Data Center Total Resource Utilization Efficiency (TRUE) System And Method

Abstract

Embodiments disclosed include methods and systems that adaptively, in real-time, evaluate data center performance, assess data center efficiency, data center sustainability, data center availability, compute performance, storage performance and provide data center customers with an overall data center performance rating, presented as a Total Resource Utilization Efficiency or TRUE score. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. Other embodiments of the methods or systems include addition of newly defined metrics as categories or sub-categories to be used to calculate data center TRUE score.

I/We claim: 
     
         1 . A computer automated system configured to:
 adaptively, in real-time, determine a Total Resource Utilization Efficiency (TRUE) of a data center facility;   based on the determined Total Resource Utilization Efficiency (TRUE), calibrate at least one of a power unit, a water unit, a compute system, a storage system, a power management system and an operating condition of the data center facility; and   determine an environmental impact based on a plurality of environmental impact variables comprising at least one of a greenhouse gas (CHG) intensity, a Particle Matter Intensity, and an SO2/NOX intensity.   
     
     
         2 . The computer automated system of  claim 1  wherein determining the Total Resource Utilization Efficiency (TRUE) further comprises:
 determining the operational availability of the data center facility over a finite time period; and 
 determining the environmental impact during the finite time period; and 
 determining the environmental impact based on the plurality of environmental variables further comprising a carbon intensity. 
 
     
     
         3 . The computer automated system of  claim 1  wherein determining the Total Resource Utilization Efficiency further comprises:
 optimizing the power usage efficiency or power usage effectiveness (PUE) which comprises: 
 determining a total power requirement of the facility; 
 determining a total power requirement of the input-output system, the compute system and the storage system comprised in the facility; 
 determining a total area occupied by the facility; 
 aggregating the total power requirement of the input-output system, the compute system and the storage system; and 
 determining and displaying a result comprising the total facility power requirement divided by the aggregated total power requirement of the input output system, the compute system and the storage system and further multiplied by the reciprocal of the determined total area occupied by the facility. 
 
     
     
         4 . The computer automated system of  claim 1  wherein:
 in determining the environmental impact the computer automated system is further configured to: 
 aggregate the plurality of environmental impact variables comprising greenhouse gas (GHG) intensity, a Carbon Intensity, Particle Matter Intensity, S 02 /NOX intensity, based on a number of units produced per megawatt hour (MWh); and 
 determine a water treatment chemical intensity, based on the number of chemicals used annually at the data center for water treatment, calculated using a number of liters of chemicals used for water treatment per kilowatt hour (kWh) or L/kWh. 
 
     
     
         5 . The computer automated system of  claim 1 , wherein in calibrating the water unit, the computer automated system is configured to:
 determine a water usage effectiveness (WUE) which further comprises determining a quantum of water consumed per kilowatt hour (kWh) over a finite time period.   
     
     
         6 . The computer automated system of  claim 1  wherein:
 based on the determined Total Resource Utilization Efficiency (TRUE), calibrate the compute system to operate at a load that allows maximum energy efficiency; and 
 wherein the calibrating is based on a pre-defined data management policy comprising determining data to retain and a retention period required for the retained data, and accordingly determining data to migrate to another compute system; and 
 wherein in calibrating the compute system, the computer automated system is further configured to optimize compute performance index which comprises a benchmark performance index multiplied by an average utilization per watt, the result of which is multiplied by 100. 
 
     
     
         7 . The computer automated system of  claim 1  wherein in calibrating the storage system the computer automated system is further configured to:
 optimize storage performance index which comprises a benchmark performance index multiplied by an average utilization per watt, the result of which is multiplied by  100 . 
 
     
     
         8 . The computer automated system of  claim 1  wherein in determining the data center operational availability, the computer automated system is further configured to measure and quantify the total annual uptime of the data center. 
     
     
         9 . The computer automated system of  claim 1  wherein:
 based on the determined real-time operating condition of the compute system, the storage system, and the power management system housed in the facility, the computer automated system is further configured to determine an air intake requirement and a humidity requirement, and based on pre-defined criteria, raise or lower the air intake and humidity according to the determined requirement. 
 
     
     
         10 . The computer automated system of  claim 1  wherein based on the determined real-time operating condition, the system is further configured to:
 minimize the power requirement, which minimization comprises automatic activation and deactivation of lighting equipment. 
 
     
     
         11 . The computer automated system of  claim 1  wherein based on the determined real-time operating condition the system is further configured to:
 predictively determine a heat generated based on an operating load, and accordingly control a re-configurable cooling equipment in the facility; and 
 wherein the reconfigurable cooling equipment is configured to automatically scale up or down according to the operating load. 
 
     
     
         12 . The computer automated system of  claim 1  wherein the system is further configured to:
 in a virtual reality interface, facilitate inspection, calibration and control of the power unit, the water unit, the compute system, the storage system, and the facility. 
 
