Gauri Sudhir Shah Deceased11721 Southshore Ct, Cupertino, CA 95014

7587570, Sep 8, 2009

May 31, 2006

11/421071

Prasenjit Sarkar - San Jose CA, US
Gauri Sudhir Shah - San Jose CA, US

International Business Machines Corporation - Armonk NY

G06F 12/08

711170

A storage provisioning system generates a storage management framework comprising a resource model representing a set of storage devices for use by an application. The resource model comprises a set of data containers and at least one volume container such that the resource model provides storage to the application independent of a plurality of interfaces used by the set of storage devices. The volume container is a specialized data container that interfaces directly with the storage devices and represents a bottom of a storage stack comprising at least one data container and at least one volume container. The resource model comprises a rules module for governing the construction of the data containers and the volume container and association between the data containers and the volume container.

7685165, Mar 23, 2010

Apr 1, 2005

10/907427

Sandeep Gopisetty - Morgan Hill CA, US
Norman Jerome Pass - Sunnyvale CA, US
Ramani Routray - San Jose CA, US
Aameek Singh - Smyrna GA, US
Gauri Shah - Santa Clara CA, US
Sandeep Uttamchandani - San Jose CA, US
Kaladhar Voruganti - San Jose CA, US

International Business Machines Corporation - Armonk NY

G06F 12/00

707200, 7071041

A policy-based resource management system provides an infrastructure that uses high-level definitions of data-types along with desired policy rules to manage legacy data. In particular, relevant legacy data is identified as well as the characteristics of storage devices where the legacy data resides. The storage management system determines what if any of the legacy data is stored in a manner that is non-compliant with the policy rules and corrects any violations. It generates corrective action plans, and then uses existing data migration software to migrate the data. Accordingly, the legacy data may be managed in a policy-based manner without requiring typical storage management software.

7725441, May 25, 2010

Dec 10, 2007

11/953115

Srinivasan Balasubramanian - San Jose CA, US
Tushar Mohan - Waltham MA, US
Roberto C. Pineiro - Santa Cruz CA, US
Rohit Jain - New Delhi, IN
Ramani R. Routray - San Jose CA, US
Gauri Shah - Santa Clara CA, US
Akshat Verma - New Delhi, IN
Kaladhar Voruganti - San Jose CA, US

International Business Machines Corporation - Armonk NY

G06F 17/30

707675, 707686

Formulating an integrated disaster recovery (DR) plan based upon a plurality of DR requirements for an application by receiving a first set of inputs identifying one or more entity types for which the plan is to be formulated, such as an enterprise, one or more sites of the enterprise, the application, or a particular data type for the application. At least one data container representing a subset of data for an application is identified. A second set of inputs is received identifying at least one disaster type for which the plan is to be formulated. A third set of inputs is received identifying a DR requirement for the application as a category of DR Quality of Service (QoS) class to be applied to the disaster type. A composition model is generated specifying one or more respective DR QoS parameters as a function of a corresponding set of one or more QoS parameters representative of a replication technology solution. The replication technology solution encompasses a plurality of storage stack levels.

7945537, May 17, 2011

May 23, 2008

12/126487

Srinivasan Balasubramanian - San Jose CA, US
Tushar Mohan - Waltham MA, US
Roberto C. Pineiro - Santa Cruz CA, US
Rohit Jain - New Delhi, IN
Ramani R. Routray - San Jose CA, US
Gauri Shah - Santa Clara CA, US
Akshat Verma - New Delhi, IN
Kaladhar Voruganti - San Jose CA, US

International Business Machines Corporation - Armonk NY

G06F 17/30

707675, 707686

Formulating an integrated disaster recovery (DR) plan based upon a plurality of DR requirements for an application by receiving a first set of inputs identifying one or more entity types for which the plan is to be formulated, such as an enterprise, one or more sites of the enterprise, the application, or a particular data type for the application. At least one data container representing a subset of data for an application is identified. A second set of inputs is received identifying at least one disaster type for which the plan is to be formulated. A third set of inputs is received identifying a DR requirement for the application as a category of DR Quality of Service (QoS) class to be applied to the disaster type. A composition model is generated specifying one or more respective DR QoS parameters as a function of a corresponding set of one or more QoS parameters representative of a replication technology solution. The replication technology solution encompasses a plurality of storage stack levels.

