Enabling real-time optical networks as distributed sensors via open-source telemetry architecture

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Abstract
The paradigm shift toward 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 min 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 the 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 ( <200ms ) 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.
Funding Information
  • EU Horizon project SENSEI (101189545)

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