CN113778215A - Method for realizing data center PUE prediction and consumption reduction strategy based on big data - Google Patents
Method for realizing data center PUE prediction and consumption reduction strategy based on big data Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000005265 energy consumption Methods 0.000 claims abstract description 34
- 238000013473 artificial intelligence Methods 0.000 claims abstract description 10
- 238000010801 machine learning Methods 0.000 claims abstract description 10
- 238000004364 calculation method Methods 0.000 claims description 27
- 238000005057 refrigeration Methods 0.000 claims description 22
- 238000004088 simulation Methods 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 abstract description 9
- 238000012544 monitoring process Methods 0.000 abstract description 6
- 238000007405 data analysis Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 abstract description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 12
- 238000001816 cooling Methods 0.000 description 10
- 238000007710 freezing Methods 0.000 description 7
- 230000008014 freezing Effects 0.000 description 7
- 238000005259 measurement Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000005457 optimization Methods 0.000 description 5
- 238000012549 training Methods 0.000 description 4
- 238000011282 treatment Methods 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 2
- 239000000498 cooling water Substances 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000004134 energy conservation Methods 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 238000004378 air conditioning Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013524 data verification Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3234—Power saving characterised by the action undertaken
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/16—Constructional details or arrangements
- G06F1/20—Cooling means
- G06F1/206—Cooling means comprising thermal management
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention provides a method for realizing data center PUE prediction and consumption reduction strategies based on big data, which is characterized in that based on a mature big data analysis technology, scientific detection and utilization are carried out on massive equipment monitoring information, an artificial intelligence technology is used for machine learning, parameters influencing energy consumption are virtually adjusted, a final realization state is simulated, then the future change trend of the data center PUE is predicted, and the consumption reduction strategies are provided.
Description
Technical Field
The invention relates to a data center PUE prediction method, in particular to a method for realizing data center PUE prediction and consumption reduction strategies based on big data.
Background
The data center is used as a basis of digital transformation of various industries, the consumption of energy consumption is always a focus of attention, the energy consumption of the data center is measured by adopting a PUE index in the industry, and the PUE is the total power consumption of the data center/the power consumption of IT equipment in the data center, so that the PUE is always larger than 1.0, how to approach the PUE to 1.0 as far as possible is realized, and the PUE value of the next day or a plurality of days in the future can be predicted according to the existing situation and the weather situation, so that a basis is provided for a decision maker of the data center, and the method is particularly important.
Disclosure of Invention
The invention provides a method for realizing a data center PUE prediction and consumption reduction strategy based on big data, which solves the prediction problem of energy consumption of a data center, and adopts the following technical scheme:
a method for realizing a data center PUE prediction and consumption reduction strategy based on big data comprises the following steps:
s1: defining and decomposing a PUE calculation formula, changing the calculation formula into a calculation mode containing specific projects, and establishing a consumption reduction target;
s2: determining specific items that can be adjusted;
s3: setting adjustable acquisition parameters for specific projects, and acquiring data of the parameters;
s4: influence data of local temperature information is introduced;
s5: virtually adjusting parameters of specific projects by using artificial intelligence, taking the parameters as analog values, and incorporating the analog values into a calculation formula of the PUE for analog trial calculation;
s6: judging whether the virtually adjusted parameters influence the safe operation of the data center or not by combining historical data, and finding out critical points influencing the safe operation of the data center by continuous adjustment;
s7: obtaining a value of future PUE according to the virtual parameters obtained from the critical points;
s8: judging whether the specified consumption reduction target is met or not according to the value of the future PUE and the value of the current PUE;
s9: and displaying the virtual adjustment parameters to generate a consumption reduction strategy and a parameter adjustment list.
Further, in step S1, PUE is total power consumption of the data center/power consumption of the IT equipment in the data center, where the total power consumption of the data center and the power consumption of the IT equipment in the data center are both decomposed into sums of specific items, and the specific items are energy consumption of the IT equipment, energy consumption of refrigeration, energy consumption of lighting, and energy consumption of other offices.
Further, in step S1, the consumption-reduction target is performed in stages, and the difference value is the same for each stage.
Further, in step S2, specific items participating in the PUE are determined, and specific items capable of reducing energy consumption are defined.
Further, in step S3, the parameter is the content of the parameter related to the energy consumption of the specific project.
Further, in step S4, according to the altitude of the location where the data center is located and the weather factor, when the altitude and the weather factor affect the PUE, the influence data of the local temperature information is determined to be introduced.
Further, in step S5, the data of the parameters acquired in step S3 is virtually adjusted by artificial intelligence through machine learning, and the virtually adjusted data of the parameters are included as simulation values in the calculation formula of the PUE for simulation trial calculation.
Further, in step S6, the historical data is the parameter data of the past 12-36 months of the data center.
