Reece S Joyner, Age 53San Francisco, CA

7734781, Jun 8, 2010

Jul 9, 2002

10/192182

William Tracy Fuller - Cupertino CA, US
David Powers - Morgan Hill CA, US
Alan Nitteberg - Pleasanton CA, US
Michael David Durham - Fremont CA, US
Reece Joyner - San Mateo CA, US
Barry Van Hooser - Pleasanton CA, US
Lieu Thuy Thi Ta - San Jose CA, US

Savvis Communications Corporation - Town & Country MO

G06F 15/173

709226, 709225

In a shared storage infrastructure accessible by more than one customer, the shared storage infrastructure is separated into a plurality of logical storage areas. A customer is associated with at least one logical storage area within the shared storage infrastructure. The customer and its logical storage areas are associated with at least one port in the shared storage infrastructure. Each customer has access only to the logical storage areas associated with the customer and cannot access the logical storage areas of any other customer. The shared storage infrastructure provides policy based storage management to each customer, that is, each customer appears to have access to a non-shared storage subsystem. By providing a virtualization layer, a customer may be given the ability to manage its own storage needs.

2003000, Jan 9, 2003

Jun 14, 2002

10/173512

Christopher Eidler - Danville CA, US
William Fuller - Cupertino CA, US
Michael Hanly - San Jose CA, US
Steven Berman - San Jose CA, US
Reece Joyner - San Mateo CA, US
Barry Hooser - Pleasanton CA, US
Patrick Conroy - San Antonio TX, US

G06F007/00

707/001000

A secure shared storage infrastructure accessible by more than one customer is separated into a plurality of logical storage areas. A customer is associated with at least one logical storage area within the shared storage infrastructure. The customer and its logical storage areas are associated with at least one port in the shared storage infrastructure. Each customer has access only to the logical storage areas associated with the customer and cannot access the logical storage areas of any other customer.

2007019, Aug 23, 2007

Feb 9, 2007

11/704734

William Fuller - Cupertino CA, US
Michael Perka - Mountain View CA, US
Barry Van Hooser - Pleasanton CA, US
Heidi Schoolcraft - San Jose CA, US
Ray Villeneuve - Atherton CA, US
Gideon Senderov - Cupertino CA, US
Reece Joyner - San Francisco CA, US

G07G 1/00

705010000

A system and methods for capacity planning of electronic systems storage are provided. Features include a non-invasive data discovery and collection mechanism, an adaptive grouping mechanism, a flexible analysis mechanism, predictive modeling and forecasting mechanisms, and a business metric and correlation mechanism. A discovery engine that ascertains the availability of collectable entities. A collection engine in turn gathers information from and about entities discovered by the discovery engine. A grouping module groups together entities according to parameters specified by the user. An analysis and forecasting module allows a user to create, modify and save forecast scenarios from which a model is generated by a modeling module. Correlations can be identified between specified key business metrics and historical data. Once a model has been created, a forecast is generated and can then be analyzed using Bold Freehand Extrapolation, time-series analysis, and business trend-based forecasting.

2011025, Oct 13, 2011

Jun 10, 2011

13/158204

Ray M. Villeneuve - Atherton CA, US
Gideon J. Senderov - Cupertino CA, US
Barry S. Van Hooser - Pleasanton CA, US
Heidi M. Schoolcraft - San Jose CA, US
William T. Fuller - Cupertino CA, US
Michael F. Perka - Mountain View CA, US
Reece Joyner - San Francisco CA, US

G06F 15/173

709224

A system and methods for capacity planning of electronic systems storage are provided. Features include a non-invasive data discovery and collection mechanism, an adaptive grouping mechanism, a flexible analysis mechanism, predictive modeling and forecasting mechanisms, and a business metric and correlation mechanism. A discovery engine that ascertains the availability of collectable entities. A collection engine in turn gathers information from and about entities discovered by the discovery engine. A grouping module groups together entities according to parameters specified by the user. An analysis and forecasting module allows a user to create, modify and save forecast scenarios from which a model is generated by a modeling module. Correlations can be identified between specified key business metrics and historical data. Once a model has been created, a forecast is generated and can then be analyzed using Bold Freehand Extrapolation, time-series analysis, and business trend-based forecasting.