Data Center Infrastructure Management (DCIM) system comprising predictive analytics
A Data Center Infrastructure Management (DCIM) system comprising predictive analytics and methods for collecting data, analyzing data, optimizing infrastructure efficiency and automating management of data center infrastructure systems and components is disclosed. The DCIM system comprising predictive analytics may generally comprise a DCIM appliance or server, data collection hardware, database hardware, software for collecting data from a plurality of infrastructure systems, infrastructure components and wireless sensors, presentation client software, reporting software and an intelligent predictive analytics engine. The intelligent predictive analytics engine may be employed to identify infrastructure optimization actions enabling the DCIM system software or DCIM element controller to enact changes to the operational state of data center infrastructure systems or components to sustain optimal data center infrastructure efficiency. The DCIM system comprising predictive analytics may continuously collect and analyze infrastructure system, infrastructure components and environmental data.
This application claims reference to Provisional Patent application No. 61/925,531 filed on Jan. 9, 2014, entitled “A Data Center Infrastructure Management (DCIM) system with predictive analytics.”
FIELDThe present invention relates to infrastructure management systems, especially with respect to data center facilities, but not restricted to the said data center facilities.
BACKGROUND OF THE INVENTIONData centers and co-location providers in particular struggle with both supplying requisite power as well as cooling. As data center density continues to increase there is a growing demand for more energy efficient and cost effective data centers and co location solutions.
A data center is designed to maintain interior ambient conditions suitable for proper operation of the computer systems therein. Typical data centers may consume more than twice the power needed to support the plurality of computer systems housed therein. This is a result of the inefficient air conditioning units that may account for half of the total power consumed in the data center to cool the plurality of computer systems therein. This inefficiency prohibits support of high density computing systems in today's data centers.
Embodiments disclose a data center infrastructure management (DCIM) system and method for monitoring, controlling and analyzing infrastructure and environmental conditions and managing resources according to the monitored and analyzed conditions.
Traditional data centers face challenges with technical innovation, operational efficiency and modern design principles. With increasingly complex environments such challenges with energy efficiency and resource utilization management have become vital to long term sustainment of data center facilities. Current data center providers struggle to monitor infrastructure systems, collect data from infrastructure systems and manage infrastructure systems to allow optimal efficiency of the data center facility.
Traditional data centers are built with physical infrastructure that is static in nature. When this constrained static infrastructure is placed under dynamic workloads, it can expose significant infrastructure inefficiencies. These inefficiencies may only be addressed through continuous collection and analysis of data center infrastructure and environmental data.
The described DCIM system comprising predictive analytics may be employed to continuously collect and analyze infrastructure system, component, and environmental data. The DCIM system comprising predictive analytics may identify inefficiencies or previously unknown interdependencies. The continuous collection and analysis of infrastructure and environmental data enables automated management of infrastructure systems and components to sustain optimal infrastructure efficiencies.
SUMMARYA system for data center infrastructure management comprising a processing unit coupled to a memory element, and having instructions encoded thereon, wherein the encoded instructions cause the system to: collect and store data center infrastructure system condition data, environmental condition data and component condition data; analyze the collected infrastructure system, environmental and component condition data; and based on the collected and analyzed data, automatically make zero or more adjustments to data center infrastructure system condition, environmental condition and component condition.
In a system for data center infrastructure management comprising a processing unit coupled to a memory element, and having instructions encoded thereon, a method comprising: collecting and storing data center infrastructure system condition data, environmental condition data and component condition data; analyzing the collected infrastructure system, environmental and component condition data; and based on the collected and analyzed data, automatically making zero or more adjustments to data center infrastructure system condition, environmental condition and component condition.
A system for data center infrastructure management comprising a processing unit coupled to a memory element, and having instructions encoded thereon, wherein the encoded instructions cause the system to: collect and store data center infrastructure system condition data, environmental condition data and component condition data; analyze the collected infrastructure system, environmental and component condition data; and based on the collected and analyzed data, automatically make zero or more adjustments to data center infrastructure system condition, environmental condition and component condition; wherein the said zero or more adjustments are based on a predictive analytics functionality configured to continuously collect and analyze data, and wherein the predictive analytics functionality is further configured to implement predictive analytics of a single or plurality of virtual machines, an instance or instances over a cloud computing network, and to estimate demand for the said virtual machines and cloud instances; and wherein the analytics for demand comprises: estimating a baseline of virtual machine or cloud demands based on collected real-time and historical demand data; estimating a baseline of virtual machine or cloud status based on collected real-time and historical demand data; predicting future status and demand based on predictive modeling which further comprises the collected real-time estimations; and based on the predictive modeling and analytics, dynamically implementing an action or actions.
