CN115907202A - Data center PUE calculation analysis method and system under double-carbon background - Google Patents
Data center PUE calculation analysis method and system under double-carbon background Download PDFInfo
- Publication number
- CN115907202A CN115907202A CN202211601036.6A CN202211601036A CN115907202A CN 115907202 A CN115907202 A CN 115907202A CN 202211601036 A CN202211601036 A CN 202211601036A CN 115907202 A CN115907202 A CN 115907202A
- Authority
- CN
- China
- Prior art keywords
- data center
- pue
- energy consumption
- function
- calculation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 130
- 238000004458 analytical method Methods 0.000 title claims abstract description 39
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 27
- 238000012937 correction Methods 0.000 claims abstract description 134
- 230000032683 aging Effects 0.000 claims abstract description 120
- 238000005457 optimization Methods 0.000 claims abstract description 46
- 238000012423 maintenance Methods 0.000 claims abstract description 24
- 238000000034 method Methods 0.000 claims abstract description 23
- 238000010276 construction Methods 0.000 claims abstract description 20
- 238000005265 energy consumption Methods 0.000 claims description 168
- 238000005070 sampling Methods 0.000 claims description 36
- 238000012360 testing method Methods 0.000 claims description 18
- 230000006872 improvement Effects 0.000 claims description 11
- 230000004048 modification Effects 0.000 claims description 6
- 238000012986 modification Methods 0.000 claims description 6
- 238000013433 optimization analysis Methods 0.000 claims description 6
- OIGNJSKKLXVSLS-VWUMJDOOSA-N prednisolone Chemical compound O=C1C=C[C@]2(C)[C@H]3[C@@H](O)C[C@](C)([C@@](CC4)(O)C(=O)CO)[C@@H]4[C@@H]3CCC2=C1 OIGNJSKKLXVSLS-VWUMJDOOSA-N 0.000 claims description 6
- 238000012163 sequencing technique Methods 0.000 claims description 5
- 238000007689 inspection Methods 0.000 claims description 4
- 230000010354 integration Effects 0.000 claims description 3
- 238000005192 partition Methods 0.000 claims description 3
- 230000001131 transforming effect Effects 0.000 claims description 3
- 230000002159 abnormal effect Effects 0.000 abstract description 3
- 230000003864 performance function Effects 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000005057 refrigeration Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- 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
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a data center PUE calculation analysis method and system under a double-carbon background, and relates to the technical field of data center management. The method comprises the steps of respectively constructing corresponding aging correction quantity and efficiency correction quantity through data center aging correction function construction and data center efficiency correction function construction; the aging correction quantity can correct the meaningless decrease of the PUE value caused by the aging of IT equipment, and the efficiency correction quantity can correct the problem of abnormal increase of the PUE value caused by calculation optimization; in addition, the embodiment also analyzes historical data related to the PUE to obtain guidance for effective aging maintenance and area optimization of the IT equipment.
Description
Technical Field
The invention relates to the technical field of data center management, in particular to a PUE calculation analysis method and a PUE calculation analysis system for a data center under a double-carbon background.
Background
PUE, power Usage efficiency, i.e., electric energy utilization efficiency, is an index for evaluating energy efficiency of a data center, and is a ratio of all energy consumed by the data center to energy consumed by an IT load. PUE = total energy consumption of data center/energy consumption of IT equipment, wherein the total energy consumption of data center includes energy consumption of IT equipment and energy consumption of systems such as refrigeration and power distribution, and the value is greater than 1, and the closer to 1, the less energy consumption of non-IT equipment is, the better the level of efficiency is.
The conventional PUE calculation and analysis method can theoretically promote the reduction of the energy consumption of data center infrastructure, but because the index cannot provide information about the energy efficiency of IT equipment, the evaluation result has a large deviation from the actual production efficiency of a data center, which is also an important reason for the PUE to cause the defects, and the points to be improved are as follows:
1. the existing PUE theory only considers the proportion of the energy consumption of IT equipment in the total energy consumption of a data center; after a data center for upgrading infrastructure operates for a period of time, the energy consumption of IT equipment is increased due to the aging of the IT equipment; therefore, the energy consumption ratio of the IT equipment is increased, and the PUE value is reduced. However, such a PUE value reduction does not result from optimization of energy consumption of the data center, and the electric energy utilization efficiency of the data center cannot be measured, and if the aging energy consumption is continuously increased, energy consumption waste is greatly caused, and a potential risk of data center operation is increased.
2. The existing PUE theory does not consider the efficiency relation between the IT equipment energy consumption and the total energy consumption of the data center; when IT equipment adopts calculation optimization technologies such as virtualization, the number of IT equipment and energy consumption thereof can be greatly reduced due to the increase of the working efficiency of a single equipment, which is beneficial to a data center. However, since the total energy consumption of the data center and the energy consumption of the IT equipment are not in a simple linear relationship, the total energy consumption is not reduced to the same extent, which causes the PUE value to be increased.
3. The existing calculation definition formula of the PUE theory does not introduce a time concept, and cannot provide a substantial energy-saving direction for a data center.
Therefore, it is necessary to provide a method and a system for PUE calculation and analysis in a data center under a dual carbon background to solve the above technical problems.
Disclosure of Invention
In order to solve one of the technical problems, the PUE calculation and analysis method for the data center under the double-carbon background provided by the invention is characterized in that the PUE value is calculated and adjusted by constructing an optimized PUE model, so that the optimized PUE value closer to the real situation of the data center is obtained; tracking the optimized PUE value and recording historical data related to the PUE; carrying out optimization analysis according to the historical data related to the PUE to obtain an optimization improvement strategy; the method comprises the following specific steps: the method comprises an optimization PUE calculation model building step, an optimization PUE value tracking and recording step and an optimization PUE improvement analysis step.
