Internet Outage Detection using Passive Analysis (poster abstract). Enayet, A. & Heidemann, J. In Proceedings of the ACM Internet Measurement Conference, pages 772–773, Nice, France, October, 2022. ACM. ![Internet Outage Detection using Passive Analysis (poster abstract) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Outages from natural disasters, political events, software or hardware issues, and human error place a huge cost on e-commerce ($66k per minute at Amazon). While several existing systems detect Internet outages, these systems are often too inflexible, with fixed parameters across the whole internet with CUSUM-like change detection. We instead propose a system using passive data, to cover both IPv4 and IPv6, customizing parameters for each block to optimize the performance of our Bayesian inference model. Our poster describes our three contributions: First, we show how customizing parameters allows us often to detect outages that are at both fine timescales (5 minutes) and fine spatial resolutions (/24 IPv4 and /48 IPv6 blocks). Our second contribution is to show that, by tuning parameters differently for different blocks, we can scale back temporal precision to cover more challenging blocks. Finally, we show our approach extends to IPv6 and provides the first reports of IPv6 outages.
@InProceedings{Enayet22a,
author = "Asma Enayet and John Heidemann",
title = "Internet Outage Detection using Passive
Analysis (poster abstract)",
booktitle = "Proceedings of the " # "ACM Internet Measurement Conference",
year = 2022,
sortdate = "2022-10-25",
project = "ant, eieio",
jsubject = "routing",
month = oct,
pages = "772--773",
address = "Nice, France",
publisher = "ACM",
jlocation = "johnh: pafile",
keywords = "outage detection, passive data, b-root, ipv6",
url = "https://ant.isi.edu/%7ejohnh/PAPERS/Enayet22a.html",
pdfurl = "https://ant.isi.edu/%7ejohnh/PAPERS/Enayet22a.pdf",
doi = "https://doi.org/10.1145/3517745.3563032",
abstract = "Outages from natural disasters, political events, software or hardware issues, and human error place a huge cost on e-commerce (\$66k per minute at Amazon). While several existing systems detect Internet outages, these systems are often too inflexible, with fixed parameters across the whole internet with CUSUM-like change detection. We instead propose a system using passive data, to cover both IPv4 and IPv6, customizing parameters for each block to optimize the performance of our Bayesian inference model. Our poster describes our three contributions: First, we show how customizing parameters allows us often to detect outages that are at both fine timescales (5 minutes) and fine spatial resolutions (/24 IPv4 and /48 IPv6 blocks). Our second contribution is to show that, by tuning parameters differently for different blocks, we can scale back temporal precision to cover more challenging blocks. Finally, we show our approach extends to IPv6 and provides the first reports of IPv6 outages."
}
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