Rights statement: © ACM, 2017. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in SIGCOMM '17 Proceedings of the Conference of the ACM Special Interest Group on Data Communication http://dx.doi.org/10.1145/3098822.3098855
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Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 7/08/2017 |
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Host publication | SIGCOMM 2017 - Proceedings of the 2017 Conference of the ACM Special Interest Group on Data Communication |
Place of Publication | New York |
Publisher | Association for Computing Machinery, Inc |
Pages | 446-459 |
Number of pages | 14 |
ISBN (Electronic) | 9781450346535 |
<mark>Original language</mark> | English |
Event | 2017 Conference of the ACM Special Interest Group on Data Communication, SIGCOMM 2017 - Los Angeles, United States Duration: 21/08/2017 → 25/08/2017 |
Conference | 2017 Conference of the ACM Special Interest Group on Data Communication, SIGCOMM 2017 |
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Country/Territory | United States |
City | Los Angeles |
Period | 21/08/17 → 25/08/17 |
Conference | 2017 Conference of the ACM Special Interest Group on Data Communication, SIGCOMM 2017 |
---|---|
Country/Territory | United States |
City | Los Angeles |
Period | 21/08/17 → 25/08/17 |
Peering infrastructures, namely, colocation facilities and Internet exchange points, are located in every major city, have hundreds of network members, and support hundreds of thousands of interconnections around the globe. These infrastructures are well provisioned and managed, but outages have to be expected, e.g., due to power failures, human errors, attacks, and natural disasters. However, little is known about the frequency and impact of outages at these critical infrastructures with high peering concentration. In this paper, we develop a novel and lightweight methodology for detecting peering infrastructure outages. Our methodology relies on the observation that BGP communities, announced with routing updates, are an excellent and yet unexplored source of information allowing us to pinpoint outage locations with high accuracy. We build and operate a system that can locate the epicenter of infrastructure outages at the level of a building and track the reaction of networks in near real-time. Our analysis unveils four times as many outages as compared to those publicly reported over the past five years. Moreover, we show that such outages have significant impact on remote networks and peering infrastructures. Our study provides a unique view of the Internet's behavior under stress that often goes unreported.