Modeling redirection in geographically diverse server sets

Abstract

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.