Peer-to-peer disaggregated telemetry for autonomic machine-learning-driven transceiver operation

Abstract

Autonomic networking and monitoring will drive the evolution of next generation software defined networking (SDN) optical networks towards the zero touch networking paradigm. Optical telemetry services will play a key role to enable advanced network awareness at device and component granularity. Optical disaggregation is pushing the adoption of open models, enabling multi-vendor interoperability, including telemetry. Moreover, due to whitebox programmability and the adoption of open source micro services, it is becoming feasible to monitor data streams from optical devices related to quality of transmission key performance indicators. Finally, due to mature big data analytics platforms, including machine learning and artificial intelligence, the telemetry data lake is processed to effectively detect network anomalies. However, current centralized telemetry architectures are prone to scalability issues, suboptimal soft failure recovery due to operational mode limitations, and/or the inability of the SDN controller of tuning finer or proprietary transmission parameters. Conversely, a number of soft failures might be detected and recovered directly at the optical card transmitter, often in a hitless fashion, also relying on optimized vendor-proprietary configurations. The paper proposes what we believe to be a novel peer-to-peer telemetry (P2PT) service ready for next generation digital coherent optics cards, for local processing and soft failure recovery at the transceiver agent level. The P2PT architecture, workflow, and subscription extensions are conceived to enable direct and fast recovery at the transceiver level, resorting to optical signal retuning and adaptations. Experimental evaluations, including lightweight machine learning detection at the card agent, are provided in a multi-vendor disaggregated optical network testbed to assess different soft failure use cases and P2PT service scalability.