Towards Understanding City-Level Routing using BGP Location Communities

BGP communities are widely used by operators to encode routing metadata for traffic engineering, policy enforcement, and operational debugging. However, ≈90% of observed communities lack public documentation, limiting their utility for research and operational analysis. Among these, city-level communities offer valuable geographic insight into routing behavior, yet remain largely untapped. In this paper, we develop a scalable method to infer the geographic meaning of undocumented city communities using BGP data. We validate our approach against a ground truth dataset covering 1,482 city communities and through operator feedback. Applied to data from May 2025, our algorithm infers the locations of 80% of city communities with a precision of 70 km or better. We publish all code and datasets to support reproducibility and further research.

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Publication Details

Journal
Proceedings of the ACM on Networking
Published
2025-11-24
DOI
https://doi.org/10.1145/3768998
Primary Topic
Network Traffic and Congestion Control
Type
article
Field-Weighted Citation Impact
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article

Towards Understanding City-Level Routing using BGP Location Communities

Thomas Krenc, Matthew Luckie, Benoît Donnet, kc claffy et al.
Proceedings of the ACM on Networking
Network Traffic and Congestion Control
article

Towards Understanding City-Level Routing using BGP Location Communities

Thomas Krenc, Matthew Luckie, Benoît Donnet, kc claffy, Shivani Hariprasad
article en

Abstract

BGP communities are widely used by operators to encode routing metadata for traffic engineering, policy enforcement, and operational debugging. However, ≈90% of observed communities lack public documentation, limiting their utility for research and operational analysis. Among these, city-level communities offer valuable geographic insight into routing behavior, yet remain largely untapped. In this paper, we develop a scalable method to infer the geographic meaning of undocumented city communities using BGP data. We validate our approach against a ground truth dataset covering 1,482 city communities and through operator feedback. Applied to data from May 2025, our algorithm infers the locations of 80% of city communities with a precision of 70 km or better. We publish all code and datasets to support reproducibility and further research.

Proceedings of the ACM on NetworkingVol. 3(CoNEXT4)
University of Liège (BE), University of California San Diego (US), IIJ Research Laboratory (JP)
Openalex Percentile: Top 58%
Network Traffic and Congestion Control
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Towards Understanding City-Level Routing using BGP Location Communities — Thomas Krenc, Matthew Luckie, et al. · Proceedings of the ACM on Networking (2025) | TGRS Research Map | TGRS