Methods of achieving cognizant power management

• US 10,289,184 B2
• Filed: 10/07/2015
• Issued: 05/14/2019
• Est. Priority Date: 03/07/2008
• Status: Active Grant

• Current Assignee: Raritan Americas Incorporated (Legrand SA)
• Original Assignee: Sunbird Software, Inc.
• Inventors: Malik, Naim R., Paetz, Christian, Weinstock, Neil, Yang, Allen, Onyshkevych, Vsevolod, Somasundaram, Siva
• Primary Examiner(s): Elamin, Abdelmoniem I
• Application Number:
  US14/877,621
• Publication Number:
  US 20160252946A1
• Time in Patent Office: 1,315 Days
• CPC Class Codes: G06F 1/26 Power supply means, e.g. regulation thereof for memories G11C
• CPC Class Codes: G06F 1/3203 Power management, i.e. event-based initiation of a power-saving mode
• CPC Class Codes: G06F 1/3209 Monitoring remote activity, e.g. over telephone lines or network connections
• CPC Class Codes: G06F 1/324 by lowering clock frequency
• CPC Class Codes: G06F 1/3296 by lowering the supply or operating voltage
• CPC Class Codes: G06F 9/5094 where the allocation takes into account power or heat criteria power management in computers in general G06F1/3203; thermal management in computers in general G06F1/206
• CPC Class Codes: Y02D 10/00 Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

A system and method of increasing the efficiency of overall power utilization in data centers by integrating a power management approach based on a comprehensive, dynamic model of the data center created with integrated environmental and computational power monitoring to correlate power usage with different configurations of business services utilization, with the techniques of CPU level power management.

Claims

1. A method for cognizant power management in a distributed computing system including a computing device, servers, power supply units and cooling units, comprising the steps of:

gathering, by the computing device, values for electrical power metrics from the servers and power supply units, the electrical power metrics characterizing electrical power usage in the computing environment;

gathering, by the computing device, values for computational power metrics from operating systems and applications executed within the servers, the computational power metrics characterizing a distribution of a computational load among the servers;

gathering, by the computing device, values for environmental metrics from environmental monitoring devices in the computing environment, the environmental metrics characterizing environmental conditions in the computing environment;

generating, by the computing device, a predictive behavior power utilization model based on the values of the electrical power metrics, the computational power metrics and the environmental metrics for the computing system, the predictive behavior power utilization model effective to predict values of electrical power metrics and environmental metrics as a function of the values of the computational power metrics;

distributing, by the computing device, an application load to the distributed computing system;

gathering, by the computing device, values for the computational power metrics associated with the application load;

applying, by the computing device, the predictive behavior power utilization model to predict values for the electrical power metrics and the environmental metrics under the application load;

gathering, by the computing device, values of the actual electrical power metrics and the actual environmental metrics under the application load;

updating, by the computing device, the predictive behavior power utilization model when the predicted values for the electrical power metrics and the environmental conditions fall outside defined tolerance limits for the actual electrical power metrics and environmental conditions; and

reallocating, by the computing device, the application load by executing an optimization step whereby the application load is reallocated to minimize a value of total electrical power utilization by the servers, power supply units and cooling units as determined by values of the electrical power metrics and environmental metrics predicted by the updated dynamic model as a function of computational power metrics associated with the reallocated application load.

2. The method of claim 1, wherein the servers include at least one of a plurality of central processing units, a plurality of functional units within central processing units, and a plurality of hypervisors managing virtual machines.

3. The method of claim 2, further comprising the step of:

setting a state in at least one of the servers based on the predictive behavior power utilization model before carrying out the distributing step, wherein the state is one of a plurality of performance states.

4. The method of claim 3, wherein setting one of a plurality of performance states includes changing the frequency of a processing unit.

5. The method of claim 3, wherein setting one of a plurality of performance states includes changing the voltage of a processing unit.

6. The method of claim 3, wherein setting one of a plurality of performance states includes changing the frequency and the voltage of a processing unit.

7. The method of claim 3, wherein one of a plurality of performance states includes at least a first performance state and a second performance state, wherein the first performance state can bear a greater computational load than the second state.

8. The method of claim 3, wherein the state is one of a plurality of sleep states.

9. The method of claim 8, wherein the plurality of sleep states includes at least a first sleep state and second sleep state, wherein the first sleep state has a lower wake up time than the second sleep state.