Abstract: Configuring a device. A method includes identifying that a device has been connected to a control network. The method further includes, as a result, identifying characteristics of the device, including identifying at least one control or sensor coupled to the device. The method further includes, as a result, identifying an existing configuration template that applies to the device as identified, including identified characteristics. The method further includes configuring the device using the configuration template.

Abstract: In one embodiment, a supervisory service for a network obtains a first set of user experience survey data from users of an online application whose traffic was rerouted by a predictive routing engine. The supervisory service obtains a second set of user experience survey data from users of the online application whose traffic was not rerouted by the predictive routing engine. The supervisory service generates, using the first set of user experience survey data and the second set of user experience survey data, a user experience prediction model to predict user experience scores for the online application. The supervisory service adjusts the predictive routing engine using predicted user experience scores from the user experience prediction model.

Abstract: A method may include receiving a data flow of an application directed to the destination in a software-defined network (SDN). The method may also include identifying a classification of the application. The method may additionally include identifying a set of performance thresholds associated with the classification of the application. The method may also include determining a current performance of the data flow of the application in the SDN. The method may also include generating a performance score for the application based on the set of performance thresholds and the current performance of the data flow of the application in the SDN. The method may further include causing the performance score for the application to be presented via an interface.

Abstract: A digitized Intellectual Capital (IC) system obtains code modules configured to detect one or more issues in a computing system. The IC system selects from the code modules to generate a first set of code modules based on a corresponding value metric. The corresponding value metric for each code module in the first set of code modules is higher than a predetermined threshold. The IC system also samples from the remainder of the code modules unselected for the first set of code modules to generate a second set of code modules. The IC system runs the first set of code modules and the second set of code modules to detect the one or more issues and updates the corresponding value metric for at least one code module.

Abstract: A private network system operates over a public network to provide an asymmetric service to members with information from other members and non-members. The system includes central servers and databases connected via the public network to client systems of public network users. The users may be members who communicate with a private protocol or non-members who communicate with the public protocol. A process for the private service includes communication of information between users via the private service servers. The private service server implements the private service, which includes a viral engine for network deployment. Features of the viral engine include genetic algorithms, data mining, personalization, frictionless service setup, user maximization, and member-controlled privacy. A sample setup process and contact update wizard that include several viral engine features are described.

Abstract: In accordance with an aspect of the present disclosure, there is provided a method for identifying a type of a vocoder. The method comprises acquiring identification target bitstreams encoded with a voice signal, acquiring, for each of a plurality of vocoders, a probability that each of the plurality of vocoders is related to the identification target bitstreams from the identification target bitstreams, acquiring waveforms for each decoder of each of the plurality of vocoders by inputting the identification target bitstreams to the each decoder of each of the plurality of vocoders, acquiring intelligibility values for each of the waveforms obtained for the each decoder of each of the plurality of vocoders from the waveforms, and determining the type of the vocoder related to the voice signal from the probability and the intelligibility values for each waveform.

Abstract: In one embodiment, a service receives telemetry data indicative of a plurality of performance metrics captured in a network. The service jointly trains, using the received telemetry data, a compression model and an inference model, the compression model being a first machine learning model trained to convert the telemetry data into a compressed representation of the telemetry data and the inference model being a second machine learning model trained to take the compressed representation of the telemetry data as input and apply a classification label to it. The service deploys the compression model to the network. The service receives compressed telemetry data generated by the compression model deployed to the network. The service uses the inference model to classify the compressed telemetry data generated by the compression model deployed to the network.

Abstract: A decentralised computer storage network is described comprising a plurality of hypervisors or nodes HV1, HV2, HVn. Each storage region in a node is controlled by an associated software controller 11, 12. Each node is configured to prepare a compressed metadata file detailing a snapshot of the data stored in its storage drive. The nodes can broadcast their metadata files so that they can be received by other nodes in the network. This allows each node to be aware of the data stored in the other nodes. By maintaining a local record of metadata files received over the network, each storage node can establish a live picture of the data stored in other storage nodes. This can permit any individual storage node to take decisions that control the distribution of data in the network.

Abstract: A method and a system for detecting anomalies that indicate potential system infrastructure problems is provided. The method includes: obtaining a first set of telemetry data that relates to a system infrastructure events, logs, and metrics that occur during a predetermined time interval; retrieving, from a memory, historical data that relates to system infrastructure telemetry; comparing the obtained first set of telemetry data with the retrieved historical data; detecting an anomaly based on a result of the comparing; and determining a potential system infrastructure problem based on the detected anomaly. The method may include using a machine learning algorithm to analyze the obtained first set of telemetry data; identify a pattern in the data; assign scores to the events included in the data; and use the pattern and/or the scores to detect the anomaly using an artificial intelligence (AI) model that is trained using the retrieved historical data.