In a system for data center infrastructure management comprising a processing unit coupled to a memory element, and having instructions encoded thereon, a method comprising: collecting and storing data center infrastructure system condition data, environmental condition data and component condition data; analyzing the collected infrastructure system, environmental and component condition data; and based on the collected and analyzed data, automatically making zero or more adjustments to data center infrastructure system condition, environmental condition and component condition; wherein the said zero or more adjustments are based on a predictive analytics functionality configured for continuously collecting and analyzing data, and wherein the predictive analytics functionality is further configured to implement predictive analytics of a single or plurality of virtual machines, an instance or instances over a cloud computing network, and to estimate demand for the said virtual machines and cloud instances; and wherein the analytics for demand comprises: estimating a baseline of virtual machine or cloud demands based on collected real-time and historical demand data; estimating a baseline of virtual machine or cloud status based on collected real-time and historical demand data; predicting future status and demand based on predictive modeling which further comprises the collected real-time estimations; and based on the predictive modeling and analytics, dynamically implementing an action or actions.
As stated above, Traditional data centers face challenges with technical innovation, operational efficiency and modern design principles. With increasingly complex environments such challenges with energy efficiency and resource utilization management have become vital to long term sustainment of data center facilities. Current data center providers struggle to monitor infrastructure systems, collect data from infrastructure systems and manage infrastructure systems to allow optimal efficiency of the data center facility. Embodiments of the present invention solve this problem.
The Data Center Infrastructure Management (DCIM) system described may be employed to provide continuous monitoring and analysis of data to enable automated management of data center mechanical, electrical and cooling infrastructure to maintain optimal infrastructure efficiency.
Embodiments disclosed are different from, and superior to what currently exists. Embodiments disclosed include a new and improved method and system for infrastructure management and control, and more particularly for data center infrastructure management and control. According to an embodiment, the Data Center Infrastructure Management System (DCIM) system comprises predictive analytics described in this document, which is beyond the scope of existing systems. The ability to automate infrastructure management through collected data and predictive analytics provides a clear advantage to what currently exists.
Data center infrastructure is constrained and static in nature. The inefficiencies of such constrained static design are quickly exposed when placed under a dynamic load. Without continuous collection and analysis of infrastructure and environmental data, management of data center infrastructure systems and components is a hit and miss proposition. These limitations cause inefficient power consumption and prohibit automated management of data center infrastructure.
The described DCIM system comprising predictive analytics may be employed to continuously collect and analyze infrastructure system, component, and environmental data. The DCIM system comprising predictive analytics may identify inefficiencies or previously unknown interdependencies. The continuous collection and analysis of infrastructure and environmental data enables automated management of infrastructure systems and components to sustain optimal infrastructure efficiencies. Alternatively and additionally, embodiments of this invention can continuously monitor, collect and analyze data to automate management of virtual machine resources across a data center or data centers, wherein the monitoring, collecting, analyzing and control can be performed onsite, or remotely in a centralized fashion.
