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PacketLab is a recently proposed model for accessing remote vantage points. The core design is for the vantage points to export low-level network operations that measurement researchers could rely on to construct more complex measurements. Motivating the model is the assumption that such an approach can overcome persistent challenges such as the operational cost and security concerns of vantage point sharing that researchers face in launching distributed active Internet measurement experiments. However, the limitations imposed by the core design merit a deeper analysis of the applicability of such model to real-world measurements of interest. We undertook this analysis based on a survey of recent Internet measurement studies, followed by an empirical comparison of PacketLab-based versus native implementations of common measurement methods. We showed that for several canonical measurement types common in past studies, PacketLab yielded similar results to native versions of the same measurements. Our results suggest that PacketLab could help reproduce or extend around 16.4% (28 out of 171) of all surveyed studies and accommodate a variety of measurements from latency, throughput, network path, to non-timing data. 

The goal of this proposal, Annotated Schema (AS), is to provide a limited ontology of annotations for dataset metadata that inform a prospective user of the classes, properties, and identifiers contained in the data. The intended audience of this document includes Data Curators (annotators) who want to create meaningful dataset descriptions, and those searching for data sets in our catalog. 

The Internet Route Registry (IRR) and Resource Public Key Infrastructure (RPKI) both emerged as different solutions to improve routing security in the Border Gateway Protocol (BGP) by allowing networks to register information and develop route filters based on information other networks have registered. RPKI is a crypto system, with associated complexity and policy challenges; it has seen substantial but slowing adoption. IRR databases often contain inaccurate records due to lack of validation standards. Given the widespread use of IRR for routing security purposes, this inaccuracy merits further study. We study IRR accuracy by quantifying the consistency between IRR and RPKI records, analyze the causes of inconsistency, and examine which ASes are contributing correct IRR information. In October 2021, we found ROAs for around 20% of RADB IRR records, and a consistency of 38% and 60% in v4 and v6. For RIPE IRR, we found ROAs for 47% records and a consistency of 73% and 82% in v4 and v6. For APNIC IRR, we found ROAs for 76% records and a high consistency of 98% and 99% in v4 and v6. For AFRINIC IRR, we found ROAs for only 4% records and a consistency of 93% and 97% in v4 and v6.