Growth analysis of a large ISP

Ferguson, Andrew D.; Place, Jordan; Fonseca, Rodrigo

doi:10.1145/2504730.2504769

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…In 2013, Ferguson et al studied the interconnection, capacity, geography, and growth of Cogent's network. They continuously resolved the hostnames of address space used by Cogent to number their routers, and then applied a regex that they manually constructed to Cogent's hostnames to extract interface speeds, locations, and names of Cogent's routers [7]. They found that Cogent's network grew by 11 routers per week between 2012 and 2013.…”

Section: Extracting Information From Dnsmentioning

confidence: 99%

“…Researchers have traditionally manually derived regular expressions (regexes) from apparent router naming conventions to extract network topology information, such as the geographic placement and roles of routers, link speeds, and router names (e.g., [6,7,27]). In this paper, we consider the challenge of automatically learning if an operator uses a convention within a suffix that includes a router name -i.e., a unique router identifier -by evaluating automatically generated regexes.…”

Section: Introductionmentioning

confidence: 99%

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Learning Regexes to Extract Router Names from Hostnames

Luckie

Huffaker

claffy

2019

Proceedings of the Internet Measurement Conference

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We present the design, implementation, evaluation, and validation of a system that automatically learns to extract router names (router identifiers) from hostnames stored by network operators in different DNS zones, which we represent by regular expressions (regexes). Our supervised-learning approach evaluates automatically generated candidate regexes against sets of hostnames for IP addresses that other alias resolution techniques previously inferred to identify interfaces on the same router. Conceptually, if three conditions hold: (1) a regex extracts the same value from a set of hostnames associated with IP addresses on the same router; (2) the value is unique to that router; and (3) the regex extracts names for multiple routers in the suffix, then we conclude the regex accurately represents the naming convention for the suffix. We train our system using router aliases inferred from active probing to learn regexes for 2550 different suffixes. We then demonstrate the utility of this system by using the regexes to find 105% additional aliases for these suffixes. Regexes inferred in IPv4 perfectly predict aliases for ≈85% of suffixes with IPv6 aliases, i.e., IPv4 and IPv6 addresses representing the same underlying router, and find 9.0 times more routers in IPv6 than found by prior techniques. CCS CONCEPTS • Information systems → Clustering and classification; • Networks → Naming and addressing.

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Section: Extracting Information From Dnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Regexes to Extract Router Names from Hostnames

Luckie

Huffaker

claffy

2019

Proceedings of the Internet Measurement Conference

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“…POPsicle finds 2.4x more physical nodes than Ark probes, and in our custom deployment in Chicago, POPsicle finds nearly all POPs identified in the Atlas physical topologies. In a case study of Cogent's network, POPsicle identified more than 90% of the POPs known through Atlas as well as through the recently described technique based on using reverse DNS records [21]. Moreover, it found many more POPs than Ark probes, or the most recent Rocketfuel measurements.…”

Section: Main Findings and Implicationsmentioning

confidence: 99%

“…To validate this idea, we deployed POPsicle at the Equinix IXP in Chicago, USA, and measured the number of POPs for 10 ISPs and found that POPsicle reveals almost all POPs compared to Atlas and extra POPs (in certain cases) compared to Ark. We also find through a case study of Cogent network that POPsicle identifies over 90% of the nodes identified in Atlas or by the reverse DNS technique of [21], compared with about 65% of the POPs identified through Ark, and only 25% identified in the most recently available Rocketfuel data.…”

Section: Introductionmentioning

confidence: 95%

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Layer 1-informed Internet Topology Measurement

Durairajan

Sommers

Barford

2014

Proceedings of the 2014 Conference on Internet Measurement Conference

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Understanding the Internet's topological structure continues to be fraught with challenges. In this paper, we investigate the hypothesis that physical maps of service provider infrastructure can be used to effectively guide topology discovery based on network layer TTL-limited measurement. The goal of our work is to focus layer 3-based probing on broadly identifying Internet infrastructure that has a fixed geographic location such as POPs, IXPs and other kinds of hosting facilities. We begin by comparing more than 1.5 years of TTLlimited probe data from the Ark [25] project with maps of service provider infrastructure from the Internet Atlas [15] project. We find that there are substantially more nodes and links identified in the service provider map data versus the probe data. Next, we describe a new method for probe-based measurement of physical infrastructure called POPsicle that is based on careful selection of probe sourcedestination pairs. We demonstrate the capability of our method through an extensive measurement study using existing "looking glass" vantage points distributed throughout the Internet and show that it reveals 2.4 times more physical node locations versus standard probing methods. To demonstrate the deployability of POPsicle we also conduct tests at an IXP. Our results again show that POPsicle can identify more physical node locations compared with standard layer 3 probes, and through this deployment approach it can be used to measure thousands of networks world wide.

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