Data center intelligent control and optimization

• US 9,709,965 B2
• Filed: 03/07/2013
• Issued: 07/18/2017
• Est. Priority Date: 12/04/2008
• Status: Expired due to Fees

• Current Assignee: Baselayer Technology, LLC
• Original Assignee: Baselayer Technology, LLC
• Inventors: Slessman, George, Slessman, William, Malik, Kevin
• Primary Examiner(s): Swearingen, Jeffrey R
• Application Number:
  US13/788,834
• Publication Number:
  US 20130190899A1
• Time in Patent Office: 1,594 Days
• Field of Search: None
• CPC Class Codes: G05B 13/02 electric
• CPC Class Codes: G06F 1/206 comprising thermal management
• CPC Class Codes: G06F 1/3203 Power management, i.e. event-based initiation of a power-saving mode
• 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: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: H05K 7/1488 Cabinets therefor, e.g. chassis or racks or mechanical interfaces between blades and support structures
• CPC Class Codes: H05K 7/1498 Resource management, Optimisation arrangements, e.g. configuration, identification, tracking, physical location thermal management H05K7/20836
• CPC Class Codes: H05K 7/20745 within rooms for removing heat from cabinets, e.g. by air conditioning device
• CPC Class Codes: Y02D 10/00 Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

Systems and methods of monitoring, analyzing, optimizing and controlling data centers and data center operations are disclosed. The system includes data collection and storage hardware and software for harvesting operational data from data center assets and operations. Intelligent analysis and optimization software enables identification of optimization and/or control actions. Control software and hardware enables enacting a change in the operational state of data centers.

Claims

1. A data center control and optimization system, comprising:

a memory, the memory in communication with a processor, the processor when executing a computer program for data center control and optimization, performs operations comprising;

accessing, by the processor, input data associated with data center operations, wherein the input data comprises inventory data, operational data, historical data and external data;

identifying and analyzing the input data based on a plurality of optimization dimensions, the plurality of optimization dimensions including at least a redundancy optimization dimension and a latency optimization dimension;

selecting, by the processor, at least one data center optimization dimension from the plurality of optimization dimensions, the selected at least one data center optimization dimension including at least the redundancy optimization dimension,

forecasting, by the processor and based upon at least a portion of the input data, to produce predictive data associated with the selected redundancy optimization dimension;

identifying a plurality of optimization actions based on the input data and the predictive data associated with the selected redundancy optimization dimension;

determining, for each optimization action, a respective optimization score based upon the selected redundancy optimization dimension;

selecting, by the processor without operator interaction and based upon the selected redundancy optimization dimension, the predictive data, the input data, and the optimization scores, the optimization action; and

generating, by the processor without operator interaction, an optimization instruction based upon the selected redundancy optimization action and executing the optimization instruction to modify at least one asset of the data center.

2. The system of claim 1, wherein the plurality of optimization dimensions further comprises at least one of reliability, resiliency, risk, efficiency, cost and threat avoidance.

3. The system of claim 1, wherein the identifying comprises at least one of receiving input, obtaining from a data source, calculating and forecasting, and wherein the data source comprises at least one of a historical database, a customer relationship management database, a data warehouse, a product catalog, an accounting system, the internet, a government data source, a vendor database, a utility system, a power system, a security system, a network provider system and data collection points associated with the data center operations.

4. The system of claim 3, wherein at least a subset of the data collection points comprises at least one of a sensor, a detector, information technology (IT) equipment, an IT equipment interface, a data translator, a virtualization system interface, an application programming interface, and external system data.

5. The method of claim 4, wherein the sensor comprises at least one of a temperature sensor, a humidity sensor, a fire detector, a smoke detector, a high sensitivity smoke detector, a very early smoke detection apparatus, a moisture detector, a spot leak detector, a pressure sensor, an intrusion detector, a motion detector, a biometric sensor, an acoustic sensor, a valve position indicator, a power monitor, a fan speed and a fan direction.

6. The system of claim 1, further comprising analyzing a plurality of the data center assets based upon the selected optimization dimension.

7. The system of claim 6, further comprising analyzing the plurality of data center assets based upon at least one attribute of each data center asset, wherein the attribute comprises at least one of risk state, power capability, power usage effectiveness, physical space, cooling capability, networking capability, redundancy capability, and geographic proximity.

8. The system of claim 1, wherein the analyzing the input data comprises analyzing the input data to identify at least one of customer data, configuration data, product data, sales data, cost data, revenue data, and revenue by product data.

9. The system of claim 1, wherein the determining the optimization action comprises at least one of performing a proprietary algorithm, performing a simulation, performing artificial intelligence, performing a stochastic method and executing a linear programming model.

10. The system of claim 1, further comprising:

in response to the optimization instruction being executed, receiving updated operational data from data collection points associated with the data center operations; and

updating a historical database with the updated operational data.

