Abstract: Systems and methods are provided relating to power systems, such as a power grid, including for providing control to a power system by utilizing available flexibility in charging electric vehicles (EVs). The system generates control information for controlling the power system based on predicted power demand in the system during a target time period and based on predicted EV charging curtailment information, which relates to a predicted flexibility in charging EVs while meeting charging goals of the EVs during a target time period. The generated control information includes EV charging scheduling information that utilizes the predicted flexibility in charging EVs by scheduling charging of EVs to curtail or to increase an aggregate charging load of the EVs during the target time period.

Abstract: Systems and methods relating to generating metrics and providing control in relation to electric vehicles (EVs) are provided. The metrics and control may be based on information relating to environmental emissions generated by power generation sources that provide power that is used to charge the EVs. The method may generate an overall score for a time interval based on the power grid information and the environmental emissions information, wherein the overall score indicates a level of suitability for charging an EV during the time interval, wherein higher suitability is associated with a lower quantity of environmental emissions. The generating control information may be based on the overall score, wherein the control information comprises EV charging schedule information.

Abstract: Systems and methods include managing a Network Digital Twin (NDT) for a network, the NDT including an emulation model and an optimization model, wherein the emulation model is configured to emulate the network and the optimization model is configured to determine configuration changes to the network based on a cost function; performing an iterative procedure with the optimization model and the emulation model to determine one or more configuration changes to the network based on the cost function; and providing the one or more configuration changes from the iterative procedure for use in the network, where the one or more configuration changes address the cost function.

Abstract: A method of modeling an optical system includes obtaining input data for a channel in an optical system that includes a transmitter, one or more spans, and a receiver; processing the input data with a transmitter sub-model, output data from the transmitter sub-model with one or more span sub-models, and output data from one or more span sub-models with a receiver sub-model; and providing output data for the channel based on the processing. The method can also include, prior to the receiving, training an optical system model for the optical system; and decomposing the optical system model into the transmitter sub-model, the one or more span sub-models, and the receiver sub-model.

Abstract: Systems and methods are provided herein for analyzing root-causes of network access failures in a wireless network. In response to detecting that a client device experiences a network access failure that prevents communication with a server device, a method, according to one implementation, includes a step of analyzing the network access failure to predict one or more root-causes. Also, the method includes beginning a remediation procedure for remediating the one or more root-causes.

Abstract: Systems and methods for providing control in relation to electric vehicles (EVs) in an on-demand fleet of vehicles are provided. An on-demand fleet receives requests for trips that are unscheduled, which creates challenges for the fleet operator in managing and controlling fleet vehicles. A system receives information relating to EVs in the fleet and trip demand information, and provides control in relation to the fleet including generating control information based on the EV and trip demand information. The control information includes EV charging schedule information including indications of EVs to perform charging during a given time interval. The control information is transmitted for use by computing devices associated with the EVs for use in controlling the EVs.

Abstract: Systems and methods include obtaining input data related to a networking system; modeling operation of the networking system via a composite model that includes a plurality of sub-models that collectively form the composite model, wherein at least one sub-model is a behavioral sub-model and at least one sub-model is a machine learning model, wherein various data in the input data is provided to corresponding sub-models, and wherein each sub-model is configured to model one or more components in the networking system; and providing output data based on the modeling.

Abstract: Systems and methods are provided involving executing an optimizer using output power information of a renewable energy generator (REG) and market information, and executing an energy management system to manage the utilization of power generated by the REG based on the optimization. The REG is associated with a hydrogen production plant (HPP) for producing hydrogen, and the HPP is powered at least by the REG. The management of the power generated by the REG includes generating an electricity market offer to an electrical grid and a hydrogen market offer to an hydrogen distribution system. The system then dispatches REG power to the electrical grid and hydrogen to the hydrogen distribution system based on the generated market offers. REG output power typically fluctuates over time, as do other potentially relevant factors, such as consumer electricity demand, electricity transmission capacity, electricity market prices, hydrogen market prices, etc.

Abstract: Systems and methods for providing control in relation to multiple EVs, for example an EV fleet, are provided. A system generates charging control information for EVs based on a receding horizon optimization. The optimization may be based on EV charging goal information related to the EVs including information relating to target charging completion time, and target EV battery state of charge (SoC) information at target charging completion times. The optimization may also be based on prediction information relating to EVs predicted to become available for charging during the optimization horizon. The charging control information may comprise indications of individual EVs to charge during a given time interval during the horizon. The system utilizes various available information as well as predicted data to provide more optimal control to EVs, thereby taking advantage of previously missed opportunities for enhanced optimization.

Abstract: Systems and methods are provided for predicting an impending change to Interior Gateway Protocol (IGP) metrics associated with Network Elements (NEs) in an optical network. A method, according to one implementation, includes receiving Performance Monitoring (PM) data related to an optical network having a plurality of links. The method also includes analyzing the PM data to predict the likelihood of an impending change to an IGP metric associated with a problematic link of the plurality of links.

