Paper accepted at LCN'25

8 July 2025, by Mathias Fischer

Photo: https://www.ieeelcn.org

We are happy to announce that our paper, “MITHRIL: Multi-Objective Topology Synthesis with Reinforcement Learning for Critical Networks”, has been accepted for publication at the 50th IEEE Conference on Local Computer Networks (IEEE LCN'25). In this paper, we present a reinforcement learning approach to optimize topologies for critical networks toward multiple design objectives.

We look forward to presenting our results at LCN'25 in Sydney, Australia, and seek an exchange with international researchers.

Paper abstract:

Mission-critical systems (MCSs) have evolving latency and reliability requirements, even under challenging conditions such as node and link failures and cyberattacks. To fulfill these requirements, emerging networking technologies like the IEEE 802.1 Time-Sensitive Networking standards provide several protocols for deterministic communication on top of off-the-shelf Ethernet equipment. While Ethernet-based networks offer better configurability than legacy fieldbus systems, they still require the design of adequate topologies for MCSs that fulfill various design objectives such as optimal quality of service and increased resilience against challenges. In this paper, we propose MITHRIL, a multi-objective topology synthesis model with reinforcement learning. It leverages deep reinforcement learning to optimize Ethernet-based topologies in terms of resilience and effectiveness, while adhering to realistic MCS constraints. Our evaluation indicates that MITHRIL enhances the failure and attack tolerance of network topologies while reducing the associated costs, compared to well-connected topologies and other heuristics from the literature.