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The goal of Content Delivery Networks (CDNs) is to serve content to end-userswith high performance. In order to do that, a CDN measures the latency on thepaths from its servers to users and then selects a best available server foreach user. For large CDNs, monitoring paths from thousands of servers tomillions of users is a challenging task due to its size. In this paper, weaddress this problem and propose a framework to scale the task of pathmonitoring. Simply stated, the goal of our framework is clustering IP addresses(clients) such that in each cluster the choice of best available server is same(or similar). Then, finding a best available server for one client in a givencluster will be sufficient to assign that server to the rest of the clients inthe cluster.  To achieve this goal, first we introduce two distance metrics to compute howsimilar the server choices of any given two clients. Second, we use aclustering method that is based on interdomain routing information. We evaluatethe goodness of our clusters by using the metrics we introduce. We show thatthere is a strong correlation between the similarity in how two destinationclients are routed to in the Internet and the similarity in their serverselections. Finally, we show how to choose representative clients from eachcluster so that it is sufficient to learn the latencies from the CDN servers tothe representative and find a best available server accordingly for the rest ofthe clients in the same cluster.

Routing-Aware Partitioning of the Internet Address Space for Server Ranking in CDNs