Shriram Te Krishnan

7657615, Feb 2, 2010

Dec 8, 2003

10/731889

Martin Patterson - Menlo Park CA, US
Jayaraman Manni - Santa Clara CA, US
Shriram Krishnan - Oakland CA, US
Benjamin H. Stoltz - Mountain View CA, US
Christopher T. La - Union City CA, US

Sun Microsystems, Inc. - Santa Clara CA

G06F 15/177

709221

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.

8335206, Dec 18, 2012

Mar 4, 2010

12/717196

Olaf Hirsch - Sunnyvale CA, US
Bharath Bhoopalam - San Jose CA, US
Shriram Krishnan - Sunnyvale CA, US
Manev Luthra - Santa Clara CA, US
Peter Van Horn - San Jose CA, US

Qualcomm Atheros, Inc. - San Jose CA

H04J 3/06

370350, 370349

A common coexistence timer can be implemented on collocated or proximate wireless radio devices so that timing information associated with scheduled communications of each of the wireless radio devices is communicated with a common time reference. The coexistence timer provides the common time reference for the wireless radio devices. A transmitting wireless radio device can calculate a difference time associated with the timing information of a scheduled communication and can provide the difference time to a receiving wireless radio device. The receiving wireless radio device can convert the difference time into a time format corresponding to the receiving wireless radio device's local time reference. This can minimize interference between the collocated or proximate wireless radio devices, thus minimizing performance degradation, packet collision, and interference. Moreover, this also can eliminate timing errors due to the wireless radio devices using different timers operating at different frequencies or being synchronized to different crystals.

20130058316, Mar 7, 2013

Nov 2, 2012

13/667913

Bharath Bhoopalam - San Jose CA, US
Shriram Krishnan - Sunnyvale CA, US
Manev Luthra - Santa Clara CA, US
Peter Van Horn - San Jose CA, US

QUALCOMM INCORPORATED - San Diego CA

H04W 72/12

370336

A common coexistence timer can be implemented on collocated or proximate wireless radio devices so that timing information associated with scheduled communications of each of the wireless radio devices is communicated with a common time reference. The coexistence timer provides the common time reference for the wireless radio devices. A transmitting wireless radio device can calculate a difference time associated with the timing information of a scheduled communication and can provide the difference time to a receiving wireless radio device. The receiving wireless radio device can convert the difference time into a time format corresponding to the receiving wireless radio device's local time reference. This can minimize interference between the collocated or proximate wireless radio devices, thus minimizing performance degradation, packet collision, and interference. Moreover, this also can eliminate timing errors due to the wireless radio devices using different timers operating at different frequencies or being synchronized to different crystals.

7237077, Jun 26, 2007

Dec 8, 2003

10/731681

Martin Patterson - Menlo Park CA, US
Shriram Krishnan - Oakland CA, US
Jayaraman Manni - Santa Clara CA, US
Benjamin H. Stoltz - Mountain View CA, US

Sun Microsystems, Inc. - Santa Clara CA

G06F 12/00

711162, 711152

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.

20210286611, Sep 16, 2021

May 27, 2021

17/332649

- Redwood Shores CA, US
Amit Ganesh - San Jose CA, US
Uri Shaft - Fremont CA, US
Prasad Ravuri - San Jose CA, US
Long Yang - Redwood City CA, US
Sampanna Shahaji Salunke - Dublin CA, US
Sumathi Gopalakrishnan - Fremont CA, US
Timothy Mark Frazier - Livermore CA, US
Shriram Krishnan - Oakland CA, US

Oracle International Corporation - Redwood Shores CA

G06F 8/65
G06N 20/00
G06F 8/60
G06F 16/906
G06F 8/61
G06F 9/50

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.

20210132993, May 6, 2021

Oct 31, 2019

16/670072

- Redwood Shores CA, US
Prasad Ravuri - San Jose CA, US
Karl Dias - Foster City CA, US
Gayatri Tripathi - Bengaluru, IN
Shriram Krishnan - Oakland CA, US
Chaitra Jayaram - Pleasanton CA, US

Oracle International Corporation - Redwood Shores CA

G06F 9/50
G06F 9/54
G06F 9/32

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.

20200249931, Aug 6, 2020

Apr 21, 2020

16/854635

- Redwood Shores CA, US
Amit Ganesh - San Jose CA, US
Uri Shaft - Fremont CA, US
Prasad Ravuri - San Jose CA, US
Long Yang - Redwood City CA, US
Sampanna Shahaji Salunke - Dublin CA, US
Sumathi Gopalakrishnan - Fremont CA, US
Timothy Mark Frazier - Livermore CA, US
Shriram Krishnan - Oakland CA, US

Oracle International Corporation - Redwood Shores CA

G06F 8/65
G06F 9/50
G06F 8/61
G06F 16/906
G06F 8/60
G06N 20/00

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.

20190361693, Nov 28, 2019

Aug 7, 2019

16/534896

- Redwood Shores CA, US
Timothy Mark Frazier - Livermore CA, US
Shriram Krishnan - Oakland CA, US
Uri Shaft - Fremont CA, US
Amit Ganesh - San Jose CA, US
Prasad Ravuri - San Jose CA, US
Sampanna Shahaji Salunke - Dublin CA, US
Sumathi Gopalakrishnan - Fremont CA, US

Oracle International Corporation - Redwood Shores CA

G06F 8/65
G06F 8/60
G06N 20/00
G06F 16/906
G06F 8/61
G06F 9/50

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.