Using random sampling, we extend a class of well-known information retrieval ranking algorithms such that they can be applied in this decentralized setting. We analyze the overhead of our approach, and quantify exactly how our system scales with increasing number of documents, system size, document to node mapping (uniform versus non-uniform), and types of queries (rare versus popular terms). Our analysis and simulations show that a) these extensions are efficient, and can scale with little overhead to large systems, and b) the accuracy of the results obtained using distributed ranking is comparable to that of a centralized implementation.

@inProceedings{hipc07,
	title = "Distributed Ranked Search",
	author = "Vijay Gopalakrishnan and Ruggero Morselli and Bobby Bhattacharjee and Pete Keleher and Aravind Srinivasan",
	booktitle = {14th Annual IEEE International Conference on High Performance Computing},
	year = {2007},
}