approximation of the optimal; (3) in a cooperative caching scenario, the recent 'GreedyDual' local replacement algorithm performs much better than the other local replacement algorithms; (4) our 'Hierarchical GreedyDual' replacement algorithm yields further improvements over the GreedyDual algorithm especially when there are idle caches in the system; and (5) a key challenge to coordinated placement algorithms is generating good predictions of access patterns based on past accesses.