A system for data center infrastructure management comprising a processing unit coupled to a memory element, and having instructions encoded thereon, wherein the encoded instructions cause the system to collect and store data center infrastructure system condition data, environmental condition data and component condition data; analyze the collected infrastructure system, environmental and component condition data; and based on the collected and analyzed data, automatically make zero or more adjustments to data center infrastructure system condition, environmental condition and component condition. The said analyzing further comprises predictive analytics configured for continuously collecting and analyzing data from the infrastructure system, the environment, and the said component or components. The said collecting further comprises collecting environmental data from a plurality of wireless sensors and collecting infrastructure system and component data from infrastructure elements wherein said infrastructure system and component data comprise collecting air temperature data and air flow data. The system is further caused to employ the analyzed data via a DCIM element controller, wherein the DCIM element controller comprises a means for configuring the infrastructure system and components' operational states for optimal efficiency, and wherein the configuring further comprises configuring based on analyzing if ambient air temperature is within a defined range. The configuring includes making zero (if ambient air temperature is within the defined range) or more (if ambient air temperature is not within the defined range) adjustments to the CRAC, CDU or/and RDHX to bring the ambient air temperature to within the defined range. Additionally, the configuring further comprises measuring ambient air flow data, and analyzing if the measured air flow is within a defined range, and making zero (if ambient air flow is within the defined range) or more (if ambient air flow is not within the defined range) adjustments to a single or plurality of VFD fans to bring the said air flow within the defined range. Additionally the configuring further comprises measuring water flow data and analyzing if the measured water flow is within acceptable predefined range and making zero or more adjustments to a single or plurality of VFD water pumps or automated, adjustable flow control valves to bring the said water flow within the defined range. According to an embodiment, the system is further caused to, via a presentation software module, allow display of the collected and analyzed data to a single or plurality of users. According to an additional embodiment the system is caused to allow access to the system over a secure network, and can access other systems via the said secure network.
According to an embodiment, the predictive analytics configured for continuously collecting and analyzing data, is further configured to implement predictive analytics of a single or plurality of virtual machines, an instance or instances over a cloud computing network, and demand for the said virtual machines and cloud instances, wherein the analytics for demand comprises: estimating a baseline of virtual machine or cloud demands based on collected real-time and historical demand data; estimating a baseline of virtual machine or cloud status based on collected real-time and historical demand data; predicting future status and demand based on predictive modeling which further comprises the collected real-time estimations; and based on the predictive modeling and analytics, dynamically implementing an action or actions. Thus, in an example embodiment, the disclosed predictive analytics is a key feature that not only enables monitoring infrastructure (electrical/cooling/mechanical) but also enables monitoring systems comprising virtual machines and entire cloud computing instances over a network. Predictive analytics for virtual machines and clouds allow the system to further leverage actionable analytics. For example based on real-time and historical data, a predictive analytics engine comprised in the system can predict when a cloud will be overrun with demand and dynamically add capacity.
The DCIM system comprising predictive analytics may comprise a plurality of DCIM appliances, or servers 314, which may be employed for hosting presentation software 308, predictive analytics engine or software 306, data collection software 304 and DCIM element controller software 302. The data collection software 304 is configured to continuously collect environmental data from a plurality of wireless sensors 300 and infrastructure system and component data from infrastructure elements 300. All of the collected data is stored in the database hardware 310. The predictive analytics engine or software 306 may be employed to analyze the stored data. The DCIM element controller 302 may be employed to issue operational state changes to infrastructure systems or components based on data that has been collected and analyzed. In one example, a wireless sensor measures air temperature 202, the data is analyzed to determine if the air temperature is within a defined range 204, if it is not within the defined range, the DCIM element controller may issue instructions to adjust the CRAC (Computer Room Air Conditioner), CDU or/and RDHX to bring the air temperature within the defined range. Then a wireless sensor 300 may measure air flow/pressure 204, the data is analyzed to find if the airflow/pressure is within the defined range, if it is not then the DCIM element controller 302 may issue instructions to adjust the VFD (Variable Frequency Drive) fans to bring the airflow/pressure within the defined range. Then a sensor may measure water flow with the data analyzed to find if the water flow is within the predefined range if it is not then the DCIM element controller may issue instructions to adjust the VFD water pumps or automated, adjustable flow control valves to bring the water flow within the defined range. Note that the above checks may be performed sequentially (as described) or alternatively, they may be performed simultaneously. Variations in prioritization of checks are possible, and in some instances, desirable, as would be apparent to a person having ordinary skill in the art.
The described DCIM system comprising predictive analytics may continuously collect and analyze data from a plurality of infrastructure systems, components and wireless sensors. A plurality of wireless sensors may be employed to continuously collect environmental data.
The data collected by the DCIM system may be stored in a database. The stored data may then be analyzed by the predictive analytics engine. The analyzed data may be employed by the DCIM element controller to manage infrastructure systems and components operational states to sustain optimal infrastructure efficiency.
