Oracle International Corporation

Oracle Lnternational Corporation

Frazier Spaeth LLC

Timothy Mark Frazier

Dustin R Garvey

Sampanna Shahaji Salunke

Sumathi Gopalakrishnan

Uri Shaft

Amit Ganesh

Shriram Krishnan

Prasad Ravuri

Brent Arthur Enck

Long Yang

Eric L Sutton

John Branson Bley

Neil Goodman

Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.

Artificial intelligence driven configuration management

Generating persistent multifaceted statistical distributions of event data associated with computing nodes is disclosed. From a data stream, events are identified that occur during a first time interval. Characteristics associated with the events are determined. Based on a primary characteristic, it is determined that an event corresponds to an event cluster. The event count for that cluster is incremented. It is determined that the characteristics correspond to an event descriptor of events in the cluster. Responsive to requests to view the event cluster, information about descriptors from the cluster are displayed indicating events having a particular event descriptor, or a summary of characteristics that distinguish the descriptor from other event descriptors.

Streaming method for the creation of multifaceted statistical distributions

Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.

Automatic behavior detection and characterization in software systems

Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.

Systems and methods for automatically detecting, summarizing, and responding to anomalies

Techniques for analyzing, understanding, and remediating differences in configurations among many software resources are described herein. Machine learning processes are applied to determine a small feature set of parameters from among the complete set of parameters configured for each software resource. The feature set of parameters is selected to optimally cluster configuration instances for each of the software resources. Once clustered based on the values of the feature set of parameters, a graph is generated for each cluster of configuration instances that depicts the differences among the configuration instances within the cluster. An interactive visualization tool renders the graph in a user interface, and a management tool allows changes to the graph and changes to the configuration of one or more software resources.

Method for automatically selecting configuration clustering parameters

Techniques are described herein for scalable clustering of target resources by parameter set. In some embodiments, a plurality of parameter sets of varying length are received, where a parameter set identifies attributes of a target resource. A plurality of signature vectors are generated based on the plurality of parameter sets such that the signature vectors have equal lengths. A signature vector may map to one or more parameter sets of the plurality of parameter sets. A plurality of clusters are generated based on the similarity between signature vectors. Operations may be performed on a target resource based on one or more nodes in the plurality of clusters.

Scalable artificial intelligence driven configuration management

A computer system for automatically generating and sending messages to individuals, entities, processes, or locations (objects) in response to events when specified conditions are met. The automated messaging system is capable of delivering generated messages via a plurality of message delivery mechanisms (e.g. e-mail, FAX, voice mail, textual page). The automated messaging system provides the ability to link objects in chains of relationships and to use these chains to determine which messages to generate, which objects are the recipient of generated messages, and via which message delivery mechanism generated messages are sent. The automated messaging system is useful in a variety of environments including asset management, electronic-commerce, and Internet-based securities trading, as well as other applications.

Growing community of inventors

Livermore, CA, United States of America

Timothy Mark Frazier