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Center for Applied Internet Data Analysis > publications : papers : 2017 : challenges_inferring_internet_congestion
Challenges in Inferring Internet Congestion Using Throughput Measurements
S. Sundaresan, D. Lee, X. Deng, Y. Feng, and A. Dhamdhere, "Challenges in Inferring Internet Congestion Using Throughput Measurements", in ACM Internet Measurement Conference (IMC), Nov 2017.
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Challenges in Inferring Internet Congestion Using Throughput Measurements

Srikanth Sundaresan3
Danny Lee2
Xioahong Deng4
Yun Feng4
Amogh Dhamdhere1

CAIDA, San Diego Supercomputer Center, University of California San Diego


Georgia Institute of Technology (Georgia Tech)


Princeton University


University of New South Wales

We revisit the use of crowdsourced throughput measurements to infer and localize congestion on end-to-end paths, with particular focus on points of interconnections between ISPs. We analyze three challenges with this approach. First, accurately identifying which link on the path is congested requires fine-grained network tomography techniques not supported by existing throughput measurement platforms. Coarse-grained network tomography can perform this link identification under certain topological conditions, but we show that these conditions do not always hold on the global Internet. Second, existing measurement platforms provide limited visibility of paths to popular web content sources, and only capture a small fraction of interconnections between ISPs. Third, crowdsourcing measurements inherently risks sample bias: using measurements from volunteers across the Internet leads to uneven distribution of samples across time of day, access link speeds, and home network conditions. Finally, it is not clear how large a drop in throughput to interpret as evidence of congestion. We investigate these challenges in detail, and offer guidelines for deployment of measurement infrastructure, strategies, and technologies that can address empirical gaps in our understanding of congestion on the Internet.

Keywords: congestion, measurement methodology, passive data analysis, topology
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