In preferred embodiments, the predictive analytics configured for continuously collecting and analyzing data, and comprised in the DCIM, is further configured to collect and analyze data from a single or plurality of virtual machines, and an instance or instances over a cloud computing network. Additionally, the predictive analytics includes, estimating a demand for the said virtual machines and cloud instances, wherein the said estimating comprises: estimating a baseline of virtual machine or cloud demands based on collected real-time and historical demand data; estimating a baseline of virtual machine or cloud status based on collected real-time and historical demand data; predicting future status and demand based on predictive modeling which further comprises the collected real-time estimations; and based on the predictive modeling and analytics, dynamically implementing an action or actions. The DCIM element controller 302 may then be employed to issue operational state changes to infrastructure systems or components based on data that has been collected and analyzed.
The presentation software permits viewing of all the collected and analyzed data by an end user with the presentation client software. The DCIM system may be accessible over a secure IP network (not pictured). Additionally and alternatively, the DCIM system can control infrastructure elements, systems, components, virtual machines and cloud based instances, remotely, over a network.
In a system for data center infrastructure management comprising a processing unit coupled to a memory element, and having instructions encoded thereon, a method comprising, collecting and storing data center infrastructure system condition data, environmental condition data and component condition data, analyzing the collected infrastructure system, environmental and component condition data, and based on the collected and analyzed data, automatically making zero or more adjustments to data center infrastructure system condition, environmental condition and component condition.
According to an embodiment the analyzing is comprised in predictive analytics configured for continuously collecting and analyzing data from the infrastructure system, the environment, and the said component or components. The collecting further comprises collecting environmental data from a plurality of wireless sensors and collecting infrastructure system and component data from infrastructure elements wherein the said infrastructure system and component data comprise collecting air temperature data and air flow data.
An embodiment includes employing the analyzed data via a DCIM element controller, wherein the DCIM element controller comprises means for configuring the infrastructure system and components' operational states for optimal efficiency. Additionally, the said configuring further comprises configuring based on analyzing if ambient air temperature is within a defined range, and making zero (if the ambient air temperature is within the defined range) or more (if the ambient air temperature is not within the defined range) adjustments to the CRAC, CDU or/and RDHX to bring the ambient air temperature to within the defined range. According to additional embodiments the configuring further comprises measuring ambient air flow data, and analyzing if the measured air flow is within a defined range, and making zero (if the ambient air flow is within the defined range) or more (if the ambient air flow is not within the defined range) adjustments to a single or plurality of VFD fans to bring the said air flow within the defined range. According to additional embodiments the configuring further comprises measuring water flow data, and analyzing if the measured water flow is within a defined range, and making zero (if the water flow is within the defined range) or more (if the water flow is not within the defined range) adjustments to a single or plurality of VFD water pumps or automated, adjustable flow control valves to bring the said water flow within the defined range.
Embodiments disclosed further include in the method, via a presentation software module, allowing display of the collected and analyzed data to a single or plurality of users, and allowing access to the system over a secure network.
Embodiments disclosed comprise a DCIM system software suite, a DCIM appliance or server used to install and run the DCIM system software suite, system elements and wireless sensors for collecting data from electrical, mechanical and cooling infrastructure systems or/and components. Preferred embodiments further include an intelligent predictive analytics engine to permit dynamic management of infrastructure systems or components.
Having described at least one embodiment of the present disclosure, various alterations, modifications and improvements will readily occur to those skilled in the art. Such alterations, modifications and improvements are intended to be within the scope and spirit of the disclosure. Accordingly, the foregoing description is by way of example only and is not intended to be limiting.
Preferred embodiments include a DCIM system including all hardware, software, system elements and wireless sensors described above. Ideally the system is highly configurable, wherein the database and predictive analytics engine can be configured for use in a multitude of scenarios that require analysis of collected data. Additionally, a presentation client and presentation interface that will be used to present data to end users is configurable according to various situations.
Embodiments of the system and method described may be employed by any field where it would be beneficial for systems or components to be dynamically managed based on defined data ranges and with a defined set of control commands/instructions that can be executed to change the operational state of the systems or components.
Further variations of embodiments of this invention are capable of continuously monitoring, collecting and analyzing data to automate management of virtual machine resources across a data center or data centers, on site or remotely, as would be apparent to a person having ordinary skill in the art.
