Efficient identification of uncongested internet links for topology downscaling
ACM SIGCOMM Computer Communication Review
Predicting the performance of internet-like networks using scaled-down replicas
ACM SIGMETRICS Performance Evaluation Review
On scaling the IEEE 802.11 to facilitate scalable wireless networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Making the case for random access scheduling in wireless multi-hop networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
On the efficiency of CSMA-CA scheduling in wireless multihop networks
IEEE/ACM Transactions on Networking (TON)
Flow-based partitioning of network testbed experiments
Computer Networks: The International Journal of Computer and Telecommunications Networking
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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. This is complementary to the work of Psounis, 2003, where the authors presented a way to scale down the Internet in time, by creating a slower replica of the original system. The key insight that we leverage in this work 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. Using extensive simulations with transmission control protocol (TCP) traffic and theoretical analysis, we show that it is possible to achieve this kind of performance scaling even on topologies the size of the CENIC backbone (that provides Internet access to higher education institutions in California). We also show that simulating a scaled topology can be up to two orders of magnitude faster than simulating the original topology