8135821, Mar 13, 2012

Jan 16, 2007

11/623452

Dakshi Agrawal - Monsey NY, US
Seraphin B. Calo - Cortlandt Manor NY, US
Murthy Devarakonda - Peekskill NY, US
David A. George - Somers NY, US
Sandeep K. Gopisetty - Morgan Hill CA, US
Konstantinos Magoutis - New York NY, US
Ramani R. Routray - San Jose CA, US
Joseph D. Ryan - Coronado CA, US
Gauri Shah - Santa Clara CA, US
Chung-hao Tan - San Jose CA, US
Norbert G. Vogl - Mahopac NY, US
Kaladhar Voruganti - San Jose CA, US

International Business Machines Corporation - Armonk NY

G06F 15/173

709224, 709205, 709221, 709226

A method for validating that a networked system of modules conforms to at least one interoperability rule pertinent to interoperability of at least two of the modules includes the steps of obtaining the at least one interoperability rule, the at least one rule being encoded in computer-operable format; obtaining configuration information pertinent to at least one of the modules, the configuration information being encoded in computer-operable format; and comparing the at least one interoperability rule to the configuration information to validate whether the system conforms to the at least one interoperability rule.

2007001, Jan 11, 2007

Jul 9, 2005

11/177189

Murthy Devarakonda - Peekskill NY, US
David George - Somers NY, US
Sandeep Gopisetty - Morgan Hill CA, US
Konstantinos Magoutis - New York NY, US
Ramani Routray - San Jose CA, US
Gauri Shah - Santa Clara CA, US
Chung-Hao Tan - San Jose CA, US
Norbert Vogl - Mahopac NY, US
Kaladhar Voruganti - San Jose CA, US

G06F 17/00

707104100

A multi-plan generation system generates a multi-plan for a multi-layer storage area network. The system generates a predetermined number of plans while generating and retaining globally optimum plans. The system dynamically calculates and orders these plans according to optimization criteria and user requirements. The system comprises a planner for each of the layers in a storage area network such as a host planner, a storage planner, a network planner, a zone planner, etc. Each of the planners applies a variance optimization, a preview optimization, and a detail optimization and interacts with a policy repository to obtain policy guidance, enabling the system to prune the set of plans. A control module invokes the planners in a predetermined order.

2007018, Aug 9, 2007

Feb 8, 2006

11/349081

Mary Roth - San Jose CA, US
Gauri Shah - Santa Clara CA, US
Willi Urban - Gaeufelden, DE
Lingling Yan - San Jose CA, US

G06F 17/30

707006000

Mechanisms for searching XML repositories for semantically related schemas from a variety of structured metadata sources, including web services, XSD documents and relational tables, in databases and Internet applications. A search is formulated as a problem of computing a maximum matching in pairwise bipartite graphs formed from query and repository schemas. The edges of such a bipartite graph capture the semantic similarity between corresponding attributes of the schema based on their name and type semantics. Tight upper and lower bounds are also derived on the maximum matching that can be used for fast ranking of matchings whilst still maintaining specified levels of precision and recall. Schema indexing is performed by ‘attribute hashing’, in which matching schemas of a database are found by indexing using query attributes, performing lower bound computations for maximum matching and recording peaks in the resulting histogram of hits.

2008027, Oct 30, 2008

Apr 30, 2007

11/741859

Dakshi Agrawal - Monsey NY, US
Chatschik Bisdikian - Chappaqua NY, US
Seraphin Bernard Calo - Cortlandt Manor NY, US
Murthy V. Devarakonda - Peekskill NY, US
David Alson George - Somers NY, US
Sandeep K. Gopisetty - Morgan Hill CA, US
Konstantinos Magoutis - New York NY, US
Ramani Ranjan Routray - San jose CA, US
Gauri Shah - Santa Clara CA, US
Chung-hao Tan - San Jose CA, US
Norbert George Vogl - Mahopac NY, US
Kaladhar Voruganti - San Jose CA, US
Steve Richard White - New York NY, US

G06F 9/44

717102

Techniques for automatically designing an application-requirement based configuration for a distributed computing system are provided. One or more application-level templates are obtained, wherein the one or more templates are representative of one or more requirements associated with one or more applications. Using the one or more application-level templates, one or more logical flows are created. The one or more logical flows are then used to generate one or more system-level configuration plans of the distributed computing system.