Additionally, partial or complete embodiments of the disclosed invention can be utilized in alternate applications without departing from the scope and spirit of the disclosure. For example, DCIM systems and predictive analytics can be utilized to manage electrical, mechanical, cooling, and other crucial components, in commercial or residential buildings, factories, supermarkets, stores, and other resource consuming space including but not limited to buildings or dwellings, in an energy-efficient and cost-effective manner.
Embodiments disclosed provide systems and methods for efficient onsite and remote monitoring of infrastructure systems, efficient and accurate collection of data from the infrastructure systems and optionally automated management of these infrastructure systems to allow optimal efficiency of data center facilities and other such spaces.
Embodiments disclosed include dynamic real time management and control of infrastructure resources in data centers and other such facilities, resulting in increased efficiencies and lowered costs. Systems and methods disclosed provide for continuous data collection, real time data analysis and accurate forecasting for resource allocation through embodiments of the predictive analysis engine, module, and software.
Embodiments of the DCIM system comprising predictive analytics may be employed to continuously collect and analyze infrastructure system, component, and environmental data, identify inefficiencies or previously unknown interdependencies, and enable automated management of infrastructure systems and components to sustain optimal infrastructure efficiencies.
Since various possible embodiments might be made of the above invention, and since various changes might be made in the embodiments above set forth, it is to be understood that all matter herein described or shown in the accompanying drawings is to be interpreted as illustrative and not to be considered in a limiting sense. Thus it will be understood by those skilled in the art of infrastructure management, and more specifically automated infrastructure management especially pertaining to data centers, that although the preferred and alternate embodiments have been shown and described in accordance with the Patent Statutes, the invention is not limited thereto or thereby.
The figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. It should also be noted that, in some alternative implementations, the functions noted/illustrated may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Some portions of embodiments disclosed are implemented as a program product for use with an embedded processor. The program(s) of the program product defines functions of the embodiments (including the methods described herein) and can be contained on a variety of signal-bearing media. Illustrative signal-bearing media include, but are not limited to: (i) information permanently stored on non-writable storage media (e.g., read-only memory devices within a computer such as CD-ROM disks readable by a CD-ROM drive); (ii) alterable information stored on writable storage media (e.g., hard-disk drive, solid state disk drive, etc.); and (iii) information conveyed to a computer by a communications medium, such as through a computer or telephone network, including wireless communications. The latter embodiment specifically includes information downloaded from the Internet and via other networks. Such signal-bearing media, when carrying computer-readable instructions that direct the functions of the present invention, represent embodiments of the present invention.
In general, the routines executed to implement the embodiments of the invention, may be part of an operating system or a specific application, component, program, module, object, or sequence of instructions. The computer program of the present invention typically is comprised of a multitude of instructions that will be translated by the native computer into a machine-accessible format and hence executable instructions. Also, programs are comprised of variables and data structures that either reside locally to the program or are found in memory or on storage devices. In addition, various programs described hereinafter may be identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature that follows is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature.
The present invention and some of its advantages have been described in detail for some embodiments. It should be understood that although the system and process is described with reference to automated infrastructure management in water borne data centers, the system and process is highly reconfigurable, and may be used in other contexts as well. It should also be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims. An embodiment of the invention may achieve multiple objectives, but not every embodiment falling within the scope of the attached claims will achieve every objective. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification. A person having ordinary skill in the art will readily appreciate from the disclosure of the present invention that processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed are equivalent to, and fall within the scope of, what is claimed. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.
Claims
1. A computer system for data center infrastructure management (DCIM) comprising:
- a processor unit; a memory element coupled to the processor unit; a connection to and means for communicating over a wired and wireless network: wherein the system is configured to:
- determine an optimum placement of a single or plurality of servers, which further comprises, estimating a power, a cooling and a network data resource requirement for each rack in a plurality of racks;
- based on the determined optimum placement, enforce a pre-defined process for operating the data center;
- based on the pre-defined process, determine an operational requirement from collected operational data, which comprises collected environmental, power, cooling, and information technology (IT) data;
- via a predictive analytics engine configured to communicate over the network, analyze and store the collected operational data;
- based on the collected and analyzed operational data, automatically make zero or more adjustments to the, environmental condition, power condition, cooling condition and IT condition;
- wherein the predictive analytics engine is further configured to analyze a single or plurality of virtual machines, an instance or instances over a cloud computing network, and to estimate based on the analyzed virtual machines and instances, a demand for the said virtual machines and cloud instances; and
- wherein the analytics for demand comprises:
- estimating a baseline of virtual machine or cloud demands based on collected real-time and historical demand data;
- estimating a baseline of virtual machine or cloud status based on collected real-time and historical demand data;
- predicting future status and demand based on predictive modeling which further comprises the collected real-time estimations; and
- based on the predictive modeling and analytics, and via a planning engine configured to communicate over the network, dynamically implementing an action or actions.
