  The goal of Content Delivery Networks (CDNs) is to serve content to end-users
with high performance. In order to do that, a CDN measures the latency on the
paths from its servers to users and then selects a best available server for
each user. For large CDNs, monitoring paths from thousands of servers to
millions of users is a challenging task due to its size. In this paper, we
address this problem and propose a framework to scale the task of path
monitoring. 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 given
cluster will be sufficient to assign that server to the rest of the clients in
the cluster.
  To achieve this goal, first we introduce two distance metrics to compute how
similar the server choices of any given two clients. Second, we use a
clustering method that is based on interdomain routing information. We evaluate
the goodness of our clusters by using the metrics we introduce. We show that
there is a strong correlation between the similarity in how two destination
clients are routed to in the Internet and the similarity in their server
selections. Finally, we show how to choose representative clients from each
cluster so that it is sufficient to learn the latencies from the CDN servers to
the representative and find a best available server accordingly for the rest of
the clients in the same cluster.
