Exploiting Locality of Interest in Online Social Networks
Wittie, Mike P.
Deek, Lara B.
Almeroth, Kevin C.
Zhao, Ben Y.
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Online Social Networks (OSN) are fun, popular, and socially significant. An integral part of their success is the immense size of their global user base. To provide a consistent service to all users, Facebook, the world’s largest OSN, is heavily dependent on centralized U.S. data centers, which renders service outside of the U.S. sluggish and wasteful of Internet bandwidth. In this paper, we investigate the detailed causes of these two problems and identify mitigation opportunities. Because details of Facebook’s service remain proprietary, we treat the OSN as a black box and reverse engineer its operation from publicly available traces. We find that contrary to current wisdom, OSN state is amenable to partitioning and that its fine grained distribution and processing can significantly improve performance without loss in service consistency. Through simulations of reconstructed Facebook traffic over measured Internet paths, we show that user requests can be processed 79% faster and use 91% less bandwidth. We conclude that the partitioning of OSN state is an attractive scaling strategy for Facebook and other OSN services.
Mike P. Wittie, Veljko Pejovic, Lara Deek, Kevin C. Almeroth, Ben Y. Zhao "Exploiting Locality of Interest in Online Social Networks," in ACM CoNEXT, 2010