Oracle International Corporation

Sun Microsystems, Inc.

Oracle Lnternational Corporation

Shriram Krishnan

Prasad Ravuri

Amit Ganesh

Dustin R Garvey

Uri Shaft

Sampanna Shahaji Salunke

Sumathi Gopalakrishnan

Timothy Mark Frazier

Long Yang

Martin Patterson

Benjamin H Stoltz

Jayaraman Manni

Karl Dias

Amol Achyut Chiplunkar

Gayatri Tripathi

Chaitra Jayaram

Christopher T La

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

The embodiments disclosed herein relate to predictive rate limiting. A workload for completing a request is predicted based on, for example, characteristics of a ruleset to be applied and characteristics of a target set upon which the ruleset is to be applied. The workload is mapped to a set of tokens or credits. If a requestor has sufficient tokens to cover the workload for the request, the request is processed. The request may be processed in accordance with a set of processing queues. Each processing queue is associated with a maximum per-tenant workload. A request may be added to a processing queue as long as adding the request does not result in exceeding the maximum per-tenant workload. Requests within a processing queue may be processed in a First In First Out (FIFO) order.

Rate limiting compliance assessments with multi-layer fair share scheduling

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

An approach for provisioning network devices generally involves supplying boot data to network devices over a network so that the network devices can be booted up in an imaging mode or an application mode, depending upon the particular boot data supplied to the network device. When booted up in the imaging mode, imaging data can be downloaded and stored on network devices. When booted up in the application mode, the network devices execute one or more programs contained in the image data stored on the network devices. The first and second boot data may be in the form of boot loader scripts. Furthermore, the first and second boot data may be provided to the network device in the payload of a dynamic host configuration protocol (DHCP) reply. The DHCP reply may be generated and provided by a DHCP server to the network device in response to receiving a DHCP request from the network device. The approach may be implemented using a secure network environment.

Approach for provisioning network devices

A method and apparatus for replicating an image from a source to a destination disk are provided. Specific embodiments may be optimized for single source to multiple destination replication requests, for example. In one embodiment, the present invention provides tools and techniques for synchronous data replication responsive to asynchronous same-source-to-different-destination replication requests.

Tool for disk image replication

Growing community of inventors

Oakland, CA, United States of America

Shriram Krishnan