CN205353675U - Cloud calculates center computer room energy -saving control system - Google Patents
Cloud calculates center computer room energy -saving control system Download PDFInfo
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- CN205353675U CN205353675U CN201620043321.4U CN201620043321U CN205353675U CN 205353675 U CN205353675 U CN 205353675U CN 201620043321 U CN201620043321 U CN 201620043321U CN 205353675 U CN205353675 U CN 205353675U
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Abstract
The utility model discloses a cloud calculates center computer room energy -saving control system belongs to cloud computer technology field, central controller contain electrical unit, CPU module, input output module, central controller be connected with data acquisition unit, data displaying processor, environment centralized monitoring and energy consumption management module be connected with central controller, intelligent control platform and environment centralized monitoring and energy consumption management module interconnect, system control case and intelligent control platform interconnect, air inlet machine and system control case interconnect, play fan and air inlet machine interconnect. Its structural design is reasonable, and through make the processing that is suitable for data center to the energy -conserving of discovery and energy -conserving factor, the level of resources utilization is optimized simultaneously to reduction data center energy consumption.
Description
Technical field
This utility model relates to cloud computer technical field, is specifically related to cloud computing center machine room energy-saving control system.
Background technology
Along with China's economic fast and stable develop, China became second largest economy in the world in 2010.After this, Chinese topmost target seeks to keep and improve economic development.The whole IT industry that appears as of cloud computing brings the change in technology and business model, and having people cloud computing to be compared to is change the second time IT tide after changing with the Internet continue personal computer.It can be seen that cloud computing certainly will will become the new trend that IT service provides, and in the process of China's Economic Transition, occupy large market share with its uniqueness.
Data center in cloud computing environment includes ten hundreds of computers and server, how effectively to reallocate resources, reduce operating cost, saves the energy run required for computing terminal and become a hot issue paid close attention to.According to statistics, in the middle of the energy consumption composition that machine room is huge, air-conditioning occupies more than 40%, it is energy consumption " main force ", further, along with the appearance of large-scale data center high density server Yu low-density mixed model, owing to the density unevenness of server weighs, thus the heat of generation is also unbalanced, the average refrigerating method of conventional data centers has been difficult to meet demand.
And cloud computing resources is widely distributed on geographical position, being substantially isomery, and each management domain has different policies in resource management and access price model, therefore the resource management of cloud computing must handle these isomerism problems well.Simultaneously as cloud computing is advocated as " green calculate ", just more and more consider energy-saving and environment friendly factor, so providing, in resource, the consideration also needed in optimization process in energy-saving and emission-reduction.If resource provides strategy unreasonable, not only result in the waste of time and resource, also can cause the carbon emission that the energy runs off and environment is had a negative impact.Considering these factors, it is possible to sum up the problem that current cloud computing generally faces " two high two low ", it may be assumed that consumption of data center is high, data center's CO2 emission is high, and resource utilization is too low and cloud resource supplier income is low.Therefore, the energy consumption problem in research cloud computing environment and efficient resource thereof provide optimization method will be of great practical significance for the application of cloud computing.
Utility model content
For the problems referred to above, the technical problems to be solved in the utility model is to provide a kind of reasonable in design, easy to operate, working (machining) efficiency is high and stay-in-grade cloud computing center machine room energy-saving control system.
Cloud computing center machine room energy-saving control system of the present utility model, it comprises central controller, data acquisition unit, data video-stream processor, power subsystem, CPU module, input/output module, environment centralized supervision and managing power consumption module, intelligent console, system control box, air inlet fan, blowing machine；Described central controller comprises power subsystem, CPU module, input/output module；Described central controller is connected with data acquisition unit, data video-stream processor；Described environment centralized supervision and managing power consumption module are connected with central controller；Described intelligent console is connected with each other with environment centralized supervision and managing power consumption module；Described system control box and intelligent console are connected with each other；Described air inlet fan and system control box are connected with each other；Described blowing machine and air inlet fan are connected with each other.
As preferably, being provided with outdoor temperature module, indoor temperature module, smoke sensing module, filter differential pressure module in described intelligent console.
As preferably, described CPU module is single-chip microcomputer.
This utility model operational approach is as follows: the equipment in data center is clustered by (1) according to data power consumption values.Equipment close for energy consumption is classified as a class, it is possible to relative analysis goes out the difference of high-energy equipment class and low power consumption equipment class, if rationally.Can be that high-energy equipment class is adjusted by standard according to low power consumption equipment class if unreasonable, thus can be reduced the energy consumption of high-energy equipment, reached energy-conservation purpose.Additionally, the internal unit of each class is analyzed, and owing to the equipment energy consumption of identical device type should be close, and the equipment energy consumption value of distinct device type difference is bigger, so it is observed that the whether abnormal energy consumption equipment of each apoplexy due to endogenous wind, if existed, warping apparatus is adjusted.