Further, in step S8, if the consumption reduction target is not met, it is determined whether the safe operation of the data center has been affected, and then the process jumps to step S5 to perform virtual adjustment of the parameters again.
The method for realizing the PUE prediction and consumption reduction strategy of the data center based on the big data is based on a mature big data analysis technology, scientific detection and utilization are carried out on massive equipment monitoring information, an artificial intelligence technology is used for machine learning, parameters influencing energy consumption are virtually adjusted, a final realization state is simulated, then the future change trend of the PUE of the data center is predicted, and the consumption reduction strategy is provided.
Drawings
FIG. 1 is a schematic flow chart of the method for implementing data center PUE prediction and consumption reduction strategies based on big data;
FIG. 2 is a schematic diagram of a power utilization level of a data center;
FIG. 3 is a schematic diagram of a data center power consumption monitoring point;
FIG. 4 is an exemplary diagram of PUE adjustment feature parameters;
FIG. 5 is a schematic diagram of a machine learning fusion model trial calculation;
FIG. 6 is a schematic diagram of a simulated calculation of the PUE;
fig. 7 is a schematic diagram showing device parameters participating in computation by way of a list.
Detailed Description
The energy conservation and consumption reduction of the refrigeration system in the data center are important means for reducing the PUE of the data center. The refrigeration system comprises a water chilling unit, a water pump, a cooling tower, a tail end air conditioner and the like, and as refrigeration and equipment heat dissipation, equipment configuration, a machine room environment and atmospheric conditions are mutually related, after operation and maintenance reach a certain maturity, the requirement for further reducing energy consumption cannot be met by means of hardware energy conservation or simple optimization based on manual experience. The reasons that the energy consumption of the traditional chilled water refrigeration system is high mainly include:
(1) the water temperature is not set reasonably. Once the inlet water temperature at the tail end of the chilled water is set, no adjustment is made in the life cycle. However, such simple setting cannot match the actual service operation condition. According to experience, the efficiency of the water chilling unit is improved by 1-3% when the water temperature is set to be improved by 1 ℃. However, based on manual experience, the water temperature regulation proportion is difficult to accurately calculate and cannot be linked with IT load fluctuation in real time;
(2) the management technology of the group control system is not sufficient. The refrigeration system under the control of the BA system works according to a set target value, the operation efficiency of the refrigeration equipment is low, the whole refrigeration system operates in a low-efficiency interval for a long time, and the temperature of a machine room is unstable due to unreasonable control, so that the energy consumption of the refrigeration system is further improved.
In order to better improve energy efficiency and reduce PUE, end-to-end acquisition monitoring and comprehensive training are required to be carried out on the whole refrigeration link of the data center, so that the system can complete optimal refrigeration parameter adjustment according to the current situation, and the aim of saving energy of the data center is fulfilled. With the wide application of big data technology, the storage and analysis capability of big data is utilized to collect and trial-calculate mass data for the refrigeration energy consumption of the data center, machine learning is carried out by artificial intelligence, and the refrigeration data of the data center is learned, simulated and adjusted, so that calculation is carried out.
The method for realizing the PUE prediction and consumption reduction strategy of the data center based on the big data, provided by the invention, is based on the artificial intelligence data center energy efficiency optimization technology, under the conditions of given weather conditions and the like, by analyzing and cleaning the service of a large amount of data, utilizing AI deep learning reasoning optimization and expert experience optimization, exploring key factors influencing energy consumption, and forming a set of model capable of predicting and optimizing the energy consumption. And the model is applied to a practical system, and the aim of a green data center is achieved through continuous adjustment and optimization. As shown in fig. 1, the method comprises the following steps:
s1: defining PUE calculation formula and making consumption reduction target
The power consumption level of the data center is schematically shown in fig. 2, the total power consumption includes energy consumption W1 of IT equipment, energy consumption W2 of refrigeration, energy consumption W3 of lighting, office and other power consumption W4 and the like, the above energy consumption data are included in a PUE calculation formula of the data center, PUE is the total power consumption W of the data center/the energy consumption W1 of the IT equipment in the data center, and a target value for reducing PUE is established (the target value is ideally limited to be below 1.4), at present, the PUE value of most data centers is above 1.7, and can be reduced to about 1.4 by the method.
The actual meaning of the PUE is to calculate how much power is actually applied to IT equipment in the total power provided to the data center. The larger the PUE value of the data center machine room is, the larger the electric energy consumed by the matched infrastructure such as refrigeration and power supply is. The PUE is simple in definition and easy to operate, and the PUE value of the data center can be calculated only by respectively measuring the total power consumption of the data center and the power consumption of the IT equipment.