2. The computer system of claim 1, wherein the predictive analytics engine is further configured to analyze a future infrastructure system condition, a future environment condition, and a future component or components' condition.
3. The computer system of claim 1 wherein the collection of operational data further comprises collecting environmental data from a plurality of wireless sensors and collecting infrastructure system and component data from infrastructure and component elements, wherein said infrastructure system and component data comprise collecting air temperature data, air flow data, water temperature data and water flow data.
4. The computer system of claim 1 further comprises a data center infrastructure management (DCIM) element controller, wherein the DCIM element controller is caused to employ the analyzed data, and based on the analyzed data, configure the infrastructure system and components' operational states for optimal efficiency.
5. The system of claim 4 wherein the DCIM element controller is further caused to configure based on whether an analyzed ambient air temperature is within a defined range.
6. The system of claim 5 wherein based on the analyzed air temperature, the DCIM element controller is caused to make zero or more adjustments to at least one of a computer room air-conditioner (CRAC), a rear door heat exchanger (RDHX), a coolant distribution unit (CDU), and a single or plurality of automated, adjustable flow control valves, to bring the ambient air temperature to within the defined range.
7. The system of claim 4 wherein the DCIM element controller is further caused to configure based on whether an analyzed water temperature and water flow is within a defined range.
8. The system of claim 7 wherein based on the analyzed water temperature and water flow, the DCIM element controller is caused to make zero or more adjustments to at least one of a coolant distribution unit (CDU), a rear door heat exchanger (RDHX), a single or plurality of automated, adjustable flow control valves, and a single or plurality of variable frequency drive (VFD) pumps to bring the water flow and the water temperature to within a defined range.
9. The system of claim 4 wherein the DCIM element controller is further caused to configure based on whether an analyzed, measured air flow is within a defined range.
10. The system of claim 9 wherein based on the analyzed, measured air flow, the DCIM element controller is caused to make zero or more adjustments to a single or plurality of variable frequency drive (VFD) fans to bring the said air flow to within the defined range.
11. The system of claim 1 wherein the system is further caused to, via a presentation software module, display the collected and analyzed data to a single or plurality of users.
12. The system of claim 1 wherein the system further caused to allow access to the system over a secure network.
13. In a system for data center infrastructure management (DCIM) comprising a processing unit coupled to a memory element, and having instructions encoded thereon, a method comprising:
- determining an optimum placement of a single or plurality of servers, which further comprises, estimating a power, cooling and network data resource requirement for each rack in a plurality of racks;
- based on the determined optimum placement, enforcing a pre-defined process for operating the data center;
- based on the pre-defined process for operating the data center, determining operational requirements from collected operational data, which comprises collected environmental data, power data, cooling data, and information technology (IT) data;
- via a predictive analytics engine configured to communicate over the network, collecting, analyzing and storing data of the said operational requirements;
- based on the collected and analyzed data, automatically making zero or more adjustments to the environmental condition, power condition, cooling condition and IT condition;
- wherein the predictive analytics engine is further configured to analyze a single or plurality of virtual machines, an instance or instances over a cloud computing network, and to estimate based on the analyzed virtual machines and instances, a demand for the said virtual machines and cloud instances; and
- wherein the analytics for demand comprises:
- estimating a baseline of virtual machine or cloud demands based on collected real-time and historical demand data;
- estimating a baseline of virtual machine or cloud status based on collected real-time and historical demand data;
- predicting future status and demand based on predictive modeling which further comprises the collected real-time estimations; and
- based on the predictive modeling and analytics, and via a planning engine configured to communicate over the network, dynamically implementing an action or actions.
14. The method of claim 13, further comprising, based on collected and analyzed data, predictively analyzing a future infrastructure system condition, a future environment condition, and a future component or components' condition.
