Supplying a resource to an entity from a resource actuator
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G05B-013/04
G06F-001/28
G06F-017/11
G06F-009/50
출원번호
US-0581990
(2009-10-20)
등록번호
US-8812166
(2014-08-19)
발명자
/ 주소
Wang, Zhikui
Bash, Cullen E.
Tolia, Niraj
Marwah, Manish
Ranganathan, Parthasarathy
Joshi, Shailesh N
Phelan, Andrew James
출원인 / 주소
Hewlett-Packard Development Company, L.P.
인용정보
피인용 횟수 :
0인용 특허 :
23
초록▼
In a method for supplying a resource to an entity from a resource actuator, a plurality of physics-based models pertaining to the resource actuator and the entity are developed, a condition detected at the entity is received, feedback control on a resource demand of the entity employed based upon th
In a method for supplying a resource to an entity from a resource actuator, a plurality of physics-based models pertaining to the resource actuator and the entity are developed, a condition detected at the entity is received, feedback control on a resource demand of the entity employed based upon the detected condition, feed forward control on the resource demand of the entity is employed based upon the detected condition and the plurality of physics-based models, a constraint optimization problem having an objective function and at least one constraint using the plurality of physics-based models is formulated, a solution to the constraint optimization problem is determined, in which the solution provides the actuator setting, and the resource actuator is set to the actuator setting to supply the entity with the resource from the resource actuator.
대표청구항▼
1. A method for supplying a resource to an entity from a plurality of resource actuators, said method comprising: developing, by a processor, a plurality of physics-based models pertaining to the resource actuators and the entity, wherein the entity has a resource demand;receiving conditions detecte
1. A method for supplying a resource to an entity from a plurality of resource actuators, said method comprising: developing, by a processor, a plurality of physics-based models pertaining to the resource actuators and the entity, wherein the entity has a resource demand;receiving conditions detected at the entity;performing, by the processor, feedback control to generate an output to modify a resource demand threshold of the entity at a first interval of time based upon a difference between a condition threshold and the detected conditions;performing, by the processor, feed forward control to generate an output to minimize the resource demand threshold of the entity at a second interval of time that is longer than the first interval of time based upon the detected conditions and the plurality of physics-based models;formulating, by the processor, a constraint optimization problem having an objective function and at least one constraint using the plurality of physics-based models, wherein the objective function computes at least a proportional quantity of a total power consumption level of the plurality of resource actuators and the at least one constraint comprises a minimum resource demand of the entity;solving the constraint optimization problem based on the feedback control output and the feed forward control output, wherein the solution provides resource actuator setting values to minimize a power consumed by the plurality of resource actuators; andsetting, by the processor, the plurality of resource actuators to the resource actuator setting values. 2. The method according to claim 1, wherein developing the plurality of physics-based models further comprises: developing a power model for the plurality of resource actuators that relates settings of the plurality of resource actuators to the power consumed by the plurality of resource actuators;developing an actuator performance model of the plurality of resource actuators that relates settings of the plurality of resource actuators to resource capacities that the plurality of resource actuators are to supply; anddeveloping an entity performance model of the entity that relates an environmental condition in the entity to resource provisioning by the plurality of resource actuators. 3. The method according to claim 2, wherein developing the plurality of physics-based models further comprises developing the plurality of physics-based models to pertain to the plurality of resource actuators and a plurality of entities, wherein developing the plurality of physics-based models further comprises:developing a resource capacity sharing model that captures the relation between a set of capacities for the plurality of resource actuators and resources supplied to the plurality of entities. 4. The method according to claim 3, wherein the power model is defined as: Pi=pi(Ai), wherein Pi is the power consumed by the ith resource actuator, Ai is the setting of the ith resource actuator, and pi is an algebraic function relating Pi to Ai, and wherein a total power consumption (P) of a plurality of resource actuators is defined as: P=ΣPi. 5. The method according to claim 4, further comprising: obtaining experimental data pertaining to a correlation of the settings of the plurality of resource actuators to power consumption levels of the plurality of resource actuators and fitting the power model to the experimental data to calculate the function pi. 6. The method according to claim 3, wherein the performance model of the resource actuator is defined as: Ci=ci(Ai), wherein Ci is the resource capacity of the ith resource actuator, Ai is the setting of the ith resource actuator, and ci is an algebraic function relating Ci to Ai, and wherein a total resource capacity (C) of a plurality of resource actuators is defined as: C=ΣCi. 7. The method according to claim 3, wherein the entity performance model is defined as: Tj=tj(Cj, Pj, Tamb,j, t), wherein Tj is the temperature of the jth entity, Cj is a resource supply to the jth component, Pj is a power consumed by the jth entity, Tamb,j is the ambient temperature around the jth entity, t is time, and tj is an algebraic function relating Tj to (Cj, Pj, Tamb,j, t). 8. The method according to claim 7, wherein the resource capacity sharing model is defined as: Cj=sj(Ci, i=1, 2, . . . , n), wherein Ci is the resource capacity of the ith resource actuator, and wherein sj is an algebraic function relating Cj to Ci. 9. A computer-implemented optimizer to determine optimal settings to allow a plurality of resource actuators to vary an environmental condition at an entity, said computer-implemented optimizer comprising: a processor; anda memory device on which is stored instructions to cause the processor to: receive data from a plurality of input sources;develop a plurality of physics-based models pertaining to the plurality of resource actuators and the entity, wherein the entity has a resource demand;perform feedback control to generate an output to modify a resource demand threshold of the entity at a first interval of time based upon a condition threshold and the received data;perform feed forward control to generate an output to minimize the resource demand threshold of the entity at a second interval of time that is longer than the first interval of time based upon the received data and the plurality of physics-based models;formulate a constraint optimization problem having an objective function and at least one constraint using the plurality of physics-based models, wherein the objective function computes at least a proportional quantity of a total power consumption level of the plurality of resource actuators and the at least one constraint comprises a minimum resource demand of the entity;solve the constraint optimization problem based on the feedback control output and the feed forward control output, wherein the solution provides resource actuator setting values for the plurality of resource actuators to minimize power consumed by the plurality of resource actuators; andset the plurality of resource actuators to the resource actuator setting values to supply the entity with the resource from the resource actuator. 