Thomas Krenc

BGP data collected from operational routers by platforms such as RIPE RIS or RouteViews is essential to address connectivity issues and analyze the global Internet's structure. However, the vast and ever-growing volume of this data makes deriving useful insights challenging: Existing APIs and dashboards offer limited perspectives, while analyzing large archives of MRT files is slow and cumbersome. We present ChatBGP, a domain-specific chatbot that turns plain-English questions about BGP data into Python code that runs swiftly and returns accurate answers. ChatBGP is enabled by four contributions: (i) a measurement study showing that BGP data is highly redundant and therefore highly compressible; (ii) a database scheme that exploits this redundancy to compress BGP data while enabling fast retrieval of specific data elements; (iii) an expressive API that exposes this database through a simple interface; and (iv) a prompt-engineering scheme that guides ChatGPT to synthesize optimized Python code that uses this API to accurately answer user queries, while retrieving only the data needed to answer each query. With ChatBGP, gaining insights into BGP data becomes effortless and significantly faster—e.g., delivering results in seconds instead of hours with existing tools. Network operators can rapidly diagnose connectivity issues, including security threats, researchers can analyze larger datasets while improving reproducibility, and students can engage with BGP monitoring in a more intuitive and interactive way.

BGP communities are essential for inter-domain routing control, but their semantics are often buried in noisy, unstructured operator documentation. Manual inspection and annotation is an laborious process that does not scale. Meanwhile, existing automated methods, mainly relying on regular expressions, cannot reliably interpret semantic meaning or handle placeholder-style formats. We develop an LLM-based framework for extracting high-precision BGP community semantics from heterogeneous operator documentation. Our framework combines format-aware preprocessing to filter community-relevant content from noisy operator documents, schema-guided extraction to generate structured output, and consensus-based aggregation across models to mitigate hallucinations and false extractions. Using a manually-annotated benchmark dataset containing 9,244 community value–meaning pairs, our framework achieves 85-99% precision across different extraction tasks and substantially outperforms prior state-of-the-art methods on placeholder communities and preserving semantic accuracy. Using our framework, we construct one of the largest open-access available BGP community dictionaries, containing more than 29.9K semantically annotated communities across 766 ASes. Finally, we perform the first large-scale analysis of semantic variability in operator-defined BGP communities using our dictionary. We release our code, benchmark, and constructed dictionary to support future research.

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.

BGP data collection platforms as currently architected face fundamental challenges that threaten their long-term sustainability. Inspired by recent work, we analyze, prototype, and evaluate a new optimization paradigm for BGP collection. Our system scales data collection with two components: analyzing redundancy between BGP updates and using it to optimize sampling of the incoming streams of BGP data. An appropriate definition of redundancy across updates depends on the analysis objective. Our contributions include: a survey, measurements, and simulations to demonstrate the limitations of current systems; a general framework and algorithms to assess and remove redundancy in BGP observations; and quantitative analysis of the benefit of our approach in terms of accuracy and coverage for several canonical BGP routing analyses such as hijack detection and topology mapping. Finally, we implement and deploy a new BGP peering collection system that automates peering expansion using our redundancy analytics, which provides a path forward for more thorough evaluation of this approach.

Dramatic growth in Internet connectivity poses a challenge for the resource-constrained data collection efforts that support scientific and operational analysis of interdomain routing. Inspired by tradeoffs made in other disciplines, we explore a fundamental reconceptualization to how we design public BGP data collection architectures: an overshoot-and-discard approach that can accommodate an order of magnitude increase in vantage points by discarding redundant data shortly after its collection. As defining redundant depends on the context, we design algorithms that filter redundant updates without optimizing for one objective, and evaluate our approach in terms of detecting two noteworthy phenomena using BGP data: AS-topology mapping and hijacks. Our approach can generalize to other types of Internet data (e.g., traceroute, traffic). We offer this study as a first step to a potentially new area of Internet measurement research.

BGP communities allow operators to influence routing decisions made by other networks (action communities) and to annotate their network's routing information with metadata such as where each route was learned or the relationship the network has with their neighbor (information communities). BGP communities also help researchers understand complex Internet routing behaviors. However, there is no standard convention for how operators assign community values, and significant efforts to scalably infer community meanings have ignored this high-level classification. We discovered that doing so comes at significant cost in accuracy, of both inference and validation. To advance this narrow but powerful direction in Internet infrastructure research, we design and validate an algorithm to execute this first fundamental step: inferring whether a BGP community is action or information. We applied our method to 78,480 community values observed in public BGP data for May 2023. Validating our inferences (24,376 action and 54,104 informational communities) against available ground truth (6,259 communities) we find that our method classified 96.5% correctly. We found that the precision of a state-of-the-art location community inference method increased from 68.2% to 94.8% with our classifications. We publicly share our code, dictionaries, inferences, and datasets to enable the community to benefit from them.