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Soft-failure localization frameworks typically use if-else rules to localize failures based on the received telemetry data. However, in certain cases, particularly in disaggregated networks, some devices may not implement telemetry, or their telemetry may not be readily available. Alternatively, machine-learning-based (ML-based) frameworks can automatically learn complex relationships between telemetry and the fault location, incorporating information from the telemetry data collected network-wide. This paper evaluates an ML-based soft-failure localization framework in scenarios of partial telemetry. The framework is based on an artificial neural network (ANN) trained by optical signal and noise power models that simulate the network telemetry upon all possible failure scenarios. The ANN can be trained in less than 2 min, allowing it to be retrained according to the available partial telemetry data. The ML-based framework exhibits excellent performance in scenarios of partial telemetry, practically interpolating the missing data. We show that in the rare cases of incorrect failure localization, the actual failure is in the localized device’s vicinity. We also show that ANN training is accelerated by principal component analysis and can be carried out using cloud-based services. Finally, the evaluated ML-based framework is emulated in a software-defined-networking-based setup using the gNMI protocol for streaming telemetry.

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Micro-mobility protocols aim to improve localized mobility by reducing the handover overheads. In this paper we present the Mobility Plane Architecture (MPA). This architecture was designed to support micro-mobility in standard IP or MPLS/GMPLS networks in a network-centric way, that is, the burden demanded by micro-mobility is placed on the network, not on the mobile nodes. The main advantages of this architecture are its independence of IPv6 and the absence of new protocols for supporting L3 mobility.