As a further solution, the step of building the optimized PUE calculation model is used for the following specific sub-steps: the method comprises the steps of data center aging correction function construction, data center efficiency correction function construction, PUE model construction integration and PUE model numerical calculation;
the data center aging correction function is constructed as follows: the aging correction method comprises the steps of evaluating the aging degree of a data center and constructing an aging correction function corresponding to IT equipment; when the specific calculation is carried out, the aging correction function of the IT equipment is calculated to obtain the corresponding data center aging correction amount;
constructing a data center efficiency correction function: the method is used for evaluating the efficiency improvement of the optimization means on the data center and constructing an efficiency correction function corresponding to the IT equipment; when the specific calculation is carried out, the corresponding IT equipment efficiency correction quantity is obtained by calculating the efficiency correction function of the IT equipment;
and integrating the PUE model modification functions: integrating the aging correction function and the efficiency correction function into an original PUE calculation model to obtain an optimized PUE calculation model PUE + ；
And (3) calculating the optimized PUE calculation model value: and (4) bringing specific values of parameters of the optimized PUE calculation model into the optimized PUE calculation model to obtain an optimized PUE value.
As a further solution, the data center aging correction function construction specific steps include: setting an energy consumption function acquisition condition, acquiring an initial energy consumption function, acquiring a current energy consumption function and calculating an aging correction function;
the energy consumption function acquisition condition is set as follows: setting unit sampling time t, setting number of sampling points N and setting maximum working intensity W of data center max Setting a working strength gradient value delta W; wherein, Δ W = W max N; the working strength is defined as the ratio of the calculated amount of the data center in unit time, wherein the unit time is unit sampling time t;
acquiring an initial energy consumption function: under the condition of energy consumption function acquisition, carrying out initial energy consumption test on a data center in an initial stage to obtain a plurality of initial energy consumption data sampling points; connecting the initial energy consumption data sampling points to obtain an initial energy consumption curve; fitting the initial energy consumption curve to obtain an initial energy consumption function IDF;
and acquiring the current energy consumption function: under the condition of energy consumption function acquisition, performing current energy consumption test on the data center at the current stage to obtain a plurality of current energy consumption data sampling points; connecting the current energy consumption data sampling points to obtain a current energy consumption curve; fitting the current energy consumption curve to obtain a current energy consumption function CDF;
the aging correction function calculates: collecting an initial energy consumption function IDF and a current energy consumption function CDF, and calculating an aging correction function ADF; the aging correction function ADF is a difference function of the current energy consumption function CDF and the initial energy consumption function IDF.
As a further solution, the initial energy consumption function collects: setting the working strength of the data center as a control variable, and setting the working energy consumption of the data center as an observation variable; setting different data center working strength values W according to the working strength gradient value delta W i Obtaining a working strength point set W; collecting to obtain W i Corresponding initial operation consumption value E0 of data center i And obtaining an initial working energy consumption point set E0; wherein, W i ＝ΔW*i，i∈[1,2,3....N]；W＝[W 1 ,W 2 ,W 3 ....W N ]；E0＝[E0 1 ,E0 2 ,E0 3 ....E0 N ](ii) a Converting E0 and W from the point set into a corresponding curve E0 (W), and obtaining an initial energy consumption function IDF through fitting: f IDF (W)＝E0(W)；
And acquiring the current energy consumption function: setting the working strength of the data center as a control variable and setting the working energy consumption of the data centerMeasuring a variable; setting different data center working strength values W according to the working strength gradient value delta W i Obtaining a working strength point set W; collecting to obtain W i Corresponding current operation energy consumption value E1 of data center i And obtaining a current working energy consumption point set E1; wherein, W i ＝ΔW*i，i∈[1,2,3....N]；W＝[W 1 ,W 2 ,W 3 ....W N ]；E1＝[E1 1 ,E1 2 ,E1 3 ....E1 N ](ii) a Converting the E1 and the W from the point set into a corresponding curve E1 (W), and obtaining a current energy consumption function IDF through fitting: f CDF (W)＝E1(W)；
The aging correction function calculates: f ADF (W)＝F CDF (W)-F IDF (W)＝E1(W)-E0(W)。
As a further solution, the data center performance modification function construction specifically includes: setting collection conditions of the efficiency function: original efficiency function collection, optimized efficiency function collection and efficiency correction function calculation;
setting the efficiency function acquisition condition: setting a reference calculation force u, setting the number M of working energy consumption sampling points and setting a working energy consumption boundary value E max Setting a calculated gradient value delta d; wherein Δ d = E max (ii) a/M; the working energy consumption is the electric energy consumed for completing the specified calculation amount under the current calculation force, wherein the current calculation force is a reference calculation force u;
collecting the original efficiency function: under the condition of acquiring the efficiency function, closing calculation optimization; carrying out original efficiency test on the data center at the same test stage to obtain a plurality of original efficiency sampling points; connecting the original efficiency sampling points to obtain an original efficiency curve; fitting the original efficiency curve to obtain an original efficiency function OEF;
the optimization efficiency function is collected: starting calculation optimization under the condition of efficiency function acquisition; performing optimization efficiency test on the data center in the same test stage to obtain a plurality of optimization efficiency sampling points; connecting the optimized efficiency sampling points to obtain an optimized efficiency curve; fitting the optimized efficiency curve to obtain an optimized efficiency function MEF;
the performance correction function calculates: collecting an original efficiency function OEF and an optimized efficiency function MEF, and calculating an efficiency correction function CEF; the efficiency correction function CEF is a difference function between the optimized efficiency function MEF and the original efficiency function OEF.