(2) equipment in data center is clustered according to the changing value in the time period.By this operation, it is possible to analyze the equipment that energy consumption change is relatively larger, analyze reason, and be evaluated.
(3) rational energy consumption model is set up by the classification of data mining and prediction algorithm.First, the situation of consumption of data center in the past period can be analyzed by model, it is also possible to draw the relation between each attribute of equipment energy consumption.Secondly, consumption of data center in following a period of time can be predicted by model, by adjusting energy consumption equipment property value, contrast prediction curve, it is possible to the curve selecting prediction of energy consumption relatively low carries out equipment adjustment, to the energy-conservation effect serving guidance.Can also classification and prediction be applied in portfolio, set up model, the portfolio that simulation and forecast is following, equipment state can be adjusted by the curve of portfolio, reach energy-conservation purpose equally.Data mining technology includes the technology to data computational analysis such as correlation rule, clustering algorithm, classification and prediction algorithm, and each technology includes again numerous algorithms.Having levels clustering algorithm, the calculation of K mean cluster, CURE algorithm etc. as clustering algorithm, classification and prediction algorithm have NB Algorithm, neural network algorithm, Algorithm of documents categorization etc., these algorithms to have its respective advantage, also have its weak point simultaneously.By the method for tradition energy consumption data analysis is compared and summary, select wherein suitable algorithm to carry out realizing or the advantage of comprehensive several algorithm realizes hybrid algorithm, thus the demand of adaptive system.By setting up energy consumption model, set forth the data mining process of energy consumption analysis, provide rational solution for different situations, be finally reached data center's prediction in a short time, it is possible to find energy-conservation point, provide energy-saving scheme, complete energy-conservation final purpose.
(4) Airflow Pattern Designing to cloud computing machine room, develops distributing controller, to each rack decentralised control, independently controls for each rack, make air-supply uniform, it is possible to take away machine heat quickly and effectively.
The beneficial effects of the utility model: its reasonable in design, by the energy-conservation point found and energy factor are made the process being suitable for data center, reduce consumption of data center, optimize the level of resources utilization simultaneously.
Accompanying drawing explanation
For ease of illustrating, this utility model is embodied as and accompanying drawing is described in detail by following.
Fig. 1 is structural representation of the present utility model；
Fig. 2 is the schematic diagram of intelligent console in this utility model；
In figure:
Central controller 1, data acquisition unit 2, data video-stream processor 3, power subsystem 101, CPU module 102, input/output module 103, environment centralized supervision and managing power consumption module 4, intelligent console 5, system control box 6, air inlet fan 7, blowing machine 8, outdoor temperature module 9, indoor temperature module 10, smoke sensing module 11, filter differential pressure module 12.
Detailed description of the invention
As shown in Figure 1 to Figure 2, this detailed description of the invention is by the following technical solutions: it comprises central controller 1, data acquisition unit 2, data video-stream processor 3, power subsystem 101, CPU module 102, input/output module 103, environment centralized supervision and managing power consumption module 4, intelligent console 5, system control box 6, air inlet fan 7, blowing machine 8；Described central controller 1 comprises power subsystem 101, CPU module 102, input/output module 103；Described central controller 1 is connected with data acquisition unit 2, data video-stream processor 3；Described environment centralized supervision and managing power consumption module 4 are connected with central controller 1；Described intelligent console 5 is connected with each other with environment centralized supervision and managing power consumption module 4；Described system control box 6 is connected with each other with intelligent console 5；Described air inlet fan 7 is connected with each other with system control box 6；Described blowing machine 8 is connected with each other with air inlet fan 7.
As preferably, being provided with outdoor temperature module 9, indoor temperature module 10, smoke sensing module 11, filter differential pressure module 12 in described intelligent console 5.
As preferably, described CPU module 102 is single-chip microcomputer.
This utility model operational approach is as follows: the equipment in data center is clustered by (1) according to data power consumption values.Equipment close for energy consumption is classified as a class, it is possible to relative analysis goes out the difference of high-energy equipment class and low power consumption equipment class, if rationally.Can be that high-energy equipment class is adjusted by standard according to low power consumption equipment class if unreasonable, thus can be reduced the energy consumption of high-energy equipment, reached energy-conservation purpose.Additionally, the internal unit of each class is analyzed, and owing to the equipment energy consumption of identical device type should be close, and the equipment energy consumption value of distinct device type difference is bigger, so it is observed that the whether abnormal energy consumption equipment of each apoplexy due to endogenous wind, if existed, warping apparatus is adjusted.
(2) equipment in data center is clustered according to the changing value in the time period.By this operation, it is possible to analyze the equipment that energy consumption change is relatively larger, analyze reason, and be evaluated.
