ReNO-2: Supporting Human Network Operators with ML - A Cognitive Approach

• Applicants: Professor Dr. Oliver Hohlfeld; Professor Dr. Stefan Schmid
• Subject Area: Security and Dependability, Operating-, Communication- and Distributed Systems
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 511099228

Wider research context. Many network outages today are caused by human errors. This project, ReNO-2 continues our ReNO-1 project of the first phase of the SPP program (Resilience in Connected Worlds), and aims to design resilient and adaptive networks even when the humans managing them introduce chaos. As in ReNO-1, we aim to support network operators in dealing with the complexity of network management and making networks more resilient using automation, leveraging tools from machine learning (ML) and formal methods (FM). Hypotheses/research questions/objectives. The main focus of ReNO-2 is on the human operator, aiming to study and limit the effect of human errors on network resilience, also accounting for cognitive aspects. We hypothesize that a more cognitive approach is needed to support human operators work efficiently and accurately. Motivated by new technological opportunities and the combination of human-driven, ML-driven, and formal methods in network operations, and in particular the recent advent of Large Language Models (LLMs), we will explore key questions such as: How can we optimally support network operators with ML, LLM, and FM tools? How do ML and FM tools influence the confidence of network operators? Can we enhance LLM tools to provide more feedback, highlighting their limitations and risks? We will contribute methodologies to support human operators in using emerging ML, LLM, and FM tools and provide automatic feedback to users. Our methodologies target not only network operators but also university students who are learning about communication networks. Approach/methods. We will specifically consider two application scenarios, intra- and inter-domain networking. These are not only critical applications to keep the Internet connected but also have the advantage that formal method tools for configuration verification and synthesis (including our own from ReNO-1) exist. Additionally, in our labs, we have existing testbeds with which we can study how to support students optimally. Level of originality / innovation. The project is timely as the opportunities and risks of using ML and especially LLM networking tools are still poorly understood. Also, the essential cognitive aspects such as confidence (and how tools can influence it) have not yet received much attention in the literature. Primary researchers involved. The PIs are both experts in networking and already successfully collaborated in the first phase of the SPP program. PI Hohlfeld further brings in expertise on quality-of-experience aspects and is experienced with user studies. PI Schmid has additional domain expertise in intra-domain resilience-related topics and formal methods and algorithms, which form additional pillars of the project.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2378: Resilience in Connected Worlds – Mastering Failures, Overload, Attacks, and the Unexpected
• International Connection: Israel
• Cooperation Partner: Professorin Dr. Rakefet Ackerman