As a further solution, the raw performance function collects: setting the calculated amount of the data center as a control variable, and setting the working energy consumption of the data center as an observation variable; setting different data center calculated quantities d according to the calculated quantity gradient value delta d j Obtaining a workload point set d; collecting reference calculation force u to complete corresponding data center calculation amount d j Data center original operation energy consumption value E j And obtaining an original working energy consumption point set E; wherein d is j ＝Δd*j，i∈[1,2,3....M]；d＝[d 1 ,d 2 ,d 3 ....d m ]；E＝[E 1 ,E 2 ,E 3 ....E m ](ii) a Transforming E and d from the point set to a corresponding curve E (d), obtaining the original potency function OEF by fitting: f OEF (d)＝E(d)；
And acquiring an optimized efficiency function: setting the calculated amount of the data center as a control variable, and setting the working energy consumption of the data center as an observation variable; setting different data center calculated quantities d according to the calculated quantity gradient value delta d j Obtaining a workload point set d; collecting reference calculation force u to complete corresponding data center calculation amount d j Data center optimizing work energy consumption value
And obtaining an optimized working energy consumption point set E'; wherein, d j ＝Δd*j，i∈[1,2,3....M]；d＝[d 1 ,d 2 ,d 3 ....d m ]；
Converting E 'and d from the point set into a corresponding curve E' (d), and obtaining an optimized efficiency function MEF through fitting: f MEF (d)＝E`(d)；
The performance correction function calculates: f CEF (d)＝F MEF (d)-F OEF (d)＝E`(d)-E(d)。
As a further solution, the optimized PUE calculation model PUE obtained by integrating the PUE model correction function + ：
D, U and T respectively represent the total calculation amount, total calculation force and total calculation time for carrying out PUE calculation statistics, and the D is the same as the D unit, the U is the same as the U unit, and the T is the same as the T unit; e Tot The total energy consumption of the data center is obtained; e IT In order to provide the total energy consumption of the IT equipment,
a data center aging correction amount; />
For the average working strength in the PUE calculation, <' >>
The IT equipment performance correction is obtained.
As a further solution, the PUE value trace recording step is configured to store PUE-related historical data, where the PUE-related historical data includes: a historical set of aging correction functions and a set of regional optimization PUE values.
As a further solution, the step of optimizing PUE improvement analysis comprises: IT equipment aging maintenance analysis and data center region optimization analysis, wherein:
the IT equipment aging maintenance analysis comprises the following steps: reading an aging correction function history set, and setting an aging maintenance judgment threshold; under the uniform working intensity W, calculating the aging correction of the data center acquired by each historical node to obtain an aging correction history Set (F) ADF )＝[F ADF (W) 1 ，F ADF (W) 2 ，F ADF (W) 3 ......F ADF (W) k ]Wherein k is the node number; calculate the aging correction increment Set (Δ F) ADF )＝[F ADF (W) 1 -F ADF (W) 2 ，F ADF (W) 2 -F ADF (W) 3 ，......F ADF (W) k-1 -F ADF (W) k ](ii) a Judging aging correction increment Set (delta F) ADF ) Whether there are elements exceeding the aging maintenance determination threshold; if the historical node IT equipment exists, the corresponding historical node IT equipment is aged too fast, and the inspection and maintenance are required to be enhanced; if the aging speed does not exist, the maintenance is proper, and the normal aging speed of the equipment is met;
and optimizing and analyzing the data center area: read region optimized PUE value set
Wherein k is a region number; optimizing sets of PUE values for regions Set (PUE) + ) Sequencing from big to small to obtain a region optimization sequence; and performing partition optimization on the data center according to the region optimization sequence.
A PUE calculation and analysis system of a data center under a double-carbon background is deployed in the data center and executes the PUE calculation and analysis method of the data center under the double-carbon background.
Compared with the related art, the data center PUE calculation analysis method and system under the double-carbon background provided by the invention have the following beneficial effects:
1. the method comprises the steps of respectively constructing corresponding aging correction quantity and efficiency correction quantity through data center aging correction function construction and data center efficiency correction function construction; the aging correction quantity can correct the meaningless decrease of the PUE value caused by the aging of IT equipment, and the efficiency correction quantity can correct the problem of abnormal increase of the PUE value caused by calculation optimization;
2. according to the data center aging correction method, the aging degree can be judged by acquiring the aging correction quantity of the data center through each historical node and solving the corresponding aging correction increment set; if any aging correction increment is larger than the aging maintenance judgment threshold, the aging speed of the IT equipment is too high between the corresponding historical nodes, so that the IT equipment can be corrected only by needing larger aging correction amount, and the daily equipment maintenance can be guided by the aging correction amount;
3. the data center is divided into a plurality of areas and numbered; calculating an optimized PUE value of each independent region, and sequencing from large to small to obtain a region optimized sequence; when the data center is optimized in the PUE value, the areas with the largest influence on the PUE value of the data center can be processed in the most priority mode according to the area optimization sequence, and the data center PUE value can be optimized.
Drawings
Fig. 1 is a schematic general flow chart of a preferred method and system for PUE calculation and analysis of a data center under a dual-carbon background according to an embodiment of the present invention;
FIG. 2 is a comparison diagram of energy consumption functions of a data center PUE calculation analysis method and system under a dual-carbon background according to an embodiment of the present invention;
fig. 3 is a comparison graph of performance functions of the data center PUE calculation analysis method and system under the two-carbon background according to the embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and embodiments.
As shown in fig. 1, in the data center PUE calculation analysis method under the double-carbon background provided in this embodiment, an optimized PUE model is constructed to perform calculation adjustment on a PUE value, so as to obtain an optimized PUE value closer to a real situation of the data center; tracking the optimized PUE value and recording historical data related to the PUE; carrying out optimization analysis according to the historical data related to the PUE to obtain an optimization improvement strategy; the method comprises the following specific steps: the method comprises the steps of building an optimized PUE calculation model, tracking and recording an optimized PUE value and improving and analyzing the optimized PUE.
