System and method for intelligent data center power management and energy market disaster recovery

• US 10,437,636 B2
• Filed: 11/14/2014
• Issued: 10/08/2019
• Est. Priority Date: 01/09/2014
• Status: Active Grant

• Current Assignee: Nautilus True LLC
• Original Assignee: Nautilus Data Technologies, Inc.
• Inventors: Magcale, Arnold C., Kekai, Daniel
• Primary Examiner(s): Vu, Tuan A
• Application Number:
  US14/542,011
• Publication Number:
  US 20170220061A1
• Time in Patent Office: 1,789 Days
• CPC Class Codes: A63C 17/01 Skateboards A63C17/02 - A63C17/28 take precedence; rolling devices on skis A63C5/035
• CPC Class Codes: A63C 17/1418 with radial movement against the roll surface of the wheel
• CPC Class Codes: A63C 17/26 with special auxiliary arrangements, e.g. illuminating, marking, or push-off devices
• CPC Class Codes: A63C 2017/1463 Foot or toe operated
• CPC Class Codes: A63C 2203/14 Lighting means
• CPC Class Codes: F16B 2/065 using screw-thread elements F16B2/08 - F16B2/12 take precedence
• CPC Class Codes: F16B 9/02 Detachable connections F16B9/05 – F16B9/09 take precedence
• CPC Class Codes: F16B 9/05 by way of an intermediate member
• CPC Class Codes: F16M 11/04 Means for attachment of apparatus; Means allowing adjustment of the apparatus relatively to the stand
• CPC Class Codes: F16M 13/02 for supporting on, or attaching to, an object, e.g. tree, gate, window-frame, cycle
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05F 1/66 Regulating electric power
• CPC Class Codes: G06F 9/4856 resumption being on a different machine, e.g. task migration, virtual machine migration G06F9/5088 takes precedence
• CPC Class Codes: G06F 9/505 considering the load
• CPC Class Codes: Y02D 10/00 Energy efficient computing, e.g. low power processors, power management or thermal management
• CPC Class Codes: Y10T 24/44017 with specific mounting means for attaching to rigid or semirigid supporting structure or structure-to-be-secured

Abstract

Systems and methods for intelligent data center power management and energy market disaster recovery comprised of data collection layer, analytics/automation/actions layer, energy markets analysis layer. Plurality of data centers employ the systems and methods comprised of a plurality of Tier 2 data centers that may be running applications, virtual machines and physical computer systems to enable data center and application disaster recovery from utility energy market outages. Systems and methods may be employed to enable application load balancing and data center power load balancing across a plurality of data centers may lead to financial benefits when moving application and power loads from one data center location using power during peak energy hours to another data center location using power during off-peak hours.

Claims

1. A computer automated system for intelligent power management, comprising a processing unit coupled to a memory element, and having instructions encoded thereon, which instructions cause the system to:

trigger by a collection layer comprised in the processing unit, a collection of infrastructure data, application data, power data, and machine element data by signaling a plurality of corresponding infrastructure elements, application elements, power elements, and virtual machine elements, respectively;

analyze the collected data by an analytic engine comprised in the computer automated system; and

based on the collected analyzed data, trigger an operational state change;

wherein the triggered operational state change further comprises an application load balancing and a power load balancing by the processing unit, across a plurality of data centers by real-time migration of an application load and a power load from one data center to another; and

wherein the system is configured to allow each data center to communicate with each other data center over a network and to connect to a plurality of energy providers over the network.

2. The system of claim 1 wherein the system is further caused to:

via an energy market analysis layer, automatically, without manual intervention, manage data center and application disaster recovery from utility energy market outages based on data collected from a single or plurality of energy market elements.

3. The system of claim 1 wherein the system is further caused to:

based on the collected data, measure a plurality of parameters wherein if a measured parameter falls within a predefined range, measure a next measurable parameter; and

wherein if the measured parameter falls outside of the predefined range, execute a predefined action to bring the said measured parameter within the predefined range.

4. The computer automated system of claim 1, wherein the system is further configured to:

manage in real time, data center power distribution loads, application loads and virtual machine loads, across multiple datacenters.

