document: Modeling redirection in geographically diverse server sets Internet server selection mechanisms attempt to optimize , subject to a variety of constraints , the distribution of client requests to a geographically and topologically diverse pool of servers . Research on server selection has thus far focused primarily on techniques for choosing a server from a group administered by single entity , like a content distribution network provider . In a federated , multi-provider computing system , however , selection must occur over distributed server sets deployed by the participating providers , without the benefit of the full information available in the single-provider case . Intelligent server set selection algorithms will require a model of the expected performance clients would receive from a candidate server set . In this paper , we study whether the complex policies and dynamics of intelligent server selection can be effectively modeled in order to predict client performance for server sets . We introduce a novel server set distance metric , and use it in a measurement study of several million server selection transactions to develop simple models of existing server selection schemes . We then evaluate these models in terms of their ability to accurately predict performance for a second , larger set of distributed clients . We show that our models are able to predict performance within 20ms for over 90 % of the observed samples . Our analysis demonstrates that although existing deployments use a variety of complex and dynamic server selection criteria , most of which are proprietary , these schemes can be modeled with surprising accuracy .
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