The Internet is a large, heterogeneous system operating at very high speeds and consisting of a large number of users. Researchers use a suite of tools and techniques in order to understand the performance of complex networks like the Internet: measurements, simulations, and deployments on small to medium-scale testbeds. This work considers a novel addition to this suite: a class of methods to scale down the topology of the Internet that enables researchers to create and observe a smaller replica, and extrapolate its performance to the expected performance of the larger Internet. The key insight that we leverage is that only the congested links along the path of each flow introduce sizable queueing delays and dependencies among flows. Hence, one might hope that the network properties can be captured by a topology that consists of the congested links only. We have verified this in [11, 12] using extensive simulations with TCP traffic and theoretical analysis. Further, we have also shown that simulating a scaled topology can be up to two orders of magnitude faster than simulating the original topology. However, a main assumption of our approach was that un-congested links are known in advance. We are currently working on establishing rules that can be used to efficiently identify uncongested links in large and complex networks like the Internet, when these are not known, and which can be ignored when building scaled-down network replicas.
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