Abstract: Systems and methods include obtaining network data including first data of devices and services in the network, Performance Monitoring (PM) data associated with the devices and services and with associated timestamps, and second data including any of tickets, alarms, and events affecting some of the devices and services and with associated timestamps; obtaining one or more target events from the second data based on associated operational impact in the network; determining the PM data that is statistically correlated with the one or more target events; determining the statistically correlated PM data over a corresponding time based on the associated timestamps of the PM data and the one or more target events; and providing labels for the determined statistically correlated PM data with an associated label based on the associated target event of the one or more target events.

Abstract: Systems and methods are provided for predicting an impending change to Interior Gateway Protocol (IGP) metrics associated with Network Elements (NEs) in an optical network. A method, according to one implementation, includes receiving Performance Monitoring (PM) data related to an optical network having a plurality of links. The method also includes analyzing the PM data to predict the likelihood of an impending change to an IGP metric associated with a problematic link of the plurality of links.

Abstract: Methods and systems are provided for controlling the charging of electric vehicles (EVs) and other assets based on power flow information of a power grid. The strategic control of charging of the assets, by way of generated charging control information, contributes to an effort to respect technical constraints of the power system, thereby minimizing violations of the technical constraints, and thus minimizing damage to the power grid infrastructure. New power flow information for a subsequent time period may then be generated based on the at least part of the charging control information, and this new power flow information may then in turn be used to generate new charging control information for the subsequent time period. In addition, a method contribute to the generation of a solution to an optimal power flow (OPF) problem in the power grid based on strategic controlling of the charging of a plurality of assets in the power system.

Abstract: Systems and methods include steps of determining a state of a network based on telemetry data; determining a value of a reward associated with the state; determining an action to take on the network to bring the network to a next state that is expected to have a better than or equal to value of the reward; and causing the action to be implemented in the network. The steps can also include continuing the determining steps and the causing step.

Abstract: Systems and methods for analyzing and prioritizing alarms in a communications network are provided. A method, according to one implementation, includes the step of obtaining network information regarding the condition of a network. Using the network information, the method further includes performing a hybrid Machine Learning (ML) technique that includes training and inference of a plurality of ML models to calculate metrics of the network. Also, the method includes the step of selecting one of the plurality of ML models based on a combination of the metrics.

Abstract: Artificial Intelligence (AI)-based network control includes obtaining data from a network having a plurality of network elements; analyzing the data with one or more Machine Learning (ML) algorithms to determine one or more actions for network control; analyzing the determined one or more actions to determine any risks associated therewith; and one of allowing, modifying, and blocking the determined one or more actions based on the determined risks to safeguard the network. The risks can be based on one or more of (1) non-deterministic behavior AI inference which is statistical in nature, (2) unbounded uncertainty of the AI inference that can result in arbitrarily large inaccuracy on rare occasions, (3) unpredictable behavior of the AI inference in presence of input data that is different than data in training and testing datasets, and (4) malicious input data.

Abstract: Systems and methods described herein are adapted to predict the intent of a network operator when it is determined that configuration changes (or config changes) are made to a network device. A method, according to one implementation, includes the step of collecting raw data related to a Network Element (NE). The method also includes the step of pre-processing the raw data to obtain a config change associated with the NE. Also, the method includes the step of applying the config change as input to a ML model to predict a user intent representing a desired network outcome.

Abstract: Systems and methods are provided for analyzing one or more root causes of service degradation events in a network or other environment. A method, according to one implementation, includes a step of monitoring a plurality of overlying services offered in an underlying infrastructure having a plurality of resources arranged with a specific topology. In response to detecting a negative impact on the overlying services during a predetermined time window and based on an understanding of the specific topology, the method further includes the step of identifying suspect components from the plurality of resources in the underlying infrastructure. The method also includes the step of obtaining status information with respect to the suspect components to determine a root cause of the negative impact on the overlying services.

Abstract: Systems, methods, and Machine Learning (ML) techniques are provided for detecting changes to configuration information associated with network devices and determining if the changes are network compliant. A method, according to one implementation, includes the step of fetching configuration data associated with a Network Element (NE) to be monitored. Based on detection of a configuration difference (config diff), whereby the configuration data has changed with respect to previously-stored configuration information, the method further includes the step of monitoring the configuration data to determine if the configuration data conforms to predetermined compliance rules and policies.

Abstract: Systems and methods include receiving a machine learning model that is configured to detect anomalies in network devices operating in a multi-layer network, wherein the machine learning model is trained via unsupervised learning that includes training the machine learning model with unlabeled data that describes an operational status of the network devices over time; receiving live data related to a current operational status of the network devices; analyzing the live data with the machine learning model; and detecting an anomaly related to any of the network device based on the analyzing.