(3) rational energy consumption model is set up by the classification of data mining and prediction algorithm.First, the situation of consumption of data center in the past period can be analyzed by model, it is also possible to draw the relation between each attribute of equipment energy consumption.Secondly, consumption of data center in following a period of time can be predicted by model, by adjusting energy consumption equipment property value, contrast prediction curve, it is possible to the curve selecting prediction of energy consumption relatively low carries out equipment adjustment, to the energy-conservation effect serving guidance.Can also classification and prediction be applied in portfolio, set up model, the portfolio that simulation and forecast is following, equipment state can be adjusted by the curve of portfolio, reach energy-conservation purpose equally.Data mining technology includes the technology to data computational analysis such as correlation rule, clustering algorithm, classification and prediction algorithm, and each technology includes again numerous algorithms.Having levels clustering algorithm, the calculation of K mean cluster, CURE algorithm etc. as clustering algorithm, classification and prediction algorithm have NB Algorithm, neural network algorithm, Algorithm of documents categorization etc., these algorithms to have its respective advantage, also have its weak point simultaneously.By the method for tradition energy consumption data analysis is compared and summary, select wherein suitable algorithm to carry out realizing or the advantage of comprehensive several algorithm realizes hybrid algorithm, thus the demand of adaptive system.By setting up energy consumption model, set forth the data mining process of energy consumption analysis, provide rational solution for different situations, be finally reached data center's prediction in a short time, it is possible to find energy-conservation point, provide energy-saving scheme, complete energy-conservation final purpose.
(4) Airflow Pattern Designing to cloud computing machine room, develops distributing controller, to each rack decentralised control, independently controls for each rack, make air-supply uniform, it is possible to take away machine heat quickly and effectively.
The beneficial effects of the utility model: its reasonable in design, by the energy-conservation point found and energy factor are made the process being suitable for data center, reduce consumption of data center, optimize the level of resources utilization simultaneously.
Of the present utility model ultimate principle and principal character and of the present utility model advantage have more than been shown and described.Skilled person will appreciate that of the industry; this utility model is not restricted to the described embodiments; described in above-described embodiment and description is that principle of the present utility model is described; under the premise without departing from this utility model spirit and scope; this utility model also has various changes and modifications, and these changes and improvements both fall within the scope of claimed this utility model.This utility model claims scope and is defined by appending claims and equivalent thereof.
Claims (3)
1. cloud computing center machine room energy-saving control system, it is characterised in that it comprises central controller, data acquisition unit, data video-stream processor, power subsystem, CPU module, input/output module, environment centralized supervision and managing power consumption module, intelligent console, system control box, air inlet fan, blowing machine；Described central controller comprises power subsystem, CPU module, input/output module；Described central controller is connected with data acquisition unit, data video-stream processor；Described environment centralized supervision and managing power consumption module are connected with central controller；Described intelligent console is connected with each other with environment centralized supervision and managing power consumption module；Described system control box and intelligent console are connected with each other；Described air inlet fan and system control box are connected with each other；Described blowing machine and air inlet fan are connected with each other.
2. cloud computing center machine room energy-saving control system according to claim 1, it is characterised in that be provided with outdoor temperature module, indoor temperature module, smoke sensing module, filter differential pressure module in described intelligent console.
3. cloud computing center machine room energy-saving control system according to claim 1, it is characterised in that described CPU module is single-chip microcomputer.
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| CN201620043321.4U CN205353675U (en) | 2016-01-18 | 2016-01-18 | Cloud calculates center computer room energy -saving control system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106852075A (en) * | 2017-01-20 | 2017-06-13 | 郑州云海信息技术有限公司 | For the water-cooled refrigeration plant control system and control method of data center |
| CN117539726A (en) * | 2024-01-09 | 2024-02-09 | 广东奥飞数据科技股份有限公司 | Energy efficiency optimization method and system for green intelligent computing center |
-
2016
- 2016-01-18 CN CN201620043321.4U patent/CN205353675U/en not_active Expired - Fee Related
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106852075A (en) * | 2017-01-20 | 2017-06-13 | 郑州云海信息技术有限公司 | For the water-cooled refrigeration plant control system and control method of data center |
| CN106852075B (en) * | 2017-01-20 | 2019-02-12 | 郑州云海信息技术有限公司 | Water cooling refrigeration equipment control system and control method for data center |
| CN117539726A (en) * | 2024-01-09 | 2024-02-09 | 广东奥飞数据科技股份有限公司 | Energy efficiency optimization method and system for green intelligent computing center |
| CN117539726B (en) * | 2024-01-09 | 2024-04-26 | 广东奥飞数据科技股份有限公司 | Energy efficiency optimization method and system for green intelligent computing center |
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Granted publication date: 20160629 Termination date: 20170118 |
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