Abstract: A system defines, creates, uses, and distributes information about other entities by collecting network information, recombining and synthesizing new network metrics from the collected network information, and then publishing one or more synthetic metrics datum describing the operation of the synthetic device, using a facading processing system. The technology herein has applications e.g., in the areas of network and computer systems management.

Abstract: Correlation of component information provided by diverse detection and data collection methods is used to discover additional components and identify and record relationships between these and still other components. Each detection and data collection method can be individually useful to discover components, and to provide at least some information about one or more discovered components to identify at least one relationship between the discovered components.

Abstract: Systems, software, and methods for managing networks of connected electronic devices are described. In one example, network management policy and network management applications are transferred automatically upon detection and identification of a new device, protocol or application on the network. In another example, information related to at least one aspect of the network is obtained by an NMAS, and at least one applicable management policy is identified by the NMAS; and the identified policy is used to manage at least one aspect of the network's operation.

Abstract: Correlation of component information provided by diverse detection and data collection methods is used to discover additional components and identify and record relationships between these and still other components. Each detection and data collection method can be individually useful to discover components, and to provide at least some information about one or more discovered components to identify at least one relationship between the discovered components.

Abstract: Correlation of component information provided by diverse detection and data collection methods is used to discover additional components and identify and record relationships between these and still other components. Each detection and data collection method can be individually useful to discover components, and to provide at least some information about one or more discovered components to identify at least one relationship between the discovered components.

Abstract: A system defines, creates, uses, and distributes information about other entities by collecting network information, recombining and synthesizing new network metrics from the collected network information, and then publishing one or more synthetic metrics datum describing the operation of the synthetic device, using a facading processing system. The technology herein has applications e.g., in the areas of network and computer systems management.

Abstract: A system defines, creates, uses, and distributes information about other entities by collecting network information, recombining and synthesizing new network metrics from the collected network information, and then publishing one or more synthetic metrics datum describing the operation of the synthetic device, using a facading processing system. The technology herein has applications e.g., in the areas of network and computer systems management.

Abstract: In a network discovery and management system, a machine learning (ML) DLAD processor trains, validates, updates, and stores machine learning models. A ML training data preparation program performs operations to process and format input data to generate ML training data that can be used to train ML models. ML training program uses the ML training data to train ML models, thereby generating trained ML models. The ML training program can re-train or update the training of ML models as the system collects additional data and produces additional estimates, predictions, and forecasts. ML model validation program performs validation testing on trained ML models to generate one or more metrics that can indicate accuracy of predictions generated by the trained models. The resulting ML model(s) can be used to manage the network including but not limited to retrieve, instantiate and execute dynamic applications based on predictions made based on the models.

Abstract: In a network discovery and management system, a machine learning (ML) DLAD processor trains, validates, updates, and stores machine learning models. A ML training data preparation program performs operations to process and format input data to generate ML training data that can be used to train ML models. ML training program uses the ML training data to train ML models, thereby generating trained ML models. The ML training program can re-train or update the training of ML models as the system collects additional data and produces additional estimates, predictions, and forecasts. ML model validation program performs validation testing on trained ML models to generate one or more metrics that can indicate accuracy of predictions generated by the trained models. The resulting ML model(s) can be used to manage the network including but not limited to retrieve, instantiate and execute dynamic applications based on predictions made based on the models.

Abstract: Correlation of component information provided by diverse detection and data collection methods is used to discover additional components and identify and record relationships between these and still other components. Each detection and data collection method can be individually useful to discover components, and to provide at least some information about one or more discovered components to identify at least one relationship between the discovered components.

Abstract: Correlation of component information provided by diverse detection and data collection methods is used to discover additional components and identify and record relationships between these and still other components. Each detection and data collection method can be individually useful to discover components, and to provide at least some information about one or more discovered components to identify at least one relationship between the discovered components.

Abstract: A system defines, creates, uses, and distributes information about other entities by collecting network information, recombining and synthesizing new network metrics from the collected network information, and then publishing one or more synthetic metrics datum describing the operation of the synthetic device, using a facading processing system. The technology herein has applications e.g., in the areas of network and computer systems management.

Abstract: A system defines, creates, uses, and distributes information about other entities by collecting network information, recombining and synthesizing new network metrics from the collected network information, and then publishing one or more synthetic metrics datum describing the operation of the synthetic device, using a facading processing system. The technology herein has applications e.g., in the areas of network and computer systems management.

Abstract: A system defines, creates, uses, and distributes information about other entities by collecting network information, recombining and synthesizing new network metrics from the collected network information, and then publishing one or more synthetic metrics datum describing the operation of the synthetic device, using a facading processing system. The technology herein has applications e.g., in the areas of network and computer systems management.

Abstract: In a network discovery and management system, a machine learning (ML) DLAD processor trains, validates, updates, and stores machine learning models. A ML training data preparation program performs operations to process and format input data to generate ML training data that can be used to train ML models. ML training program uses the ML training data to train ML models, thereby generating trained ML models. The ML training program can re-train or update the training of ML models as the system collects additional data and produces additional estimates, predictions, and forecasts. ML model validation program performs validation testing on trained ML models to generate one or more metrics that can indicate accuracy of predictions generated by the trained models. The resulting ML model(s) can be used to manage the network including but not limited to retrieve, instantiate and execute dynamic applications based on predictions made based on the models.

Abstract: Correlation of component information provided by diverse detection and data collection methods is used to discover additional components and identify and record relationships between these and still other components. Each detection and data collection method can be individually useful to discover components, and to provide at least some information about one or more discovered components to identify at least one relationship between the discovered components.

Abstract: Correlation of component information provided by diverse detection and data collection methods is used to discover additional components and identify and record relationships between these and still other components. Each detection and data collection method can be individually useful to discover components, and to provide at least some information about one or more discovered components to identify at least one relationship between the discovered components.

Abstract: Systems, software, and methods for managing networks of connected electronic devices are described. In one example, network management policy and network management applications are transferred automatically upon detection and identification of a new device, protocol or application on the network. In another example, information related to at least one aspect of the network is obtained by an NMAS, and at least one applicable management policy is identified by the NMAS; and the identified policy is used to manage at least one aspect of the network's operation.

Abstract: Systems, software, and methods for managing networks of connected electronic devices are described. In one example, network management policy and network management applications are transferred automatically upon detection and identification of a new device, protocol or application on the network. In another example, information related to at least one aspect of the network is obtained by an NMAS, and at least one applicable management policy is identified by the NMAS; and the identified policy is used to manage at least one aspect of the network's operation.

Abstract: Systems, software, and methods for managing networks of connected electronic devices are described. In one example, network management policy and network management applications are transferred automatically upon detection and identification of a new device, protocol or application on the network. In another example, information related to at least one aspect of the network is obtained by an NMAS, and at least one applicable management policy is identified by the NMAS; and the identified policy is used to manage at least one aspect of the network's operation.