For calculating PUE, the total power consumption of the data center and the power consumption of IT equipment need to be measured at the monitoring points of the data center as shown in fig. 3, and the specific measurement points are as follows:
the total power consumption of the data center: under normal conditions, the power of the data center is supplied by the commercial power, and the measuring point is before the commercial power is input into the transformer, namely a point M1 in the figure. In the event of a utility power failure, the power generated by the diesel generator (point M2 in the figure) serves as a measure of the total power consumption of the data center. If the building is a multi-purpose machine room building, other office electricity consumptions measured at the M4 point need to be subtracted from the total electricity consumption calculation of the data center.
The IT equipment consumes power: in a data center, only the power consumption of IT equipment is considered "meaningful" power. Strictly speaking, the power consumption of the IT equipment should be measured and summed at the input power of each IT equipment, but due to the large number of IT equipment, the method greatly increases the measurement workload and the cost. Thus, in actual operation, measurements may be taken at the UPS output or the column head cabinet power input, and the measurements summed as IT equipment power consumption, the measurement point being point M3 in the figure.
Measurement points of PUE index:
after the measurement points are determined, the calculation method of the PUE is as follows according to the definition:
PUE＝(PM1+PM2–PM4)/PM3
where PM1 is the amount of power usage measured at point M1, and so on.
S2: determining areas that can be adjusted
According to the actual situation of the data center, determining the fields participating in adjusting the PUE, such as the energy consumption W2 corresponding to refrigeration, the power consumption W3 of illumination, the illumination, refrigeration and air-conditioning power consumption of office and other power consumption W4, and the like, wherein in most cases, the PUE value of the data center is adjusted and reduced mainly by reducing the energy consumption of a refrigeration system.
S3: mass data collection
The method comprises the following steps of collecting energy consumption condition data of IT equipment, refrigeration equipment, lighting equipment and other energy consumption equipment, calculating a PUE value of a data center, and simultaneously collecting some main parameters influencing and controlling the adjustment of the power of a refrigeration system in real time, such as:
environmental parameters: outdoor climatic conditions (dry bulb temperature, wet bulb temperature, relative humidity), etc.
Controlling parameters: the system comprises a cooling tower approach degree, a cooling water outlet temperature, a cooling water supply and return temperature difference, the number of cooling pumps, a chilled water outlet temperature, the number of coolers, a freezing main pipe pressure difference, the number of freezing pumps, the number of plate exchangers, an air supply temperature of an air conditioner, the maximum and minimum air supply temperature difference and the like.
Derivation parameters: the system comprises a cooling tower fan frequency, a cooling main pipe water outlet quantity, a cooling pump frequency, a freezing main pipe temperature difference, a freezing main pipe water outlet flow, a freezing pump frequency, a freezing main pipe backwater temperature, a cold and hot channel temperature and humidity and the like.
S4: introducing necessary weather factors
According to the climate factor of the data center, whether local temperature information is introduced is determined, for example, when the temperature is reduced, a natural cold source can be used for reducing the temperature. In practice, the invention mainly takes part in the calculation with reference to outdoor climate conditions, such as dry bulb temperature, wet bulb temperature, relative humidity and other parameters.
As shown in fig. 4, the neural network builds a mathematical model that characterizes the relationship between input and output values, by training over a large amount of data. As shown in fig. 5, the adjustment of PUE values is achieved by machine learning a fused mathematical model.
S5: virtual tuning parameters
The machine learning is performed by using artificial intelligence, as shown in fig. 6, power consumption parameters of the air conditioner, such as temperature, outdoor temperature, lighting power consumption, and the like are subjected to system level virtual adjustment, and the power consumption parameters are taken as analog values and are included in a calculation formula of the PUE to perform analog trial calculation, for example, when the temperature is reduced, a fresh air system is turned on, the air conditioner is turned off, and the actual state during parameter adjustment is simulated, and the overall energy consumption of the data center is calculated.
The process of the simulation trial calculation of the adjustment parameters comprises the following steps:
data acquisition: collecting related operation parameters of a freezing station, a tail end air conditioner, an IT load and other systems;
and (3) data governance: using an automatic treatment tool to perform treatments such as dimension reduction, noise reduction, cleaning and the like on the parameters;
characteristic engineering: carrying out correlation analysis on the form after the treatment is finished, and finding out key parameters related to the PUE;
model training: training a PUE model by using high-quality historical data and DNN (deep neural network);
inference decision: and releasing the model to a local reasoning platform, and giving a tuning strategy by combining with real-time data reasoning.
S6: judging whether to influence the safe operation of the data center
After the parameters are virtually adjusted, the safe operation of the data center is judged according to the data collected in the past (the collection period is 12-36 months generally), and if the safe operation is not influenced, the virtual parameters such as the energy consumption data of IT equipment, refrigeration equipment, lighting equipment and other equipment can be continuously adjusted until the critical point influencing the safe operation is found out.
S7: obtaining a value for a future PUE
Through trial calculation, after the value of the critical point of safe operation is found by combining the threshold value of the equipment alarm, the future PUE is calculated, so that the value of the lowest PUE can be predicted.
