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Bio: Fabio Farina has a PhD in Computer Science and works with GARR since 2010. Fabio works on European projects, on the creation of new services and on NFV, Edge and orchestration under the GARR Network evolution framework. In detail, during last year Fabio contributed to the refactoring and the automation of the monitoring and logging software stacks adopted by the GARR Infrastructure and System Support departments.
Session 43
Community Shared Telemetry, Karl Newell (Internet2)
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Abstract: experiences monitoring and measuring the many different networks we support at different scales ranging from campus to regional to national and international. In particular, the topics I would like to focus on:
- High level operational views, derived from multiple data sets. Going from many separate data collection systems (logs, monitoring alerts, telemetry, etc) that require a high amount of user expertise to more simplified views.
- Empowering end users to explore and arrange their own data. Going from every report, customization, etc requiring software features and developer time to being able to have end users create and tune their own visualizations.
- The tools that we use to accomplish all of this, including the extensions that we had to do and the limitations or overall philosophy changes that arose as a result.
Bio: GlobalNOC at Indiana University as the head of our Data Collection and Analysis team.
Making OSS Network Data Available to Network Researchers, Alex Moura (RNP)
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Bio: Alex Moura is Network Engineer and Science Engagement Specialist at RNP, the Brazilian National Research and Education Network, and holds a master's degree in information systems and computer networks from Unirio.
Session 54
Network Telemetry at AmLight, Jeronimo Bezerra (Amlight)
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Bio:
DPDK + Kafka: Multi-MPPS Telemetry Data Ingest and Stream Processing at ESnet, Richard Cziva (ESnet)
NetSage measurement and monitoring platform, Doug Jontz (Indiana University)
Abstract:
Bio: Richard Cziva is a software engineer at ESnet. He has a range of technical interests including traffic and performance analysis, data-plane programmability, high-speed packet processing, software-defined networking, and network function virtualization.
Prior to joining ESnet in 2018, Richard was a Research Associate at University of Glasgow, where he looked at how advanced services (e.g., personalized firewalls, intrusion detection modules, measurement functions) can be implemented and managed inside wide area networks with programmable edge capabilities.
Richard holds a BSc in Computer Engineering (2013) from Budapest University of Technology and Economics, Hungary and a Ph.D. in Computer Science (2018) from University of Glasgow, United Kingdom.
NetSage measurement and monitoring platform, Doug Jontz (Indiana University)
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A Proposal towards sFlow Monitoring Dashboards for AI-controlled NRENs, Mariam Kiran (Esnet)
Abstract: Network monitoring collects heterogeneous data such as various kinds of performance data such as TCP transfers, packet-related checks, bandwidth, download speeds, and more, usually through passive and active probing of the network. Multiple monitoring tools can help collect these disparate, heterogeneous metrics, but mostly through probing the network which introduces challenges of extra noise or packets that are also recorded. Additionally having a visualization tool that encompasses all this data into one is challenging to build. In this paper, we start by discussing NetGraf, a tool we were developing for multiple network monitoring tools to visualize using Grafana, and discuss the key findings and challenges we faced. As a result, we propose to further work towards sFlow monitoring dashboard to improve network monitoring challenges. This paper contributes to the theme of automating open-source network monitoring tools software setups and their usability for researchers looking to deploy an end-to-end monitoring stack on their own testbeds.
Bio: Mariam Kiran is a research scientist with shared positions with Energy Sciences Network and the Scientific Data Management (SDM) group in Computational Research Division. Her work specifically concentrates on using advanced software and machine learning techniques to advance system architectures, particularly high-speed networks such as DOE networks.
Her current work is exploring reinforcement learning, unsupervised clustering and classification techniques to optimally control distributed network resources, improving high-speed big data transfers for exascale science applications and optimize how current network infrastructure is utilized. Kiran is the recipient of the DOE ASCR Early Career Award in 2017. Before joining LBNL in 2016, Kiran held positions as a lecturer and research fellow at the Universities of Sheffield and Leeds in the UK. She earned her undergrad and PhD degree in software engineering and computer science from the University of Sheffield, UK in 2011.A Proposal towards sFlow Monitoring Dashboards for AI-controlled NRENs, Mariam Kiran (Esnet)