As a further solution, the step of building an optimized PUE calculation model is used for, the specific sub-steps include: the method comprises the following steps of data center aging correction function construction, data center efficiency correction function construction, PUE model construction integration and PUE model numerical calculation;
the data center aging correction function is constructed as follows: the aging correction method comprises the steps of evaluating the aging degree of a data center and constructing an aging correction function corresponding to IT equipment; when the specific calculation is carried out, the aging correction function of the IT equipment is calculated to obtain the corresponding data center aging correction amount;
constructing a data center efficiency correction function: the method is used for evaluating the efficiency improvement of the optimization means on the data center and constructing an efficiency correction function corresponding to the IT equipment; when the specific calculation is carried out, the corresponding IT equipment efficiency correction quantity is obtained by calculating the efficiency correction function of the IT equipment;
and integrating the PUE model modification functions: integrating the aging correction function and the efficiency correction function into an original PUE calculation model to obtain an optimized PUE calculation model PUE + ；
And (3) performing numerical calculation on the optimized PUE calculation model: and (4) bringing specific values of parameters of the optimized PUE calculation model into the optimized PUE calculation model to obtain an optimized PUE value.
It should be noted that: the existing PUE calculation method only considers the ratio of IT equipment energy consumption to the total energy consumption of the data center, does not consider the efficiency relation between the IT equipment energy consumption and the total energy consumption of the data center, and does not introduce a time concept into a calculation definition formula; optimization improvements are therefore needed; the embodiment provides a data center PUE calculation analysis method under a double-carbon background to solve the problem; respectively constructing corresponding aging correction quantity and efficiency correction quantity through data center aging correction function construction and data center efficiency correction function construction; the aging correction quantity can correct the meaningless decrease of the PUE value caused by the aging of IT equipment, and the efficiency correction quantity can correct the problem of abnormal increase of the PUE value caused by calculation optimization; in addition, the embodiment also analyzes historical data related to the PUE to obtain guidance for effective aging maintenance and area optimization of the IT equipment.
As a further solution, the data center aging correction function construction specific steps include: setting an energy consumption function acquisition condition, acquiring an initial energy consumption function, acquiring a current energy consumption function and calculating an aging correction function;
the energy consumption function isSet condition setting: setting unit sampling time t, setting number of sampling points N and setting maximum working intensity W of data center max Setting a working strength gradient value delta W; wherein, Δ W = W max N; the working strength is defined as the ratio of the calculated amount of the data center in unit time, wherein the unit time is unit sampling time t;
acquiring an initial energy consumption function: under the condition of energy consumption function acquisition, carrying out initial energy consumption test on a data center in an initial stage to obtain a plurality of initial energy consumption data sampling points; connecting the initial energy consumption data sampling points to obtain an initial energy consumption curve; fitting the initial energy consumption curve to obtain an initial energy consumption function IDF;
and acquiring the current energy consumption function: under the condition of energy consumption function acquisition, performing current energy consumption test on the data center at the current stage to obtain a plurality of current energy consumption data sampling points; connecting the current energy consumption data sampling points to obtain a current energy consumption curve; fitting the current energy consumption curve to obtain a current energy consumption function CDF;
the aging correction function calculates: collecting an initial energy consumption function IDF and a current energy consumption function CDF, and calculating an aging correction function ADF; the aging correction function ADF is a difference function of the current energy consumption function CDF and the initial energy consumption function IDF.
It should be noted that: because the energy consumption of the IT equipment is increased due to the aging of the IT equipment, and the increasing relation is not simple linear correlation, an initial energy consumption function needs to be acquired and then compared with the current energy consumption function, so that an aging correction function is obtained; the energy consumption increase value caused by aging under different working strengths can be obtained through the aging correction function, and the influence of the energy consumption increase value on the calculation of the PUE value can be corrected by taking the energy consumption increase value as the aging correction value.
As shown in fig. 2, it can be clearly seen that, during standby, the initial energy consumption function IDF and the current energy consumption function CDF both have standby initial values, but the energy consumption is not much different; however, as the working strength increases, the equipment load also increases, and the energy consumption influence caused by aging increases sharply, so that the influence needs to be eliminated when the PUE value is calculated.
As a further solution, the initial energy consumption function collects: setting the working strength of the data center as a control variable, and setting the working energy consumption of the data center as an observation variable; setting different data center working strength values W according to the working strength gradient value delta W i Obtaining a working strength point set W; collecting to obtain W i Corresponding initial operation consumption value E0 of data center i And obtaining an initial working energy consumption point set E0; wherein, W i ＝ΔW*i，i∈[1,2,3....N]；W＝[W 1 ,W 2 ,W 3 ....W N ]；E0＝[E0 1 ,E0 2 ,E0 3 ....E0 N ](ii) a Converting E0 and W from the point set into a corresponding curve E0 (W), and obtaining an initial energy consumption function IDF through fitting: f IDF (W)＝E0(W)；
And acquiring the current energy consumption function: setting the working strength of the data center as a control variable, and setting the working energy consumption of the data center as an observation variable; setting different data center working strength values W according to the working strength gradient value delta W i Obtaining a working strength point set W; collecting to obtain W i Corresponding current operation consumption value E1 of data center i And obtaining a current working energy consumption point set E1; wherein, W i ＝ΔW*i，i∈[1,2,3....N]；W＝[W 1 ,W 2 ,W 3 ....W N ]；E1＝[E1 1 ,E1 2 ,E1 3 ....E1 N ](ii) a Converting the E1 and the W from the point set into a corresponding curve E1 (W), and obtaining a current energy consumption function IDF through fitting: f CDF (W)＝E1(W)；
The aging correction function calculates: f ADF (W)＝F CDF (W)-F IDF (W)＝E1(W)-E0(W)。
It should be noted that: since function operation is required, it is necessary to ensure that external conditions are the same except for a control variable and an observation variable, and therefore, the collection conditions of the energy consumption function are set to be unified.