     
     
         13 . The computer automated system of  claim 1  wherein:
 based on the determined real-time operating condition of the compute system, the storage system, and the power management system housed in the facility, the computer automated system is further configured to, based on a determined power requirement, deploy a modular uninterrupted power source supply, configured to optimize efficiency at partial or complete loads. 
 
     
     
         14 . A computer implemented method comprising:
 adaptively, in real-time, determining a Total Resource Utilization Efficiency (TRUE) of a data center facility;   based on the determined Total Resource Utilization Efficiency (TRUE), calibrating at least one of a power unit, a water unit, a compute system, a storage system, a power management system and an operating condition of the data center facility; and   determining an environmental impact based on a plurality of environmental impact variables comprising at least one of a greenhouse gas (CHG) intensity, a Particle Matter Intensity and an SO2/NOX intensity.   
     
     
         15 . The computer implemented method of  claim 14  wherein determining the Total Resource Utilization Efficiency (TRUE) further comprises:
 determining the operational availability of the data center facility over a finite time period; and 
 determining the environmental impact during the finite time period; and 
 determining the environmental impact based on the plurality of environmental variables further comprising a carbon intensity. 
 
     
     
         16 . The computer implemented method of  claim 14  further comprising:
 in determining the Total Resource Utilization efficiency, optimizing the power unit efficiency or power usage effectiveness (PUE) which comprises: 
 determining a total power requirement of the facility; 
 determining a total power requirement of the input-output system, the compute system and the storage system comprised in the facility; 
 determining a total area occupied by the facility; 
 aggregating the total power requirement of the input-output system, the compute system and the storage system; and 
 determining and displaying a result comprising the total power requirement divided by the aggregated total power requirement of the input-output system, the compute system and the storage system and further multiplied by the reciprocal of the determined total area occupied by the facility. 
 
     
     
         17 . The computer implemented method of  claim 14 , wherein, in determining the environmental impact, the method comprises:
 aggregating the plurality of environmental impact variables comprising greenhouse gas (CHG) intensity, a Carbon Intensity, Particle Matter Intensity, SO2/NOX intensity, based on a number of units produced per megawatt hour (MWh); and   determining a water treatment chemical intensity, based on a number of units of chemicals used annually at the data center for water treatment, calculated using the number of units used for water treatment per megawatt hour (MWh).   
     
     
         18 . The computer implemented method of  claim 14  wherein in calibrating the water unit, the method comprises:
 determining a water usage effectiveness (WUE) which further comprises determining a quantum of water consumed per kilowatt hour (kWh) over a finite time period. 
 
     
     
         19 . The computer implemented method of  claim 14  wherein the method further comprises:
 calibrating the compute system to operate at a load that allows maximum energy efficiency; and 
 wherein the calibrating  3 s based on a pre-defined data management policy comprising determining data to retain and a retention period required for the retained data, and accordingly determining data to migrate to another compute system; and 
 therein in calibrating the compute system, optimizing a compute performance index which comprises a benchmark performance index multiplied by an average utilization per watt, the result of which is multiplied by 100. 
 
     
     
         20 . The computer implemented method of  claim 14  wherein in calibrating the storage system the method further comprises:
 optimizing a storage performance index which comprises a benchmark performance index multiplied by an average utilization per watt, the result of which is multiplied by 100. 
 
     
     
         21 . The computer implemented method of  claim 14  wherein in determining the data center operational availability, the method comprises measuring the total annual downtime of the data center. 
     
     
         22 . The computer implemented method of  claim 14  wherein:
 based on the determined real-time operating condition of the compute system, the storage system, and the power management system housed in the facility, the determining an air intake requirement and a humidity requirement, and based on pre-defined criteria, raising or lowering the air intake and humidity according to the determined requirement. 
 
     
     
         23 . The computer implemented method of  claim 14  wherein based on the determined real-time operating condition, the method comprises:
 minimizing the power requirement, which minimization comprises automatic activation and deactivation of lighting equipment. 
 
     
     
         24 . The computer implemented method of  claim 14  wherein the method comprises:
 predictively determining a heat generated based on an operating load, and accordingly controlling a re-configurable cooling equipment in the facility; and 
 wherein the reconfigurable cooling equipment is configured to automatically scale up or down according to the operating load. 
 
     
     
         25 . The computer implemented method of  claim 14  wherein the method further comprises:
 in a virtual reality interface, facilitating inspection, calibration and control of the power unit, the water unit, the compute system, the storage system and the facility. 
 
     
     
         26 . The computer implemented method of  claim 14  wherein:
 based on the determined real-time operating condition of the compute system, the storage system, and the power management system housed in the facility, and based on a determined power requirement, deploying a modular uninterrupted power source supply, configured to optimize. efficiency at partial or complete loads.