Charlton A Port

US Patent:

20140068627, Mar 6, 2014

Filed:

Jun 29, 2013

Appl. No.:

13/931819

Inventors:

Christian Tanasescu - Ottobrunn, DE
George L. Thomas - Sandy UT, US
CHarlton Port - Sandy UT, US

Assignee:

Silicon Graphics International Corp. - Fremont CA

International Classification:

G06F 9/50

US Classification:

718105

Abstract:

Embodiments of the invention relate to a system and method for dynamically scheduling resources using policies to self-optimize resource workloads in a data center. The object of the invention is to allocate resources in the data center dynamically corresponding to a set of policies that are configured by an administrator. Operational parametrics that correlate to the cost of ownership of the data center are monitored and compared to the set of policies configured by the administrator. When the operational parametrics approach or exceed levels that correspond to the set of policies, workloads in the data center are adjusted with the goal of minimizing the cost of ownership of the data center. Such parametrics include yet are not limited to those that relate to resiliency, power balancing, power consumption, power management, error rate, maintenance, and performance.

US Patent:

20160335131, Nov 17, 2016

Filed:

Jul 25, 2016

Appl. No.:

15/219030

Inventors:

- MILPITAS CA, US
Christian Tanasescu - Ottobrunn, DE
George L. Thomas - Sandy UT, US
Charlton Port - Sandy UT, US

International Classification:

G06F 9/50
G06F 1/20
G06F 1/32
G06F 9/48
G06F 11/30

Abstract:

Embodiments of the invention relate to a system and method for dynamically scheduling resources using policies to self-optimize resource workloads in a data center. The object of the invention is to allocate resources in the data center dynamically corresponding to a set of policies that are configured by an administrator. Operational parametrics that correlate to the cost of ownership of the data center are monitored and compared to the set of policies configured by the administrator. When the operational parametrics approach or exceed levels that correspond to the set of policies, workloads in the data center are adjusted with the goal of minimizing the cost of ownership of the data center. Such parametrics include yet are not limited to those that relate to resiliency, power balancing, power consumption, power management, error rate, maintenance, and performance.