Scalable Facility Location for Massive Graphs on Pregel-like Systems.

We propose a new scalable algorithm for the facility-location problem. We study the graph setting, where the cost of serving a client from a facility is represented by the shortest-path distance on a graph. This setting is applicable to various problems arising in the Web and social media, and allows to leverage the inherent sparsity of such graphs. To obtain truly scalable performance, we design a parallel algorithm that operates on clusters of shared-nothing machines. In particular, we target modern Pregel-like architectures, and we implement our algorithm on Apache Giraph. Our work builds upon previous results: a facility location algorithm for the PRAM model, a recent distance-sketching method for massive graphs, and a parallel algorithm to finding maximal independent sets. The main challenge is to adapt those building blocks to the distributed graph setting, while maintaining the approximation guarantee and limiting the amount of distributed communication. Extensive experimental results show that our algorithm scales gracefully to graphs with billions of edges, while, in terms of quality, being competitive with state-of-the-art sequential algorithms.