Internet data analysis is essential for policymakers and regulators to make informed decisions. Despite the various datasets made available by the research community, the analysis of these datasets is challenging due to their various formats and required analysis tools. The Internet Yellow Pages (IYP) database is designed to simplify Internet data analysis by combining many datasets into a single format. Hence, IYP enables users to retrieve data from various Internet datasets using a single querying language called Cypher. However, learning Cypher and understanding IYP’s schema is still challenging. Large Language Models (LLMs) offer the potential to generate Cypher queries from English text. In this paper, we assess the ability of different LLMs to generate Cypher queries. We introduce CypherEval, a dataset for evaluating LLM-generated Cypher queries, and we propose a methodology to benchmark LLMs for IYP. Finally, we present Pythia, a system for the generation of IYP Cypher queries.

The popularity of the RIPE Atlas measurement platform comes primarily from its openness and unprecedented scale. The platform provides users with over ten thousand vantage points, called probes, and is usually considered as giving a reasonably faithful view of the Internet. A good use of Atlas, however, requires a clear understanding of its limitations and bias. In this work we highlight the influence of probe locations on Atlas measurements and advocate the importance of selecting a diverse set of probes for fair measurements. We propose Metis, a data-driven probe selection method, that picks a diverse set of probes based on topological properties (e.g., round-trip time or AS-path length). Using real experiments we show that, compared to Atlas’ default probe selection, Metis’ probe selections collect more comprehensive measurement results in terms of geographical, topological, RIR, and industry-type coverage. Metis triples the number of probes from the underrepresented AFRINIC and LACNIC regions, and improves geographical diversity by increasing the number of unique countries included in the probe set by up to 59%. In addition, we extend Metis to identify locations on the Internet where new probes would be the most beneficial for improving Atlas’ footprint. Finally, we present a website where we publish periodically updated results and provide easy integration of Metis’ selections with Atlas.

The Internet measurement community has significantly advanced our understanding of the Internet by documenting its various components. Subsequent research often builds on these efforts, using previously published datasets. This process is fundamental for researchers, but a laborious task due to the diverse data formats, terminologies, and areas of expertise involved. Additionally, the time-consuming task of merging datasets is undertaken only if the expected benefits are worthwhile, posing a barrier to simple exploration and innovation. In this paper we present the Internet Yellow Pages (IYP), a knowledge graph for Internet resources. By leveraging the flexibility of graph databases and ontology-based data integration, we compile datasets (currently 46) from diverse and independent sources into a single harmonized database where the meaning of each entity and relationship is unequivocal. Using simple examples, we illustrate how IYP allows us to seamlessly navigate data coming from numerous underlying sources. As a result, IYP significantly reduces time to insight, which we demonstrate by reproducing two past studies and extending them by incorporating additional datasets available in IYP. Finally, we discuss how IYP can foster the sharing of datasets as it provides a universal platform for querying and describing data. This is a seminal effort to bootstrap what we envision as a community-driven project where dataset curation and ontology definitions evolve with the Internet measurement community.

Delivering videos under less-than-ideal network conditions without compromising end-users' quality of experiences is a hard problem. Virtually all prior work follow a piecemeal approach---either "tweaking" the fully reliable transport layer or making the client "smarter." We propose VOXEL, a cross-layer optimization system for video streaming. We use VOXEL to demonstrate how to combine application-provided "insights" with a partially reliable protocol for optimizing video streaming. To this end, we present a novel ABR algorithm that explicitly trades off losses for improving end-users' video-watching experiences.