As a further solution, the data center performance modification function construction specifically includes: setting the collection condition of the efficiency function: acquiring an original efficiency function, acquiring an optimized efficiency function and calculating an efficiency correction function;
setting the efficiency function acquisition condition: setting a reference calculation force u, setting the number M of working energy consumption sampling points and setting a working energy consumption boundary value E max Setting a calculated gradient value delta d; wherein Δ d = E max (ii) a/M; the working energy consumption is the electric energy consumed for completing the specified calculation amount under the current calculation force, wherein the current calculation force is a reference calculation force u;
collecting the original efficiency function: under the condition of acquiring the efficiency function, closing calculation optimization; carrying out original efficiency test on the data center at the same test stage to obtain a plurality of original efficiency sampling points; connecting the original efficiency sampling points to obtain an original efficiency curve; fitting the original efficiency curve to obtain an original efficiency function OEF;
and acquiring an optimized efficiency function: starting calculation optimization under the condition of efficiency function acquisition; performing optimization efficiency test on the data center in the same test stage to obtain a plurality of optimization efficiency sampling points; connecting the optimized efficiency sampling points to obtain an optimized efficiency curve; fitting the optimized efficiency curve to obtain an optimized efficiency function MEF;
the performance correction function calculates: collecting an original efficiency function OEF and an optimized efficiency function MEF, and calculating an efficiency correction function CEF; the efficiency correction function CEF is a difference function between the optimized efficiency function MEF and the original efficiency function OEF.
It should be noted that: the method for constructing the efficiency correction function is similar to that of the aging correction function, except that the variables are different and the acquisition conditions of the efficiency function are different, and the PUE value caused by the performance deviation can be abnormally increased through the different conditions.
As shown in fig. 3, the original performance function and the optimized performance function have almost no difference in energy consumption when there is no calculation task, but as the calculation amount increases, the difference in energy consumption between the two functions is gradually increased; at the working energy consumption boundary value E max The limit of the calculation task of the calculation unit is reached, and the calculation optimized meter is easily seen hereThe calculation amount is much more than that of the unoptimized calculation, which affects the calculation of the PUE value, but the PUE value is increased, so that the influence needs to be eliminated.
As a further solution, the raw performance function collects: setting the calculated amount of the data center as a control variable, and setting the working energy consumption of the data center as an observation variable; setting different data center calculated quantities d according to the calculated quantity gradient value delta d j Obtaining a workload point set d; collecting reference calculation force u to complete corresponding data center calculation amount d j Data center original operation energy consumption value E j And obtaining an original working energy consumption point set E; wherein d is j ＝Δd*j，i∈[1,2,3....M]；d＝[d 1 ,d 2 ,d 3 ....d m ]；E＝[E 1 ,E 2 ,E 3 ....E m ](ii) a Transforming E and d from the point set to a corresponding curve E (d), obtaining the original potency function OEF by fitting: f OEF (d)＝E(d)；
And acquiring an optimized efficiency function: setting the calculated amount of the data center as a control variable, and setting the working energy consumption of the data center as an observation variable; setting different data center calculated quantities d according to the calculated quantity gradient value delta d j Obtaining a workload point set d; collecting reference calculation force u to complete corresponding data center calculation amount d j Data center optimizing work energy consumption value
And obtaining an optimized working energy consumption point set E'; wherein d is j ＝Δd*j，i∈[1,2,3....M]；d＝[d 1 ,d 2 ,d 3 ....d m ]；
Converting E 'and d from the point set into a corresponding curve E' (d), and obtaining an optimized efficiency function MEF through fitting: f MEF (d)＝E`(d)；
The performance correction function calculates: f CEF (d)＝F MEF (d)-F OEF (d)＝E`(d)-E(d)。
As a further solutionAnd the optimized PUE calculation model PUE obtained by integrating the PUE model correction function + ：
D, U and T respectively represent the total calculation amount, total calculation force and total calculation time for carrying out PUE calculation statistics, and the D is the same as the D unit, the U is the same as the U unit, and the T is the same as the T unit; e Tot The total energy consumption of the data center is obtained; e IT In order to provide the total energy consumption of the IT equipment,
a data center age correction; />
For the average working strength in the PUE calculation, <' >>
The IT equipment performance correction is obtained.
It should be noted that: the calculation is based on the case where the units are the same, and normalization processing is also required if the units are different, and the obtained transformation expressions may be different, but the results are the same.
As a further solution, the PUE value trace recording step is configured to store PUE-related historical data, where the PUE-related historical data includes: a historical set of aging correction functions and a set of regional optimization PUE values.
As a further solution, the optimized PUE improvement analysis step comprises: IT equipment aging maintenance analysis and data center region optimization analysis, wherein:
the IT equipment aging maintenance analysis comprises the following steps: reading a historical collection of aging correction functions, and setting an aging maintenance judgment threshold; under the uniform working intensity W, calculating the aging correction of the data center acquired by each historical node to obtain an aging correction history Set (F) ADF )＝[F ADF (W) 1 ，F ADF (W) 2 ，F ADF (W) 3 ......F ADF (W) k ]Wherein k is the node number; calculate the aging correction increment Set (Δ F) ADF )＝[F ADF (W) 1 -F ADF (W) 2 ，F ADF (W) 2 -F ADF (W) 3 ，......F ADF (W) k-1 -F ADF (W) k ](ii) a Judging aging correction increment Set (delta F) ADF ) Whether there are elements exceeding the aging maintenance determination threshold; if yes, corresponding historical node IT equipment is aged too fast, and inspection and maintenance are required to be strengthened; if the aging speed does not exist, the maintenance is proper, and the normal aging speed of the equipment is met;
it should be noted that: in the embodiment, the aging degree can be judged by acquiring the aging correction amount of the data center through each historical node and solving the corresponding aging correction increment set; if any aging correction increment is larger than the aging maintenance judgment threshold, IT indicates that the aging speed of the IT equipment is too high between corresponding historical nodes, so that a larger aging correction amount is needed to correct the IT equipment, and people can guide the daily equipment maintenance through the aging correction amount. Such as: and (3) recently, if the aging speed of the IT equipment is too high, performing line inspection on each equipment of the data center, and avoiding energy consumption increase and eliminating potential risks.