15. The method of claim 13 wherein the said collecting further comprises collecting environmental data from a plurality of wireless sensors and collecting infrastructure system and component data from infrastructure and component elements wherein said infrastructure system and component data comprise collecting air temperature data, air flow data, water temperature data and water flow data.
16. The method of claim 13 further comprising: employing the analyzed data via a data center infrastructure management (DCIM) element controller; and configuring the infrastructure system and components' operational states for optimal efficiency via the DCIM controller.
17. The method of claim 16 wherein the said configuring further comprises configuring based on analyzing if ambient air temperature is within a defined range.
18. The method of claim 17 wherein the said configuring further comprises making zero or more adjustments to at least one of a computer room air-conditioner (CRAC), a rear door heat exchanger (RDHX), a coolant distribution unit (CDU), and a single or plurality of automated, adjustable flow control valves, to bring the ambient air temperature to within the defined range.
19. The method of claim 16 wherein the said configuring further comprises configuring based on analyzing if the water temperature and water flow is within a defined range.
20. The method of claim 19 wherein the said configuring further comprises making zero or more adjustments to at least one of a coolant distribution unit (CDU), a rear door heat exchanger (RDHX), a single or plurality of automated, adjustable flow control valves, and a single or plurality of variable frequency drive (VFD) pumps to bring the water flow and the water temperature to within a defined range.
21. The method of claim 16 wherein the said configuring further comprises measuring ambient air flow data, and analyzing if the measured air flow is within a defined range.
22. The method of claim 21 wherein the said configuring further comprises making zero or more adjustments to a single or plurality of VFD fans to bring the said air flow within the defined range.
23. The method of claim 13 further comprising, via a presentation software module, displaying of the collected and analyzed data to a single or plurality of users.
24. The method of claim 13 further comprising allowing access to the system over a secure network.
25. A system for data center infrastructure management (DCIM) comprising a processing unit coupled to a memory element, and having instructions encoded thereon, wherein the encoded instructions cause the system to:
- collect and store data center infrastructure system condition data, environmental condition data and component condition data;
- analyze the collected infrastructure system, environmental and component condition data; and
- based on the collected and analyzed data, automatically make zero or more adjustments to data center infrastructure system condition, environmental condition and component condition;
- wherein the said zero or more adjustments are based on a predictive analytics functionality configured to continuously collect and analyze data, and wherein the predictive analytics functionality is further configured to implement predictive analytics of a single or plurality of virtual machines, an instance or instances over a cloud computing network, and to estimate demand for the said virtual machines and cloud instances; and
- wherein the analytics for demand comprises:
- estimating a baseline of virtual machine or cloud demands based on collected real-time and historical demand data;
- estimating a baseline of virtual machine or cloud status based on collected real-time and historical demand data;
- predicting future status and demand based on predictive modeling which further comprises the collected real-time estimations; and
- based on the predictive modeling and analytics, dynamically implementing an action or actions.
26. In a system for data center infrastructure management (DCIM) comprising a processing unit coupled to a memory element, and having instructions encoded thereon, a method comprising:
- collecting and storing data center infrastructure system condition data, environmental condition data and component condition data;
- analyzing the collected infrastructure system, environmental and component condition data; and
- based on the collected and analyzed data, automatically making zero or more adjustments to data center infrastructure system condition, environmental condition and component condition;
- wherein the said zero or more adjustments are based on a predictive analytics functionality configured for continuously collecting and analyzing data, and wherein the predictive analytics functionality is further configured to implement predictive analytics of a single or plurality of virtual machines, an instance or instances over a cloud computing network, and to estimate demand for the said virtual machines and cloud instances; and
- wherein the analytics for demand comprises:
- estimating a baseline of virtual machine or cloud demands based on collected real-time and historical demand data;
- estimating a baseline of virtual machine or cloud status based on collected real-time and historical demand data;
- predicting future status and demand based on predictive modeling which further comprises the collected real-time estimations; and
- based on the predictive modeling and analytics, dynamically implementing an action or actions.
Type: Application
Filed: Jan 7, 2015
Publication Date: Aug 3, 2017
Inventors: Arnold C. Magcale (San Ramon, CA), Daniel Kekai (San Ramon, CA)
Application Number: 14/591,572
