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1. A Hop Away from Everywhere: A View of the Intercontinental Long-haul Infrastructure

   https://doi.org/10.1145/3673660.3655041  

   Carisimo, Esteban; Wang, Caleb J; Weaver, Mia; Bustamante, Fabián; Barford, Paul (June 2024, ACM SIGMETRICS Performance Evaluation Review)

   We present a longitudinal study of intercontinental long-haul links (LHL) - links with latencies significantly higher than that of all other links in a traceroute path. Our study is motivated by the recognition of these LHLs as a network-layer manifestation of transoceanic undersea cables. We present a methodology and associated processing system for identifying long-haul links in traceroute measurements, and report on our findings from. We apply this system to a large corpus of traceroute data and report on multiple aspects of long haul connectivity including country-level prevalence, routers as international gateways, preferred long-haul destinations, and the evolution of these characteristics over a 7 year period. 

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2. A Hop Away from Everywhere: A View of the Intercontinental Long-haul Infrastructure

   https://doi.org/10.1145/3626778  

   Carisimo, Esteban; Wang, Caleb J; Weaver, Mia; Bustamante, Fabián E; Barford, Paul (December 2023, Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   We present a longitudinal study of intercontinental long-haul links (LHLs) - links with latencies significantly higher than that of all other links in a traceroute path. Our study is motivated by the recognition of these LHLs as a network-layer manifestation of critical transoceanic undersea cables. We present a methodology and associated processing system for identifying long-haul links in traceroute measurements. We apply this system to a large corpus of traceroute data and report on multiple aspects of long haul connectivity including country-level prevalence, routers as international gateways, preferred long-haul destinations, and the evolution of these characteristics over a 7 year period. We identify 85,620 layer-3 links (out of 2.7M links in a large traceroute dataset) that satisfy our definition for intercontinental long haul with many of them terminating in a relatively small number of nodes. An analysis of connected components shows a clearly dominant component with a relative size that remains stable despite a significant growth of the long-haul infrastructure. 

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3. A Manifold View of Connectivity in the Private Backbone Networks of Hyperscalers

   https://doi.org/10.1145/3604620  

   Salamatian, Loqman; Anderson, Scott; Mathews, Joshua; Barford, Paul; Willinger, Walter; Crovella, Mark (August 2023, Communications of the ACM)

   As hyperscalers such as Google, Microsoft, and Amazon play an increasingly important role in today's Internet, they are also capable of manipulating probe packets that traverse their privately owned and operated backbones. As a result, standard traceroute-based measurement techniques are no longer a reliable means for assessing network connectivity in these global-scale cloud provider infrastructures. In response to these developments, we present a new empirical approach for elucidating connectivity in these private backbone networks. Our approach relies on using only lightweight (i.e., simple, easily interpretable, and readily available) measurements, but requires applying heavyweight mathematical techniques for analyzing these measurements. In particular, we describe a new method that uses network latency measurements and relies on concepts from Riemannian geometry (i.e., Ricci curvature) to assess the characteristics of the connectivity fabric of a given network infrastructure. We complement this method with a visualization tool that generates a novel manifold view of a network's delay space. We demonstrate our approach by utilizing latency measurements from available vantage points and virtual machines running in datacenters of three large cloud providers to study different aspects of connectivity in their private backbones and show how our generated manifold views enable us to expose and visualize critical aspects of this connectivity. 

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4. Learning to Extract and Use ASNs in Hostnames

   https://doi.org/10.1145/3419394.3423639  

   Luckie, Matthew; Marder, Alexander; Fletcher, Marianne; Huffaker, Bradley; Claffy, K. (October 2020, IMC '20: Proceedings of the ACM Internet Measurement Conference)

   null (Ed.)

   We present the design, implementation, evaluation, and validation of a system that learns regular expressions (regexes) to extract Autonomous System Numbers (ASNs) from hostnames associated with router interfaces. We train our system with ASNs inferred by RouterToAsAssignment and bdrmapIT using topological constraints from traceroute paths, as well as ASNs recorded by operators in PeeringDB, to learn regexes for 206 different suffixes. Because these methods for inferring router ownership can infer the wrong ASN, we modify bdrmapIT to integrate this new capability to extract ASNs from hostnames. Evaluating against ground truth, our modification correctly distinguished stale from correct hostnames for 92.5% of hostnames with an ASN different from bdrmapIT’s initial inference. This modification allowed bdrmapIT to increase the agreement between extracted and inferred ASNs for these routers in the January 2020 ITDK from 87.4% to 97.1% and reduce the error rate from 1/7.9 to 1/34.5. This work presents a new avenue for collecting validation data, opening a broader horizon of opportunity for evidence-based router ownership inference. 

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5. Pushing the Boundaries with bdrmapIT: Mapping Router Ownership at Internet Scale

   https://doi.org/10.1145/3278532.3278538  

   Marder, Alexander; Luckie, Matthew; Dhamdhere, Amogh; Huffaker, Bradley; claffy, kc; Smith, Jonathan M. (January 2018, Proceedings of the 2018 ACM SIGCOMM conference on Internet measurement conference - IMC '18)

   Two complementary approaches to mapping network boundaries from traceroute paths recently emerged [27,31]. Both approaches apply heuristics to inform inferences extracted from traceroute measurement campaigns. bdrmap [27] used targeted traceroutes from a specific network, alias resolution probing techniques, and AS relationship inferences, to infer the boundaries of that specific network and the other networks attached at each boundary. MAPIT [31] tackled the ambitious challenge of inferring all AS-level network boundaries in a massive archived collection of traceroutes launched from many different networks. Both were substantial contributions to the state-of-the-art, and inspired a collaboration to explore the potential to combine the approaches. We present and evaluate bdrmapIT, the result of that exploration, which yielded a more complete, accurate, and general solution to this persistent and central challenge of Internet topology research. bdrmapIT achieves 91.8%-98.8% accuracy when mapping AS boundaries in two Internet-wide traceroute datasets, vastly improving on MAP-IT's coverage without sacrificing bdrmap's ability to map a single network. The bdrmapIT source code is available at https://git.io/fAsI0. 

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