Fiat Lux: Illuminating IPv6 Apportionment with Different Datasets

Abstract

IPv6 adoption continues to grow, making up more than 40% of client traffic to Google globally. While the ubiquity of the IPv4 address space makes it comparably easier to understand, the vast and less studied IPv6 address space motivates a variety of works detailing methodology to collect and analyze IPv6 properties, many of which use knowledge from specific data sources as a lens for answering research questions. Despite such work, questions remain on basic properties such as the appropriate prefix size for research tasks. Our work fills this knowledge gap by presenting an analysis of the apportionment of the IPv6 address space from the ground-up, using data and knowledge from numerous data sources simultaneously, aimed at identifying how to leverage IPv6 address information for a variety of research tasks. Utilizing WHOIS data from RIRs, routing data, and hitlists, we highlight fundamental differences in apportionment sizes and structural properties depending on data source and examination method. We focus on the different perspectives each dataset offers and the disjoint, heterogeneous nature of these datasets when taken together. We additionally leverage a graph-based analysis method for these datasets that allows us to draw conclusions regarding when and how to intersect the datasets and their utility. The differences in each dataset's perspective is not due to dataset problems but rather stems from a variety of differing structural and deployment behaviors across RIRs and IPv6 providers alike. In light of these inconsistencies, we discuss network address partitioning, best practices, and considerations for future IPv6 measurement and analysis projects.