A system, method and computer program for handling inbound events on a technology network may include ingesting an inbound event from a connector, interfacing with one of different technology systems on the technology network, extracting a data element or a technology asset from the inbound event, and searching a database storing a new or existing inventory of technology assets in the technology network with respect to the data element or the technology asset. When the technology asset is extracted, a relationship between the technology asset and a record in the database is created. When the data element is extracted, a match between the data element and a record in the database is determined. When the match equals or exceeds a first predetermined threshold, the record in the database is enriched. When the match is less than a second predetermined threshold, a new technology asset in the database is created.

In one embodiment, a machine learning model evaluation system may define standardized, extensible class hierarchies for evaluating performance of a given machine learning model. The class hierarchies may include a plurality of target classes that formalize an expected output of the given machine learning model based on a given dataset, a plurality of output classes that formalize an actual output of the given machine learning model based on the given dataset, a plurality of metric classes that formalize a comparison of the expected output of the given machine learning model with the actual output of the given machine learning model, and a plurality of datasets. When a machine learning model is received for evaluation, the system may identify a target class, an output class, and a metric class that are applicable to the machine learning model. The system may also retrieve a dataset applicable to the machine learning model.

A system may include sensor device comprising a sensor configured to measure sensor data indicating an operational parameter of industrial automation equipment associated with an industrial automation process. The system may also include communication circuitry configured to transmit the sensor data. Additionally, the system includes a processor configured to receive the sensor data. Further, the system includes a non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause the processor to perform operations including identifying an operational state of the industrial automation equipment based on the sensor data. The operations may also include determining a discrepancy between the sensor data and the operational state. Further, the operations may include modifying an operation of the processor from a first operational mode to a second operational mode of a plurality of operational based on the comparison.

Server hardware failure is predicted, with a probability estimate, of a possible future server failure along with an estimated cause of the future server failure. Based on the prediction, the particular server can be evaluated and if the risk is confirmed, load balancing can be performed to move a load (e.g., virtual machines (VMs)) off of the at-risk server onto low-risk servers. High availability of deployed load (e.g., VMs) is then achieved. A flow of big data may be on the order of 1,000,000 parameters per minute. A scalable tree-based AI inference engine processes the flow. One or more leading indicators are identified (including server parameters and statistic types) which reliably predict hardware failure. This allows a telco operator to monitor cloud-based VMs and perform a hot-swap on virtual machines if needed by shifting virtual machines VMs from the at-risk server to low-risk servers. Servers having a health score indicating high risk are indicated on a visual display called a heat map. The heat map quickly provides a visual indication to the telco person of identities of at-risk servers. The heat map can also indicate commonalities between at-risk servers, such as if the at-risk servers are correlated in terms of protocols in use, if the at-risk servers are correlated in terms of geographic location, server manufacturer, server OS load, or the particular hardware failure mechanism predicted for the at-risk servers.

Disclosed herein are systems and methods for pattern-based QoS violation prediction. In one exemplary aspect, a method may comprise identifying a service on a computing device that is connected to a plurality of client devices and determining a plurality of micro-services comprised in the identified service. The method may comprise parsing access information to detect that a first client device is accessing a micro-service. The method may comprise determining, for a first period of time, QoS evaluation parameters for the access between the micro-service and the first client device. The method may comprise identifying changes in the QoS evaluation parameters within the first period of time, detecting a predetermined QoS violation pattern, and executing a QoS action based on the predetermined QoS violation pattern.

Disclosed is a method comprising obtaining a plurality of handover parameter values, using a first machine learning model to select a subset of handover parameter values from the plurality of handover parameter values, obtaining historical information of a plurality of terminal devices, determining a first set of optimal handover parameter values for the plurality of terminal devices from the subset of handover parameter values, tagging the first set of optimal handover parameter values with the historical information of the plurality of terminal devices to obtain a labelled dataset, and training a second machine learning model with the labelled dataset, wherein the trained second machine learning model is capable of predicting a second set of optimal handover parameter values for a first terminal device based on historical information of the first terminal device.

The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of automatic classification and modeling. The innovation can include determining a failure history of networked architecture, the failure history including data immediately prior to failure. The innovation can include machine learning the failure history to determine failure indicators. The innovation can include generating a black hole model based on the failure history and the machine learning. The innovation can include monitoring a networked architecture. The networked architecture has a set of elements comprising software elements and hardware elements interconnected in a common environment. Each element of the set of elements is monitored. The innovation can include determining an element is trending towards a failure. The trend is determined by a black hole model. The innovation can include enabling security features to prevent the element from failure.

The disclosure relates to a method, system and computer readable media for automatically managing an event in a cloud system. The method comprises determining a candidate action to be applied to the cloud system for managing the event. The method also comprises applying the candidate action to a model of the cloud system. The method comprises, upon determining that the model of the cloud system meets at least one performance indicator and that the candidate action is a proved action, applying the proved action to the cloud system.

At a cell manager external to a network-accessible service, a set of data associated with a first isolated cell of the service is obtained. Service requests representing respective subsets of the workload of the service are processed at respective cells, with each cell comprising a number of request processing nodes. The cell manager analyzes the set of data, and initiates a configuration change at the first isolated cell based on results of the analysis.

A system (10) for monitoring a network is formed by a plurality of devices (12), each device (12) of the plurality of devices having at least one status sensor (13) measuring at least one physical parameter of the device (12). The system (10) further has a monitoring platform (14) communicating with the at least one status sensor (13) by means of a telecommunications network (16).