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Jarrett Huddleston

dblp:425/9486 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2026
0009-0000-7116-7295ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-author · 1 since 2021Security and privacy · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Network measurement and analytics · 77% Routing and switching · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network measurement and analytics
topology discovery
0.912025
Replication: Characterizing MPLS Tunnels over Internet Paths · IMC 2025
Routing and switching
MPLS
0.312025
Replication: Characterizing MPLS Tunnels over Internet Paths · IMC 2025

Methods — techniques the papers use, named apart from their topics

traceroute analysis · 0.9
YearPublicationVenuePosition
2026 Different Policies for Different NodeBs: Comparing Downlink Schedulers in Cellular Base Stations
Zesen Zhang, Jon Larrea, Jarrett Huddleston, Haoran Wan, Ricky K. P. Mok, Bradley Huffaker, K. C. Claffy, Kyle Jamieson, Alexander Marder, Aaron Schulman
PAM3
2025 Replication: Characterizing MPLS Tunnels over Internet Paths
abstract
Traceroute is a critical tool in the Internet measurement toolbox, but its output can be misleading. One problem for traceroute analysis is that certain types of Multiprotocol Label Switching (MPLS) tunnels hide routers from traceroute output. Worse still, there is no simple way to detect or reveal missing routers. Any analysis that expects comprehensive topology discovery—including identifying performance bottlenecks, analyzing traffic engineering approaches, and evaluating traffic sovereignty—needs to account for MPLS. In this paper, we replicate previous work by Vanaubel et al. [18, 21] to characterize and provide a snapshot of the current deployment of MPLS tunnels. We also release a sustainable and easily deployed tool for MPLS detection, called PyTNT. Using PyTNT, we find that the problematic types of MPLS tunnels remain prevalent, though we inferred a general decrease in MPLS usage across the Internet. We also inferred that public clouds accounted for 3 of the top 10 networks with the most routers observed to be in MPLS tunnels. Finally, we observed more MPLS routers in Europe than any in other continent, and more MPLS routers in the U.S. than any other country.
Jarrett Huddleston, Matthew J. Luckie, Alexander Marder
IMC1