Enabling Real-Time Optical Networks as Distributed Sensors via Open-Source Telemetry Architecture

The paradigm shift towards autonomous optical networking is transforming the transport layer into a widespread distributed sensing platform. However, traditional telemetry architectures, designed for low-frequency performance monitoring (typically sampled at 1 Hz or 15-minute intervals), fail to meet the stringent requirements of physical sensing applications, which demand kHz-range sampling to capture seismic events by using indirect metrics such as State of Polarization (SOP) rotations or Jones rotation vector. This paper proposes a high-sampling rate, open-source telemetry architecture designed to bridge this gap. Implemented as a containerized microservices stack orchestrated via Docker Compose, the framework leverages Apache Kafka to decouple high-velocity sensing streams from persistent storage, elevating raw sensor data to a First-Class Citizen for immediate consumption by AI/ML agents. This approach facilitates multi-modal Sensor Fusion by enabling the temporal correlation of heterogeneous metrics in real-time. We validate the architecture through an extensive experimental scalability analysis using a Monte Carlo approach. Results demonstrate that the system sustains a throughput of over 30,000 messages/s—approximately 15 times the requirement for high-fidelity sensing—while maintaining sub-second end-to-end latency (<200 ms) in the optimal batching regime. These findings confirm the architecture's suitability for critical Early Warning scenarios, enabling robust, low-latency intervention in disaggregated optical networks.