And optimizing and analyzing the data center area: read region optimized PUE value set
Wherein k is a region number; optimizing sets of PUE values for regions Set (PUE) + ) Sequencing from big to small to obtain a region optimization sequence; and performing partition optimization on the data center according to the region optimization sequence.
It should be noted that: dividing a data center into a plurality of areas and numbering the areas; calculating an optimized PUE value of each independent region, and sequencing from large to small to obtain a region optimized sequence; when the data center is optimized in the PUE value, the areas with the largest influence on the PUE value of the data center can be processed in the most priority mode according to the area optimization sequence, and the data center PUE value can be optimized.
A PUE calculation and analysis system of a data center under a double-carbon background is deployed in the data center and executes the PUE calculation and analysis method of the data center under the double-carbon background.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A data center PUE calculation analysis method under a double-carbon background is characterized in that a PUE value is calculated and adjusted by constructing an optimized PUE model, and the optimized PUE value closer to the real situation of a data center is obtained; tracking the optimized PUE value and recording historical data related to the PUE; carrying out optimization analysis according to the historical data related to the PUE to obtain an optimization improvement strategy; the method comprises the following specific steps: the method comprises the steps of building an optimized PUE calculation model, tracking and recording an optimized PUE value and improving and analyzing the optimized PUE.
2. The data center PUE calculation analysis method under the double-carbon background according to claim 1, wherein the optimization PUE calculation model construction step is used for specifically comprising the following substeps: the method comprises the following steps of data center aging correction function construction, data center efficiency correction function construction, PUE model construction integration and PUE model numerical calculation;
the data center aging correction function is constructed as follows: the aging correction method comprises the steps of evaluating the aging degree of a data center and constructing an aging correction function corresponding to IT equipment; when the specific calculation is carried out, the aging correction function of the IT equipment is calculated to obtain the corresponding data center aging correction amount;
constructing a data center efficiency correction function: the method is used for evaluating the efficiency improvement of the optimization means on the data center and constructing an efficiency correction function corresponding to the IT equipment; when specific calculation is carried out, the corresponding IT equipment efficiency correction quantity is obtained by calculating the efficiency correction function of the IT equipment;
and integrating the PUE model modification functions: integrating the aging correction function and the efficiency correction function into an original PUE calculation model to obtain an optimized PUE calculation model PUE + ；
And (3) performing numerical calculation on the optimized PUE calculation model: and substituting specific values of each parameter of the optimized PUE calculation model to obtain an optimized PUE value.
3. The data center PUE calculation and analysis method under the double-carbon background according to claim 2, wherein the concrete steps of constructing the data center aging correction function include: setting an energy consumption function acquisition condition, acquiring an initial energy consumption function, acquiring a current energy consumption function and calculating an aging correction function;
the energy consumption function acquisition condition is set as follows: setting unit sampling time t, setting number of sampling points N and setting maximum working intensity W of data center max Setting a working strength gradient value delta W; wherein, Δ W = W max N; the working strength is defined as the ratio of the calculated amount of the data center in unit time, wherein the unit time is unit sampling time t;
acquiring an initial energy consumption function: under the condition of energy consumption function acquisition, carrying out initial energy consumption test on a data center in an initial stage to obtain a plurality of initial energy consumption data sampling points; connecting the initial energy consumption data sampling points to obtain an initial energy consumption curve; fitting the initial energy consumption curve to obtain an initial energy consumption function IDF;
collecting the current energy consumption function: under the condition of energy consumption function acquisition, performing current energy consumption test on the data center at the current stage to obtain a plurality of current energy consumption data sampling points; connecting the current energy consumption data sampling points to obtain a current energy consumption curve; fitting the current energy consumption curve to obtain a current energy consumption function CDF;
the aging correction function calculates: collecting an initial energy consumption function IDF and a current energy consumption function CDF, and calculating an aging correction function ADF; the aging correction function ADF is a difference function of the current energy consumption function CDF and the initial energy consumption function IDF.
