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Recently, IP over elastic optical network (IP-over-EON) has become a promising architecture for metro and core networks. This work studies how to visualize both layers of an IP-over-EON in real time, at different granularities (e.g., at flow-level, lightpath-level, and link-level), and with self-adaptivity. Specifically, we consider the multilayer application of in-band telemetry (INT) and propose entropy-driven adaptive INT (namely, EntropyINT). We introduce stateful processing to programmable data plane (PDP) switches for EntropyINT, such that they can make local decisions to determine whether and what type of telemetry data about the IP and EON layers should be encoded in each packet. The local decisions are designed to be based on the amount of information that can be conveyed by telemetry data to the network automation system. Meanwhile, we make EntropyINT cooperate with out-of-band monitoring, to detect and locate exceptions in the EON layer. Our proposal is implemented in a real-world testbed of IP-over-EON, to evaluate its assistance to network automation. Experimental results verify the effectiveness of our proposal, and indicate that the telemetry data collected by EntropyINT and out-of-band monitoring can better assist the machine learning in network automation, for status prediction and anomaly detection. [ABSTRACT FROM AUTHOR]

Recently, the fast development of backbone networks has made the traffic, services, and infrastructure of packet-over-optical networks increasingly complicated. This stimulates research and development on fine-grained and real-time performance monitoring and troubleshooting. In this article, we propose a ProML-INT system to oversee packet-over-optical networks in real time and enable customized performance monitoring and troubleshooting. We introduce the system design in detail, and explain how to control the overhead of multilayer INT ML-INT by inserting INT fields in packets selectively. Experiments demonstrate the ProML-INT system, a small-scale packet-over-optical network testbed. The experimental results confirm that our proposal can monitor packet and optical layers jointly in real time, and the homemade data analyzer in it can leverage artificial intelligence to identify the root causes of exceptions in packet-over-optical networks correctly and promptly. [ABSTRACT FROM AUTHOR]