11. The system of claim 1, further comprising:

detecting physical security information comprising at least one of biometric data, card swipe data and intrusion detection data, wherein the input data comprises the physical security information;

forecasting the predictive data by determining a physical security risk profile of an individual, wherein the individual is identified by the physical security information;

determining, based upon at least one of the physical security information and the physical security risk profile, the selected optimization dimension.

12. The system of claim 11, further comprising determining the at least one data center optimization action to additionally be threat avoidance, wherein the optimization instruction causes a door to be locked, wherein the door enables access to at least one of a data center, a data center module, a cabinet, a control panel and a sub-floor space.

13. The system of claim 1, further comprising determining the optimization action to include moving an application to a second location, wherein the optimization instruction causes the application to move from a first server in a first location to a second server in the second location.

14. The system of claim 13, further comprising determining the at least one data center optimization dimension to additionally be at least one of risk and reliability, wherein the optimization action is based upon the optimization dimension and at least one attribute associated with the second location, wherein the at least one attribute comprises at least one of risk state, power capability, physical space, cooling capability, networking capability, redundancy, and geographic proximity.

15. The system of claim 14, wherein the forecasting the predictive data comprises analyzing data collection point information, historical data and external data to predict the likelihood of at least one of a flood, a hurricane and an earthquake.

16. The system of claim 14, wherein the forecasting the predictive data comprises analyzing at least one of data collection point information, historical information, and external data to predict a network latency level.

17. The system of claim 14, wherein the forecasting the predictive data comprises analyzing at least one of data collection point information, historical information, and external data to create a power utilization prediction schedule for a plurality of data center assets associated with the data center operation, wherein the external data comprises at least one of power usage effectiveness data, utility usage information and utility cost information.

18. The system of claim 17, wherein the optimization instruction comprises a load balancing plan that at least one of minimizes energy consumption, minimizes energy costs and minimizes power utilization effectiveness ratio.

19. The system of claim 18, further comprising communicating the load balancing plan to a user via at least one of a graphical user interface and a notification message.

20. The system of claim 1, wherein the selecting the at least one data center optimization dimension further includes selecting the latency optimization dimension as the selected at least one optimization dimension,

wherein the accessing the input data includes accessing data center operations latency data from network monitors,

wherein the identifying the plurality of optimization actions includes ranking alternative data center assets based on a priority assignment,

wherein the selecting the optimization action includes selecting at least one of the alternative data center assets based on the ranking, and

wherein the generating and the executing of the optimization instruction includes dynamically transferring applications subject to latency to the selected at least one of the alternative data center assets.

21. The system of claim 1, wherein the plurality of optimization dimensions further includes an efficiency dimension, and wherein the selecting the at least one data center optimization dimension further includes selecting the efficiency dimension,

wherein the accessing the input data includes accessing data center operations power consumption data,

wherein the identifying the plurality of optimization actions includes evaluating alternative data center assets based on a power usage effectiveness for each of the alternative data center assets,

wherein the selecting the optimization action includes selecting at least one of the alternative data center assets based on the evaluation, and

wherein the generating and the executing of the optimization instruction includes dynamically transferring applications to the selected at least one of the alternative data center assets.

22. The system of claim 1,

wherein the accessing the input data includes accessing data center operations data for alternative data center assets, the data center operations data includes a redundancy rating for power and cooling capabilities for each of the alternative data center assets,

wherein the identifying the plurality of optimization actions includes evaluating the alternative data center assets based on the redundancy ratings;

wherein the selecting the optimization action includes selecting at least one of the alternative data center assets based on the evaluation, and

wherein the generating and the executing of the optimization instruction includes dynamically transferring applications to the selected at least one of the alternative data center assets.

23. A computerized method for controlling and optimizing data center operations, comprising:

accessing, by a data center infrastructure control system (“

DCICS”

), input data associated with data center operations, wherein the input data comprises inventory data, operational data, historical data and external data;

determining, by the DCICS, at least one data center optimization dimension,

wherein the determining includes selecting the at least one data center optimization dimension from a plurality of data center optimization dimensions that include at least a redundancy optimization dimension and a latency optimization dimension, the selected at least one data center optimization dimension including at least the redundancy optimization dimension;

forecasting, by the DCICS and based upon at least a portion of the input data, to produce predictive data;

identifying a plurality of optimization actions based on the input data and the predictive data;

determining, for each optimization action, a respective optimization score based upon the selected redundancy optimization dimension;

selecting, by the DCICS and based upon the selected redundancy optimization dimension, the predictive data, the input data, and the optimization scores, the optimization action;

generating, by the DCICS without operator interaction, an optimization instruction based upon the selected optimization action, wherein executing the optimization instruction causes to modify at least one asset of the data center as a change in an operational state; and

accessing additional input data to verify the change in the operational state.