Sciencelogic, Inc.

Matthew Luebke

Karl L Ginter

Michael Bartman

Richard M Chart

Christopher G Cordray

David F Link

Edward Adams Robie, Jr

Jeremy Sherwood

Shankar Ananthanarayanan

Nicole Eickhoff

Tim Herrmann

Mathew Maloney

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. When the information and component discoveries of a first detection and data collection method are properly combined with those of a second detection and data collection method, there can be a synergistic effect that greatly improves the utility and efficiency of a relationship-based network management system by enabling automated discovery of additional components and identification of additional component relationships, by automatically merging diverse views of a single component into a coherent picture of that component, and by automated prediction of component state changes based on state changes in related components.

Component detection and management using relationships

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.

Distributed learning anomaly detector

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.

System and method for the collection, generation, and distribution of synthetic metrics for computer system management

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.

Dynamically deployable self configuring distributed network management system

Network management method using specification authorizing network task management software to operate on specified task management hardware computing components

Dynamically deployable self configuring distributed network management system using a trust domain specification to authorize execution of network collection software on hardware components

Dynamically deployable self configuring distributed network management system with specification defining trust domain membership and/or privileges and data management computing component

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Front Royal, VA, United States of America

Matthew Luebke