A device receives network data associated with a network that includes network devices interconnected by links, and receives parameters associated with determining a network plan for the network. The device generates candidate links for each potential network plan of multiple potential network plans for the network, based on the parameters and based on a criterion associated with generating the candidate links. The device generates candidate paths for each potential network plan based on the parameters, and selects a portion of the candidate links and a portion of the candidate paths. The device generates each potential network plan based on the portion of the candidate links and the portion of the candidate paths, and identifies a potential network plan, of the multiple potential network plans, that reduces resource usage associated with operating the network. The device causes the potential network plan to be implemented in the network.

A device monitors, for a software-defined networking wide area network (SD-WAN) deployment, a set of virtualized network services of the SD-WAN deployment, and applies a set of diagnostic tests to evaluate the set of virtualized network services. The device detects, based on monitoring the set of virtualized network services and in connection with applying the set of diagnostic tests, an event associated with a virtualized network service. The device analyzes, using an analytics model of SD-WAN operation, the event to identify an issue associated with the virtualized network service, and determines, based on the analytics model of SD-WAN operation, a recommendation relating to remediating the issue. The device generates an abstraction layer user interface to represent the set of virtualized network services and to convey the recommendation relating to remediating the issue, and implements, after providing the abstraction layer user interface, the recommendation to remediate the issue.

A method, performed by a server, of executing a software package in a wireless communication system includes: obtaining package usage information for each of a plurality of software packages with respect to user equipment's (UEs) accessing a plurality of base stations (BSs) connected to the server; determining a first hardware component (HC) set, including a plurality of HCs, for processing tasks operating in a first software package from among the plurality of software packages, based on the package usage information including a use for each of the plurality of HCs included in the first HC set and a respective usage amount for the plurality of HCs; generating a second software package that is allocated to the first HC set; migrating the tasks operating in the first software package to the second software package; and deleting the first software package.

Described herein are systems and methods for fault tolerance in a network cloud environment. In accordance with various embodiments, the present disclosure provides an improved fault tolerance solution, and improvement in the fault tolerance of systems, by way of failure prediction, or prediction of when an underlying infrastructure will fail, and using the predictions to counteract the failure by spinning up or otherwise providing new component pieces to compensate for the failure.

A failure effects estimating device includes an input unit that inputs a log and a traffic amount obtained from a communication system when an abnormality occurs, an estimating unit that estimates a failure effects amount in the communication system, on the basis of the log and the traffic amount, and an output unit that outputs the failure effects amount estimated by the estimating unit.

The disclosure relates to an electronic apparatus and a method of controlling the same. The electronic apparatus includes: a communication interface; and a processor configured to receive log data of a plurality of devices connected to a network through the communication interface, acquire operation time information of each of the devices from the received log data, calculate similarity of the operation time between the plurality of devices based on the acquired operation time information, and determine a device group including two or more devices with relatively high calculated similarity among the plurality of devices.

An online system receives time series data and analyzes the data for identifying trend changes or level shifts in the time series. The time series data may describe resource utilization of systems, for example, bandwidth of computer networks. The online system uses prediction models, for example, machine learning based prediction models using regression to predict data values for the time series. The online system determines error residue values based on difference between predicted data values and actual data values of the time series. The online system determines level change in the error residue to identify change-points representing trend changes or level-shifts in the original time-series. The online system takes corrective action based on the change-point information and the trend following the occurrence of the change-point, for example, sending alerts or instructions for causing reconfiguration of the systems such as the computer network.

The explanation engine has a set of modules cooperating with each other configured to evaluate layers in a hierarchical architecture of a machine-based reasoning process that uses machine learning. The set of modules cooperate to support an explanation of how the machine-based reasoning process arrived at its reported results of both a final/top level result as well as corresponding intermediate output results. A messaging module of the explanation engine can collect the top-level result as well as one or more intermediate output results from intermediate layers of the machine-based reasoning process. Multiple layers of reasoning are associated with terminology used in at least one of i) a problem to be solved and ii) a domain pertinent to the problem in order to communicate how the machine-based reasoning process came to its reported results in a communication.

A method for Over-The-Top (OTT) media service monitoring and testing including deploying multiple test agents throughout the network and orchestrating them together to create a single OTT media service delivery path with coherent measurement points. The method includes selecting and configuring all test agents forming an OTT media service delivery path in a network, wherein the test agents are deployed on devices in the network with pairs of agents in a client-server relationship; for each selected test agent, simulating a Hyper Text Transport Protocol (HTTP) Adaptive Streaming (HAS) media playback using a pair test agent as a HAS server; for each selected test agent, monitoring the HAS media playback via a Quality of Experience (QoE) model to collect metrics, influence factors, and QoE scores; and reporting collected metrics, influence factors and QoE scores, wherein the QoE scores are compared to isolate any performance degradation.

Methods and systems provide for early detection of failures in cables and/or physical layer devices (PHY) linked thereto, of a communications network in a vehicle. The methods and systems employ a computer trained model that analyzes changes in detected values obtained from a given vehicle within an operational time period, against a range of operational values, collected from different vehicles and/or the given vehicle, for the respective PHY parameter, to determine that the cable and/or the PHY linked thereto in the given vehicle may fail within a predetermined time period.