On the use and performance of content distribution networks
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
DNS and BIND
A Precise and Efficient Evaluation of the Proximity Between Web Clients and Their Local DNS Servers
ATEC '02 Proceedings of the General Track of the annual conference on USENIX Annual Technical Conference
Evaluation of a Novel Two-Step Server Selection Metric
ICNP '01 Proceedings of the Ninth International Conference on Network Protocols
Intermediary infrastructures for the world wide web
Computer Networks: The International Journal of Computer and Telecommunications Networking
Client-side selection of replicated web services: An empirical assessment
Journal of Systems and Software
Load and Proximity Aware Request-Redirection for Dynamic Load Distribution in Peering CDNs
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
Journal of Network and Computer Applications
Architecture and performance models for QoS-driven effective peering of content delivery networks
Multiagent and Grid Systems - Content management and delivery through P2P-based content networks
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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.