MITHRIL: Multi-Objective Topology Synthesis with Reinforcement Learning for Critical Networks

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.