Abstract: The present disclosure describes a client-based web control system for analyzing and filtering web content received at a user device and presenting the filtered content on the user device in real-time, one or more operations and functions being efficiently achieved via this system comprise: receiving, at a user device, web content; identifying, at the user device, at least one model for classifying the web content; performing, at the user device, real-time analysis on the web content using the at least one model to classify the web content and determine a classification result; and taking an action on the web content at the user device based on the classification result. The present disclosure also describes the web control system including a cloud server, which, in conjunction with the user device, analyzes and classifies the web content.

Abstract: This disclosure describes techniques for employing a collaborate traffic balancer in communications among network devices. The techniques include dynamic traffic engineering concepts to improve network communications. The techniques may include causing a headend device to establish a secure communication session between a client device and a server in a resource infrastructure supporting the service. The techniques may include selecting a tunnel for the secure communication session to reach the resource infrastructure. The techniques may further include migrating the secure communication session from a current tunnel to a new tunnel where a degradation in quality of the secure communication session is predicted.

Abstract: In some implementations, a network monitoring system may measure a traffic metric for a first component of a network device. The network monitoring system may determine, based on a trained component monitoring model, that an obtained measurement of the traffic metric for the first component satisfies a criteria associated with the traffic metric pattern. The network monitoring system may compare, based on determining that the obtained measurement satisfies the criteria, the obtained measurement to a corresponding measurement of the traffic metric for a second component of the network device. The network monitoring system may determine that a measurement difference between the obtained measurement for the first component and the corresponding measurement for the second component satisfies a difference threshold. The network monitoring system may perform, based on the measurement difference satisfying the difference threshold, an action associated with the first component.

Abstract: A set of service-level reliability metrics and a method to allocate these metrics to the layers of the service delivery platform. These initial targets can be tuned during service design and delivery, and feed the vendor requirements process, forming the basis for measuring, tracking, and responding based on the service-level reliability metrics.

Abstract: One or more processing devices access a service definition for a service provided by one or more entities that each produce machine data or about which machine data is generated. The service definition identifies the entities that provide the service and, for each entity, definitional information includes information for identifying machine data pertaining to that entity. The processing devices access a key performance indicator (KPI) for the service that is defined by a search query that produces a value derived from the machine data pertaining to the entities identified in the service definition. The value indicates how the service is performing at a point in time or during a period of time and indicates a state of the KPI. A graphical interface is displayed and an indication of at least one threshold, which defines an end of a range of values representing a state of the KPI, for the KPI is received.

Abstract: A method comprising discovering workload attributes and identify dependencies, receiving utilization performance measurements including memory utilization measurements of at least a subset of workloads, grouping workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determining at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determining at least one cloud service provider (CSP)'s cloud services based on performance of the representative synthetic workloads, and generating a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP's cloud services inclu

Abstract: A network state tracking system tracks the state of a network and recommends an action when the state of the network changes. The network state tracking system detects one or more nodes on the network and generates one or more snapshots of the network. The snapshots are generated by obtaining first and second status data from the nodes and detecting whether a change in the network has occurred based on the status data. The snapshots are generated based on the first and second status data when a change in the network is detected. The snapshots are then used to train a machine learning model to recommend an action to take when a change in the network occurs.

Abstract: Various systems and methods of artificial intelligence (AI) processing using hardware acceleration within edge computing settings are described herein. In an example, processing performed at an edge computing device includes: obtaining a request for an AI operation using an AI model; identifying, based on the request, an AI hardware platform for execution of an instance of the AI model; and causing execution of the AI model instance using the AI hardware platform. Further operations to analyze input data, perform an inference operation with the AI model, and coordinate selection and operation of the hardware platform for execution of the AI model, is also described.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for identification of streaming activity and source for cached media on streaming devices.

Abstract: A system and method to monitor individual appliance performance. A monitor module is provided for aggregating data from a plurality of appliances wherein the module includes. Included in the monitor device is a gateway device configured to capture operational data relating to each of the plurality of appliances and an analyzing device configured to determine individual operational characteristics for each of the appliances from analysis of the captured operational data. A notification device is further provided which is configured to provide information to a user regarding the determined operational characteristics for each of the appliances.

Abstract: Techniques to generate network simulation scenarios are described. In one embodiment, an apparatus may comprise a records component operative to receive an example network configuration record; receive an example network operation record; a machine learning management component operative to generate a network operation model using a machine learning component based on the example network configuration record as an example input and the example network operation record as an example output; and a system-test component operative to receive a system-test network configuration record; and generate a system-test network operation record based on the system-test network configuration record using the network operation model. Other embodiments are described and claimed.