S8: determining whether the consumption reduction target is met
Through a large amount of experimental data verification, the deviation between the predicted value and the actual PUE value after actual adjustment according to the predicted value is small, and the small deviation can be ignored. And judging whether the consumption reduction target is met or not according to the predicted PUE value, whether the energy consumption reduction PUE reaches 1.41 or not can be achieved, and if the consumption reduction target is not met, judging whether the safe operation is influenced or not. Then, the process proceeds to step S5, and the virtual adjustment parameters are resumed.
S9: generating a consumption reduction strategy and parameter adjustment list
And (3) displaying the parameters of the virtual adjustment, as shown in fig. 7, intuitively displaying the current values and the analog values of the equipment parameters participating in the calculation in a list mode, so that a user can know the machine learning process and the current values and the analog values are used as a strategy basis for reducing energy consumption. All parameters are issued visually, and each energy-saving instruction can give a predicted PUE/CLF value (CLF is a Cooling Load Factor, which is called a Cooling Load coefficient and is defined as the ratio of the power consumption of the data center Cooling equipment to the power consumption of the IT equipment, namely CLF is the power consumption of the Cooling equipment divided by the power consumption of the IT equipment).
The method can scientifically detect and utilize massive equipment monitoring information based on a mature big data analysis technology, performs machine learning by using an artificial intelligence technology, virtually adjusts parameters influencing energy consumption, simulates a final realization state, predicts the future change trend of the PUE of the data center and provides a consumption reduction strategy.
Claims (9)
1. A method for realizing a data center PUE prediction and consumption reduction strategy based on big data comprises the following steps:
s1: defining and decomposing a PUE calculation formula, changing the calculation formula into a calculation mode containing specific projects, and establishing a consumption reduction target;
s2: determining specific items that can be adjusted;
s3: setting adjustable acquisition parameters for specific projects, and acquiring data of the parameters;
s4: influence data of local temperature information is introduced;
s5: virtually adjusting parameters of specific projects by using artificial intelligence, taking the parameters as analog values, and incorporating the analog values into a calculation formula of the PUE for analog trial calculation;
s6: judging whether the virtually adjusted parameters influence the safe operation of the data center or not by combining historical data, and finding out critical points influencing the safe operation of the data center by continuous adjustment;
s7: obtaining a value of future PUE according to the virtual parameters obtained from the critical points;
s8: judging whether the specified consumption reduction target is met or not according to the value of the future PUE and the value of the current PUE;
s9: and displaying the virtual adjustment parameters to generate a consumption reduction strategy and a parameter adjustment list.
2. The method for achieving data center PUE prediction and consumption reduction strategies based on big data according to claim 1, wherein the method comprises the following steps: in step S1, PUE is total power consumption of the data center/power consumption of IT equipment in the data center, where the total power consumption of the data center and the power consumption of the IT equipment in the data center are both decomposed into sums of specific items, and the specific items include power consumption of the IT equipment, power consumption of refrigeration, power consumption of lighting, and other office power consumption.
3. The method for achieving data center PUE prediction and consumption reduction strategies based on big data according to claim 1, wherein the method comprises the following steps: in step S1, the consumption reduction target is performed in stages, and the difference between each stage is the same.
4. The method for achieving data center PUE prediction and consumption reduction strategies based on big data according to claim 1, wherein the method comprises the following steps: in step S2, specific projects participating in the PUE are determined, and specific projects capable of reducing energy consumption are defined.
5. The method for achieving data center PUE prediction and consumption reduction strategies based on big data according to claim 1, wherein the method comprises the following steps: in step S3, the parameter is the content of the parameter related to the energy consumption of the specific project.
6. The method for achieving data center PUE prediction and consumption reduction strategies based on big data according to claim 1, wherein the method comprises the following steps: in step S4, according to the altitude and the weather factor of the location where the data center is located, when the altitude and the weather factor affect the PUE, the influence data of the local temperature information is determined to be introduced.
7. The method for achieving data center PUE prediction and consumption reduction strategies based on big data according to claim 1, wherein the method comprises the following steps: in step S5, the data of the acquired parameters in step S3 are virtually adjusted by artificial intelligence through machine learning, and the virtually adjusted parameter data are included as simulation values in the calculation formula of the PUE for simulation trial calculation.
8. The method for achieving data center PUE prediction and consumption reduction strategies based on big data according to claim 1, wherein the method comprises the following steps: in step S6, the history data is parameter data of the past 12-36 months of the data center.
9. The method for achieving data center PUE prediction and consumption reduction strategies based on big data according to claim 1, wherein the method comprises the following steps: in step S8, if the consumption reduction target is not satisfied, it is determined whether the safe operation of the data center has been affected, and then the process jumps to step S5 to perform virtual adjustment of the parameters again.
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