4. The PUE calculation and analysis method for data centers under the double-carbon background according to claim 3,
acquiring an initial energy consumption function: setting the working strength of the data center as a control variable, and setting the working energy consumption of the data center as an observation variable; setting different data center working strength values W according to the working strength gradient value delta W i Obtaining a working strength point set W; collecting to obtain W i Corresponding initial operation consumption value E0 of data center i And obtaining an initial working energy consumption point set E0; wherein, W i ＝ΔW*i，i∈[1,2,3....N]；W＝[W 1 ,W 2 ,W 3 ....W N ]；E0＝[E0 1 ,E0 2 ,E0 3 ....E0 N ](ii) a Converting the E0 and the W from the point set into a corresponding curve E0 (W), and obtaining an initial energy consumption function IDF through fitting: f IDF (W)＝E0(W)；
And acquiring the current energy consumption function: setting the working strength of the data center as a control variable, and setting the working energy consumption of the data center as an observation variable; setting different data center working strength values W according to the working strength gradient value delta W i Obtaining a working strength point set W; collecting to obtain W i Corresponding current operation consumption value E1 of data center i And obtaining a current working energy consumption point set E1; wherein, W i ＝ΔW*i，i∈[1,2,3....N]；W＝[W 1 ,W 2 ,W 3 ....W N ]；E1＝[E1 1 ,E1 2 ,E1 3 ....E1 N ](ii) a Converting the E1 and the W from the point set into a corresponding curve E1 (W), and obtaining a current energy consumption function IDF through fitting: f CDF (W)＝E1(W)；
The aging correction function calculates: f ADF (W)＝F CDF (W)-F IDF (W)＝E1(W)-E0(W)。
5. The data center PUE calculation analysis method under the double-carbon background according to claim 4, wherein the specific steps of constructing the data center effectiveness correction function include: setting collection conditions of the efficiency function: acquiring an original efficiency function, acquiring an optimized efficiency function and calculating an efficiency correction function;
the efficiency function acquisition condition is set as follows: setting a reference calculation force u, setting the number M of working energy consumption sampling points and setting a working energy consumption boundary value E max Setting a calculated gradient value delta d; wherein Δ d = E max (ii) a/M; the working energy consumption is the electric energy consumed for completing the specified calculation amount under the current calculation force, wherein the current calculation force is a reference calculation force u;
collecting the original efficiency function: under the condition of acquiring the efficiency function, closing calculation optimization; performing original efficiency test on the data center in the same test stage to obtain a plurality of original efficiency sampling points; connecting the original efficiency sampling points to obtain an original efficiency curve; fitting the original efficiency curve to obtain an original efficiency function OEF;
the following steps: starting calculation optimization under the condition of efficiency function acquisition; performing optimization efficiency test on the data center in the same test stage to obtain a plurality of optimization efficiency sampling points; connecting the optimized efficiency sampling points to obtain an optimized efficiency curve; fitting the optimized efficiency curve to obtain an optimized efficiency function MEF;
the performance correction function calculates: collecting an original efficiency function OEF and an optimized efficiency function MEF, and calculating an efficiency correction function CEF; the efficiency correction function CEF is a difference function between the optimized efficiency function MEF and the original efficiency function OEF.
6. The PUE calculation and analysis method for data centers under the double-carbon background according to claim 5,
collecting the original efficiency function: setting the calculated amount of the data center as a control variable, and setting the working energy consumption of the data center as an observation variable; setting different data center calculated quantities d according to the calculated quantity gradient value delta d j Obtaining a workload point set d; collecting reference calculation force u to complete corresponding data center calculation amount d j Data center original operation energy consumption value E j And obtaining the original work consumptionE, energy point set; wherein, d j ＝Δd*j，i∈[1,2,3....M]；d＝[d 1 ,d 2 ,d 3 ....d m ]；E＝[E 1 ,E 2 ,E 3 ....E m ](ii) a Transforming E and d from the point set into a corresponding curve E (d), and obtaining an original efficiency function OEF by fitting: f OEF (d)＝E(d)；
And acquiring an optimized efficiency function: setting the calculated amount of the data center as a control variable, and setting the working energy consumption of the data center as an observation variable; setting different data center calculated quantities d according to the calculated quantity gradient value delta d j Obtaining a workload point set d; collecting reference calculation force u to complete corresponding data center calculation amount d j The data center optimization operation consumption value E ″) j And obtaining an optimized working energy consumption point set E'; wherein d is j ＝Δd*j，i∈[1,2,3....M]；d＝[d 1 ,d 2 ,d 3 ....d m ]；E`＝[E` 1 ,E` 2 E` 3 ....E` M ](ii) a And converting the E, the d and the point set into a corresponding curve E' (d), and obtaining an optimized efficiency function MEF through fitting: f MEF (d)＝E`(d)；
The performance correction function calculates: f CEF (d)＝F MEF (d)-F OEF (d)＝E`(d)-E(d)。
7. The data center PUE calculation analysis method under the double-carbon background according to claim 6, characterized in that the optimized PUE calculation model PUE obtained by integrating the PUE model correction functions + ：
D, U and T respectively represent the total calculation amount, total calculation force and total calculation time for carrying out PUE calculation statistics, and the D is the same as the D unit, the U is the same as the U unit, and the T is the same as the T unit; e Tot The total energy consumption of the data center is achieved; e IT In order to provide the total energy consumption of the IT equipment,
a data center aging correction amount; />
For the mean working intensity in the PUE calculation>
F CEF And (D/U) is the IT equipment performance correction quantity.
8. The method for PUE calculation and analysis of a data center under a two-carbon background according to claim 7, wherein the PUE value trace recording step is used for storing PUE-related historical data, and the PUE-related historical data comprises: a historical set of aging correction functions and a set of regional optimization PUE values.
9. The data center PUE calculation analysis method under the double-carbon background according to claim 1, wherein the optimized PUE improvement analysis step comprises: IT equipment aging maintenance analysis and data center region optimization analysis, wherein:
the IT equipment aging maintenance analysis comprises the following steps: reading an aging correction function history set, and setting an aging maintenance judgment threshold; under the unified working intensity W, calculating the aging correction of the data center acquired by each historical node to obtain an aging correction history Set (F) ADF )＝[F ADF (W) 1 ，F ADF (W) 2 ，F ADF (W) 3 ......F ADF (W) k ]Wherein k is a node number; calculate the aging correction increment Set (Δ F) ADF )＝[F ADF (W) 1 -F ADF (W) 2 ，F ADF (W) 2 -F ADF (W) 3 ，......F ADF (W) k-1 -F ADF (W) k ](ii) a Judging aging correction increment Set (delta F) ADF ) Whether there are elements exceeding the aging maintenance determination threshold; if yes, corresponding historical node IT equipment is aged too fast, and inspection and maintenance are required to be strengthened; if not, it is said to beThe maintenance is clear and proper, and the normal aging speed of the equipment is met;
and optimizing and analyzing the data center area: read region optimized PUE value set
Wherein k is a region number; optimizing sets of PUE values for regions Set (PUE) + ) Sequencing from big to small to obtain a region optimization sequence; and performing partition optimization on the data center according to the region optimization sequence.
