Gossip-based topology inference for efficient overlay mapping on data centers

We present a distributed algorithm for identifying the location of data centers and their relative sizes. This topology information can be used in P2P systems to improve the routing performance, replica placement, or job scheduling. The algorithm uses gossiping with local agglomerative clustering. It is robust to failures and it correctly identifies outliers that are caused, e.g., by temporarily overloaded nodes or network failures. We present empirical results on the Grid 5000 testbed.