To answer the research questions AI4NetMon will be based on a data-driven approach, leveraging on rich heterogeneous network data and state-of-the-art ML/AI methods.
The project will develop a set of open-source tools, aimed to be used by Internet operators, researchers, and practitioners (without requiring expertise in ML/AI). The AI4NetMon tools will receive as input the available network data and provide as output answers to the key questions (e.g., potential locations for new monitor deployments, monitoring selection strategies, unbiased estimates for a set of measurements).
Specifically the project objectives are to:Identify the key use cases for monitoring infrastructure
Which are the best locations to deploy monitors? What is the best set of probes to conduct measurements from? ...There is not a single answer to these questions! The best measurement strategy depends on what one wants to measure; e.g., a best set of monitors for detecting an outage, may be different than the best set of monitors for estimating an anycast catchment. Since a global answer does not exist, we need to provide answers per case. AI4NetMon will identify the use cases of higher importance to network operators, and for which the monitoring infrastructure is more frequently used.Quantify the bias and efficiency of the existing infrastructure and potential for improvement
What is the accuracy, coverage, and bias of the existing monitoring infrastructure (for a certain use case)? What would be the best achievable performance? Is there a margin for improvement? How much? Building upon preliminary findings (paper), AI4NetMon aims to answer these questions through theory, measurements, and experiments.Develop open-source tools for efficient Internet monitoring
AI4NetMon will develop AI methods and tools to (i) identify the best locations (e.g., ASes, IXPs, geographical regions) to deploy new infrastructure, (ii) select set of vantage points from the existing infrastructure for lightweight and efficient measurements (e.g., subset of Atlas probes or route collectors), and (iii) unbias estimations from existing measurements. The AI4NetMon tools will collect data from various heterogeneous data sources and aggregate them in a common dataframe, which will feed state-of-the-art AI that will provide recommendations, predictions, inferences, etc., for the desired monitoring objectives.
Bias: Quantification, Metrics, Results
We quantify the bias in the Internet measurement/monitoring infrastructure and platforms. We first define the bias and the metrics to quantify it. Then we compare the bias in the different Internet measurement infrastructures (e.g., RIPE Atlas, RIPE RIS, RouteViews) and present results that visualize this bias. The answers to the following questions
- How biased is the infrastructure? (e.g., from a scale from 0 to 1, where 0 is no bias)
- Is RIPE RIS more biased than RIPE Atlas? If yes, how much? What about RouteViews?
- Is the infrastructure more biased in terms of location or network sizes?
Recommendations for deployments of extra RIPE RIS and RIPE Atlas vantage points
We aim to find new potential locations for RIPE RIS monitors (i.e., ASes to peer with RIPE RIS). When adding an AS to the RIPE RIS monitors, then (i) the bias of the RIPE RIS changes (can increase or decrease), and (ii) there is some "improvement score" in terms of proximity to origin ASes, i.e., the total distance in AS hops to origin ASes decreases).
The following plot shows what is the trade-off by adding an AS to RIPE RIS: bias difference by adding this AS to RIPE RIS (x-axis; the smaller the better) vs. the ``improvement score'' it adds to RIPE RIS (y-axis; the larger the better).
For detailed description and results see our online tool.
A quick view for 5 recommendations for extending the RIPE RIS and Atlas infrastructures with vantage points in ASes that could decrease the bias. For more data, details, and options see the online tools for RIPE RIS and RIPE Atlas
Subsampling from Internet measurement platforms to decrease measurement bias
One can select subsets ("subsampling") of the vantage points of the Internet Measurement Platforms (IMPs), such as, RIPE Atlas, RIPE RIS, or RouteViews, in order to decrease their bias and get significantly more representative measurement data. In this documentation, we provide (i) detailed results for the bias of subsampling in IMPs, and (ii) a method for efficient subsampling.
Articles / Blog posts / Presentations
Pavlos Sermpezis "Bias in Internet Measurement Platforms" [Blog/Tool/Data], in Observable blog post, 27 October 2022. [ Link]
Pavlos Sermpezis "Bias in RIPE RIS per Route Collector" [Blog/Tool/Data], in Observable blog post, 27 October 2022. [ Link]
Pavlos Sermpezis "Extending RIPE Atlas in an unbiased way" [Blog/Tool/Data], in Observable blog post, 17 August 2022. [ Link]
Pavlos Sermpezis "Extending RIPE RIS in an unbiased way" [Blog/Tool/Data], in Observable blog post, 29 June 2022. [ Link]
Pavlos Sermpezis & Alun Davies "Revealing Bias in Internet Measurements" [Podcast], in RIPE Labs, 29 Jun 2022. [ Link]
Pavlos Sermpezis "Bias in Internet Measurement Infrastructure" [Presenation], in RIPE84, 17 May 2022. [ Link]
Pavlos Sermpezis "Bias in Internet Measurement Infrastructure", in RIPE Labs, 28 March 2022. [ Link]
Pavlos Sermpezis "Potential deployments of extra RIPE RIS monitors: Bias vs. Improvement", in Observable blog post, 14 February 2022. [ Link]
Pavlos Sermpezis "Estimating the Impact of a Hijack: Measurement Bias and How to Avoid It" [Presenation], in RIPE83 (Routing WG), 22 November 2021. [ Link]
Gergana Petrova "RACI Funding in 2021 - Planning and Projects", in RIPE Labs, 17 November 2021. [ Link]
Pavlos Sermpezis, et al. "Unbiasing Internet Measurement Platforms", under submission, 2022.
AI4NetMon [ github repo ]
Principal InvestigatorPavlos Sermpezis, Datalab, AUTh
Research AssistantsSofia Kostoglou, Datalab, AUTh
Stelios Mantzouranis, Datalab, AUTh Christos Georgitsis, Datalab, AUTh
Researchers / External CollaboratorsEmile Aben, RIPE NCC
Lars Prehn, Max Planck Institute
Web DeveloperStelios Karamanidis, Datalab, AUTh
AdvisorAthena Vakali, Datalab, AUTh