5. The computer automated system of claim 1 wherein the encoded instructions further cause the system to:

without manual intervention, automatically handle data center operation state changes, and dynamically balance power loads and application loads across multiple datacenters.

6. The computer automated system of claim 1 wherein the computer system is further configured to:

collect data from a plurality of energy markets;

analyze the collected data from the said plurality of energy markets; and

based on the collected analyzed data, enable automatic data center operation state changes, thereby allowing data center and application disaster recovery from utility energy market outages.

7. The computer automated system of claim 1 wherein the analytic engine further comprises a predictive analytics engine, which comprises instructions that cause the system to:

model and enable scenario modeling for and of designated applications, virtual machines, and power loads; and

predict outages caused by energy market failures, application loads, virtual machine loads or power loads in a data center.

8. The computer automated system of claim 1 wherein the instructions further cause the system to:

automatically manage virtual machine instances, which comprises killing of virtual servers or banks of physical computer systems during low application loads and turning up virtual machines or banks of physical computer systems prior to expected peak loads.

9. In a computer automated system for intelligent power management and comprising a processing unit coupled to a memory element having instructions encoded thereon, a method comprising:

triggering by a collection layer comprised in the processing unit, collecting of infrastructure data, application data, power data, and machine element data from a plurality of corresponding infrastructure elements, application elements, power elements, and virtual machine elements, respectively;

analyzing the collected data by an analytic engine comprised in the computer automated system; and

triggering, based on the analyzed collected data, an operational state change;

wherein the triggering of the operational state change further comprises an application load balancing and power load balancing across a plurality of data centers, by real-time migration of an application load and a power load from one data center to another; and

wherein the triggering is based on a communication by each data center to each other data center over a network and the communication further comprises connecting to a plurality of energy providers over the network.

10. The method of claim 9 further comprising:

via an energy market analysis layer, automatically, without manual intervention, managing data center and application disaster recovery from utility energy market outages based on data collected from energy market elements.

11. The method of claim 9 further comprising:

based on the collected data, measuring a plurality of parameters wherein if a measured parameter falls within a predefined range, measuring a next measurable parameter; and

wherein if the measured parameter falls outside of the predefined range, executing a predefined action to bring the said measured parameter within the predefined range.

12. The method of claim 9, further comprising managing in real time, data center power distribution loads, application loads and virtual machine loads, across multiple data centers.

13. The method of claim 9 further comprising:

automatically handling data center operation state changes, and dynamically balancing power loads and application loads across multiple data centers.

14. The method of claim 9 further comprising, via the analytic engine comprised in the computer automated system:

collecting data from a plurality of energy markets;

analyzing the collected data from the said plurality of energy markets; and

based on the collected analyzed data, enabling automatic data center operation state changes, thereby allowing data center and application disaster recovery from utility energy market outages.

15. The method of claim 9 wherein the said analyzing further comprises, via a predictive analytics engine:

modeling and enabling scenario modeling for and of designated applications, virtual machines, and power loads; and

predicting outages caused by energy market failures, application loads, virtual machine loads or power loads in a data center.

16. The method of claim 9 further comprising:

automatically managing virtual machine instances, which comprises killing of virtual servers or banks of physical computer systems during low application loads and turning up virtual machines or banks of physical computer systems prior to expected peak loads.

17. A computer automated system for intelligent power management, comprising:

a processor;

a non-transitory computer readable storage medium coupled to the processor and having instructions encoded thereon, which instructions when implemented by the processor, cause the computer system to;

extract by a collection layer comprised in the processing unit, an infrastructure condition data, an application condition data, a power condition data, and a machine element condition data from a plurality of corresponding infrastructure elements, application elements, power elements, and virtual machine elements;

analyze the extracted infrastructure condition, application condition, power condition, and a machine element condition data by an analytic engine comprised in the computer automated system; and

based on the analysis, trigger an operational state change wherein the operational state change comprises an application load balancing and a power load balancing;

wherein the triggering of the operational state change further comprises an application load balancing and power load balancing across a plurality of data centers, by real-time migration of an application load and a power load from one data center to another.