10. The computer-implemented optimizer according to claim 9, wherein the instructions are further to cause the processor to: develop a power model for the plurality of resource actuators to relate settings of the plurality of resource actuators to power consumed by the plurality of resource actuators;develop an actuator performance model of the plurality of resource actuators, the actuator performance model to relate settings of the plurality of resource actuators to resource capacities that the plurality of resource actuators are to supply; anddevelop an entity performance model of the entity that relates an environmental condition in the entity to resource provisioning by the plurality of resource actuators. 11. The computer-implemented optimizer according to claim 10, wherein the instructions are further to cause the processor to: develop the plurality of physics-based models pertaining to the plurality of resource actuators and a plurality of entities,wherein the plurality of physics-based models are further to develop a resource capacity sharing model that captures the relation between a set of capacities for the plurality of resource actuators and resources supplied to the plurality of entities. 12. The computer-implemented optimizer according to claim 11, wherein the power model is defined as: Pi=pi(Ai), wherein Pi is the power consumed by the ith resource actuator, Ai is the setting of the ith resource actuator, and pi is an algebraic function relating Pi to Ai, and wherein a total power consumption (P) of the plurality of resource actuators is defined as: P=ΣPi. 13. The computer-implemented optimizer according to claim 12, wherein the instructions are further to cause the processor to: obtain experimental data pertaining to a correlation of the settings of the plurality of resource actuators to power consumption levels of the plurality of resource actuators; andfit the power model to the experimental data to calculate the function pi. 14. The computer-implemented optimizer according to claim 12, wherein the performance model of the resource actuator is defined as: Ci=ci(Ai), wherein Ci is the resource capacity of the ith resource actuator, Ai is the setting of the ith resource actuator, and ci is an algebraic function relating Ci to Ai, and wherein a total resource capacity (C) of the plurality of resource actuators is defined as: C=ΣCi. 15. The computer-implemented optimizer according to claim 12, wherein the entity performance model is defined as: Tj=tj(Cj, Pj, Tamb,j, t), wherein Tj is the temperature of the jth entity, Cj is a resource supply to the jth component, Pj is a power consumed by the jth entity, Tamb,j is the ambient temperature around the jth entity, t is time, and tj is an algebraic function relating Tj to (Cj, Pj, Tamb,j, t). 16. The computer-implemented optimizer according to claim 15, wherein the resource capacity sharing model is defined as: Cj=sj(Ci, i=1, 2, . . . , n), wherein Ci is the resource capacity of the ith resource actuator, and wherein sj is an algebraic function relating Cj to Ci. 17. A non-transitory computer readable storage medium on which is embedded one or more machine readable instructions, wherein said one or more machine readable instructions, when executed by a processor, implement a method of supplying a resource to an entity from a plurality of resource actuators, wherein the entity has a resource demand, and wherein the one or more instructions are to cause the processor to: receive a condition detected at the entity;perform feedback control to generate an output to modify a resource demand threshold of the entity at a first interval of time based upon the detected condition;perform feed forward control to generate an output to minimize the resource demand threshold of the entity at a second interval of time that is longer than the first interval of time based upon the detected condition and the plurality of physics-based models;formulate a constraint optimization problem having an objective function and at least one constraint using the plurality of physics-based models, wherein the objective function computes at least a proportional quantity of a total power consumption level of the plurality of resource actuators and the at least one constraint comprises a minimum resource demand of the entity;solve the constraint optimization problem based on the feedback control output and the feed forward control output, wherein the solution provides resource actuator setting values for the plurality of resource actuators to minimize a power consumption of the plurality of resource actuators; andset the plurality of resource actuators to the resource actuator setting values to supply the entity with the resource from the resource actuator. 18. The non-transitory computer readable storage medium according to claim 17, said one or more computer programs further comprising instructions to cause the processor to: generate a power model for the plurality of resource actuators that relates settings of the plurality of resource actuators to power consumed by the plurality of resource actuators;generate an actuator performance model of the plurality of resource actuators that relates settings of the plurality of resource actuators to resource capacities that the plurality of resource actuators are configured to supply; andgenerate an entity performance model of the entity that relates an environmental condition in the entity to resource provisioning by the plurality of resource actuators. 19. The non-transitory computer readable storage medium according to claim 17, said one or more computer programs further comprising instructions to cause the processor to: generate the plurality of physics-based models pertaining to the plurality of resource actuators and a plurality of entities, wherein to generate the plurality of physics-based models, the instructions are further to cause the processor to:generate a resource capacity sharing model that captures the relation between a set of capacities for the plurality of resource actuators and resources supplied to the plurality of entities.
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