Abstract: A system to predict events in a telecommunications network includes a processor; and memory storing instructions that, when executed, cause the processor to, responsive to obtained Performance Monitoring (PM) data over time from the telecommunications network, reduce an n-dimensional time-series into a 1-dimensional distribution, n being an integer represent a number of different PM data, wherein the n different PM data relate to a component, device, or link in the telecommunications network, utilize one or more forecast models to match the 1-dimensional distribution and to extrapolate the 1-dimensional distribution towards future time, and display a graphical user interface of a graph of the 1-dimensional distribution and the extrapolated 1-dimensional distribution, wherein the graph displays a probability of the component, device, or link being normal versus time. Also, techniques are described herein for labeling of PM data for use in supervised Machine Learning (ML).

Abstract: Systems and methods include obtaining performance monitoring data from a network which operates and generates the performance monitoring data which reflects a current state of the network; responsive to a predetermined reward which quantifies minimizing or maximizing an aspect in the network for Reinforcement Learning, causing one or more actions to be performed in the network, wherein the one or more actions are based on the predetermined reward; obtaining updated performance monitoring data from the network subsequent to the one or more actions to determine an updated state of the network; and continuing the causing the one or more actions to be performed in the network based on the current state and the updated state. The causing the one or more actions to be performed in the network is performed to maximize or minimize a total discounted accumulated reward.

Abstract: Technology for link parameter identification in an optical communications network is described. A first trained artificial neural network (ANN) may be applied to first input values representative of nonlinear noise in a signal received at a receiver from a transmitter over a link in the optical communications system, thereby generating first output values. A second trained ANN may be applied to second input values comprising the first output values and one or more known parameters of the link, thereby generating second output values. One or more link parameter estimates may be identified based on the second output values. In some examples, the first trained ANN has an architecture specialized for two-dimensional image recognition and therefore suitable for the image-like properties of the first input values. For example, the first trained ANN may comprise a deep residual learning network (ResNet) or a Convolution Neural Network (CNN).

Abstract: Systems and methods implemented by a computer to detect abnormal behavior in a network include obtaining Performance Monitoring (PM) data including one or more of production PM data, lab PM data, and simulated PM data; determining a model based on machine learning training with the PM data; receiving live PM data from the network; utilizing the live PM data with the model to detect an anomaly in the network; and causing an action to address the anomaly.

Abstract: A monitoring system for microservices includes a messaging system communicatively coupled to a plurality of services, wherein the messaging system is configured to publish metrics from the plurality of services to the analytics engine; an analytics engine communicatively coupled to the messaging system, wherein the analytics engine is configured to analyze the metrics to determine insights related to operation of the plurality of services; and a policy engine communicatively coupled to the analytics engine, wherein the policy engine is configured to determine actions associated with one or more services based on the analysis and push the actions to the one or more services for implementation.