Event Identification and Evaluation of Internet Outages (EIEIO)

Project Description

Event Identification and Evaluation of Internet Outages (EIEIO) is a project that will develop new algorithms to help understand Internet reliablity by improving coverage of Internet outage detection, identifying events in this raw data, and relating these events to the real world.

Our research goals are to expand Internet outage detection in these three ways. The EIEIO project will (1) develop new algorithms to increase IPv4 coverage with more sensitive analysis, examine passive and active measurements that reach to IPv6. (2) The EIEIO project will develop clustering algorithms that group raw data to events at Internet scales (millions of events over months of data). (3) The EIEIO project will improve confidence in these results with careful comparison of outage observations and events from multiple data sources, including different observation methods and external data sources.

The result of the EIEIO project will be increased confidence in our ability to use fine-grain observations of Internet outages for IPv4 and now IPv6. These results will help citizens selecting network providers to make informed choices about their network purchases, governments to assess the service their citizens receive, and ISPs compare their offerings and justify improvements.

The broader impact of this project will be data to help network architects and ISPs to change heir design to improve reliablity. In addition, network outage data that can assist first responders and citizens to understand natural disasters (such as hurricanes) as they are occurring. It will also quantifying network reliability to assist policy makers evaluating telecommunications policy and economics, and to carry out long-range planing.

Data from the EIEIO project will be made available to researchers at no cost, and used to support education and research.

Support

EIEIO is supported by NSF/CISE as an NSF Core-Small award CNS-2007106.

People

  • Guillermo Baltra, PhD student (USC CS Dept. and ISI)
  • John Heidemann, PI on this project, project leader and professor (USC/ISI)
  • Yuri Pradkin, researcher (USC/ISI)
  • Erica Stutz, undergraduate researcher (USC/SURE Program, visiting from Swarthmore College) elstutz (at) gmail.com

Publications

  • Xiao Song, Guillermo Baltra and John Heidemann 2023. Inferring Changes in Daily Human Activity from Internet Response. Proceedings of the ACM Internet Measurement Conference (Montreal, QC, Canada, Oct. 2023), to appear. [DOI] [PDF] [Dataset] Details
  • Guillermo Baltra and John Heidemann 2023. What Is The Internet? Partial Connectivity of the Internet Core. Technical Report arXiv:2107.11439v3. USC/Information Sciences Institute. [DOI] [PDF] Details
  • Tarang Saluja, Johnh Heidemann and Yuri Pradkin 2022. Differences in Monitoring the DNS Root Over IPv4 and IPv6. Proceedings of the National Symposium for NSF REU Research in Data Science, Systems, and Security (Portland, OR, USA, Dec. 2022), 194–203. [DOI] [PDF] Details
  • Asma Enayet and John Heidemann 2022. Internet Outage Detection using Passive Analysis (poster abstract). Proceedings of the ACM Internet Measurement Conference (Nice, France, Oct. 2022), 772–773. [DOI] [PDF] Details
  • Asma Enayet and John Heidemann 2022. Internet Outage Detection using Passive Analysis (poster abstract and poster). Technical Report arxiv:2209.13767v3. ACM. [DOI] [PDF] Details
  • Erica Stutz, Yuri Pradkin, Xiao Song and John Heidemann 2021. Visualizing Internet Measurements of Covid-19 Work-from-Home. Proceedings of the National Symposium for NSF REU Research in Data Science, Systems, and Security (REU 2021 Symposium) (Virtual Workshop, Dec. 2021), 5633–5638. [DOI] [PDF] Details
  • Guillermo Baltra and John Heidemann 2021. What Is The Internet? (Considering Partial Connectivity). Technical Report arXiv:2107.11439v2. USC/Information Sciences Institute. [DOI] [PDF] Details
  • John Heidemann 2021. Observing the Global IPv4 Internet: What IP Addresses Show. Invited talk at the SKC Science and Technology Webinar Series. [PDF] Details
  • Xiao Song and John Heidemann 2021. Measuring the Internet during Covid-19 to Evaluate Work-from-Home. Technical Report arXiv:2102.07433v4 [cs.NI]. USC/ISI. [PDF] Details

For related publications, please see the ANT publications web page.

Software

See also the see the ANT distribution web page.

Datasets

We make all datasets available through our dataset page.