10. A PUE calculation analysis system for a data center under a dual-carbon background, which is deployed in the data center and performs the PUE calculation analysis method for the data center under the dual-carbon background according to any one of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211601036.6A CN115907202B (en) | 2022-12-13 | 2022-12-13 | Data center PUE (physical distribution element) calculation analysis method and system under double-carbon background |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211601036.6A CN115907202B (en) | 2022-12-13 | 2022-12-13 | Data center PUE (physical distribution element) calculation analysis method and system under double-carbon background |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115907202A true CN115907202A (en) | 2023-04-04 |
| CN115907202B CN115907202B (en) | 2023-10-24 |
Family
ID=86481335
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211601036.6A Active CN115907202B (en) | 2022-12-13 | 2022-12-13 | Data center PUE (physical distribution element) calculation analysis method and system under double-carbon background |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115907202B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100076607A1 (en) * | 2008-08-08 | 2010-03-25 | Osman Ahmed | Data center thermal performance optimization using distributed cooling systems |
| CN102854418A (en) * | 2012-08-27 | 2013-01-02 | 北京华胜天成科技股份有限公司 | Energy usage effectiveness detector and detection system |
| CN109462223A (en) * | 2018-04-09 | 2019-03-12 | 国网浙江省电力有限公司嘉兴供电公司 | A kind of power quality and analysis method for reliability based on big data |
| CN111752710A (en) * | 2020-06-23 | 2020-10-09 | 中国电力科学研究院有限公司 | Data center PUE dynamic optimization method, system, equipment and readable storage medium |
| WO2020227983A1 (en) * | 2019-05-15 | 2020-11-19 | Alibaba Group Holding Limited | Hybrid-learning neural network architecture |
| US20200409814A1 (en) * | 2019-06-26 | 2020-12-31 | International Business Machines Corporation | Real-time calculation of data center power usage effectiveness |
| CN114810287A (en) * | 2021-06-17 | 2022-07-29 | 长城汽车股份有限公司 | Method and system for correcting LNT (Low-Density LNT) aging and vehicle |
-
2022
- 2022-12-13 CN CN202211601036.6A patent/CN115907202B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100076607A1 (en) * | 2008-08-08 | 2010-03-25 | Osman Ahmed | Data center thermal performance optimization using distributed cooling systems |
| CN102854418A (en) * | 2012-08-27 | 2013-01-02 | 北京华胜天成科技股份有限公司 | Energy usage effectiveness detector and detection system |
| CN109462223A (en) * | 2018-04-09 | 2019-03-12 | 国网浙江省电力有限公司嘉兴供电公司 | A kind of power quality and analysis method for reliability based on big data |
| WO2020227983A1 (en) * | 2019-05-15 | 2020-11-19 | Alibaba Group Holding Limited | Hybrid-learning neural network architecture |
| CN113518962A (en) * | 2019-05-15 | 2021-10-19 | 阿里巴巴集团控股有限公司 | Hybrid learning neural network architecture |
| US20200409814A1 (en) * | 2019-06-26 | 2020-12-31 | International Business Machines Corporation | Real-time calculation of data center power usage effectiveness |
| CN111752710A (en) * | 2020-06-23 | 2020-10-09 | 中国电力科学研究院有限公司 | Data center PUE dynamic optimization method, system, equipment and readable storage medium |
| CN114810287A (en) * | 2021-06-17 | 2022-07-29 | 长城汽车股份有限公司 | Method and system for correcting LNT (Low-Density LNT) aging and vehicle |
Non-Patent Citations (2)
| Title |
|---|
| 杨震 等: ""数据中心PUE能效优化的机器学习方法"", 《***工程理论与实践》, vol. 42, no. 3 * |
| 王少鹏 等: ""数据中心能效指标与能耗模型研究"", 《信息通信技术与政策》, no. 2 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115907202B (en) | 2023-10-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112465239B (en) | Desulfurization system operation optimization method based on improved PSO-FCM algorithm | |
| CN116400126B (en) | Low-voltage power box with data processing system | |
| CN116887569B (en) | Data center energy consumption prediction and energy saving adjustment method, system and storage medium | |
| CN117171590B (en) | Intelligent driving optimization method and system for motor | |
| CN116028887B (en) | Analysis method of continuous industrial production data | |
| CN112836920A (en) | Coal electric unit energy efficiency state evaluation method and device and coal electric unit system | |
| CN118013450B (en) | Photovoltaic optimization system based on total solar radiation calculation | |
| CN117851908B (en) | Improved on-line low-voltage transformer area electric energy meter misalignment monitoring method and device | |
| CN117559542B (en) | Distributed new energy group control group modulation system and method | |
| CN107808209B (en) | Wind power plant abnormal data identification method based on weighted kNN distance | |
| CN115907202A (en) | Data center PUE calculation analysis method and system under double-carbon background | |
| CN115983507B (en) | Method and system for predicting broadband oscillation risk of section of power grid of transmitting end source | |
| CN117595231A (en) | Intelligent power grid distribution management system and method thereof | |
| CN111914000A (en) | Server power capping method and system based on power consumption prediction model | |
| CN112418662A (en) | Power distribution network operation reliability analysis method using artificial neural network | |
| CN109871403B (en) | Industrial big data analysis method based on industrial supply chain | |
| CN116842464A (en) | Battery system SOC estimation method | |
| CN116304949A (en) | Calibration method for energy consumption historical data | |
| CN111983478A (en) | Electrochemical energy storage power station SOC anomaly detection method based on Holt linear trend model | |
| CN113919694A (en) | Method and system for analyzing key service bearing state of power communication optical cable | |
| KR20220061709A (en) | Reward generation method to reduce peak load of electric power and action control apparatus performing the same method | |
| CN113705874B (en) | New energy power grid evolution prediction method, device, computer equipment and storage medium | |
| CN116090593A (en) | Load reduction method for building energy consumption busy/idle conversion period | |
| CN118327883A (en) | Wind farm yaw control method based on wind direction turbulent energy | |
| CN117217417A (en) | Residential electricity regulation potential evaluation method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |