Decentralized Server Selection Through Joint Proximity and Load Optimization
Report ID: TR-868-09Author: Freedman, Michael J. / Rexford, Jennifer / Wendell, Patrick / Jiang, Joe Wenjie
Date: 2009-09-00
Pages: 6
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Abstract:
With the advent of "cloud computing" and the growth of popular Web services, many networked services are replicated at multiple geographic locations. Such distributed services face the challenge of server selection -- that is, directing an incoming client request to the appropriate server or data center, in the hope of reducing network latency or carefully tuning server loads. To meet these potentially conflicting goals, existing approaches use heuristics or rely on central coordination to perform server selection. In this work, we apply optimization theory to derive a simple, provably optimal, fully distributed solution to the server-selection problem. Our approach defines a global objective for a mapping service, and shows that decentralized mapping nodes performing small amounts of local computation and sharing limited information, can achieve the global objective. We also perform experiments, based on a 24-hour trace of a real operational CDN, that show that the distributed solution converges very quickly in practice.