CN112888268B - Energy-saving control method, device and equipment for data center machine room and storage medium - Google Patents
Energy-saving control method, device and equipment for data center machine room and storage medium Download PDFInfo
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Abstract
The embodiment of the specification provides a method, a device, equipment and a storage medium for controlling energy conservation of a data center machine room, wherein the method comprises the following steps: acquiring a plurality of parameter sets of a data center machine room; each parameter group comprises a power measured value of each IT equipment cabinet and a temperature setting value group corresponding to each refrigeration equipment; respectively inputting a plurality of parameter sets into a preset machine room total power prediction model to obtain a machine room total power prediction value corresponding to each temperature setting value group; carrying out temperature field distribution simulation on a data center machine room by utilizing the temperature set value group to obtain the temperature field distribution of the machine room, and determining a target temperature set value group according to the temperature field distribution; and controlling each refrigerating device according to the target temperature set value group. The embodiment of the specification can reduce the PUE value of the data center on the premise of meeting the requirement of normal operation of IT equipment of a data center machine room.
Description
Technical Field
The present disclosure relates to the technical field of energy saving control of data centers, and in particular, to an energy saving control method, apparatus, device, and storage medium for a data center machine room.
Background
With the rapid development of cloud computing technology, as a physical platform of cloud computing, data centers around the world have also been developed unprecedentedly. The rapidly increasing number of data centers also causes significant energy consumption overhead for operators.
Generally, the energy consumption of a data center is composed of two parts: energy consumption of IT equipment and energy consumption of other auxiliary equipment. The parameter representing the energy efficiency level of the data center is Power Usage Efficiency (PUE), which is defined as the ratio of total energy consumption of the data center to energy consumption of IT equipment. The energy consumption of the refrigeration equipment accounts for most of the energy consumption of other auxiliary equipment in the data center. The energy consumption of the refrigeration equipment is reduced, the energy consumption of the auxiliary equipment can be greatly reduced, the PUE value of the data center is reduced, and the energy efficiency level is improved.
However, the lower the energy consumption of the refrigeration equipment, the better, and when the energy consumption of the refrigeration equipment is too low, the normal operation of the IT equipment can be affected. Therefore, how to reasonably control the refrigeration equipment of the data center machine room to reduce the PUE value of the data center on the premise of satisfying the normal operation of the IT equipment of the data center machine room becomes a technical problem to be solved urgently.
Disclosure of Invention
An embodiment of the present specification aims to provide a method, an apparatus, a device and a storage medium for controlling energy conservation of a data center machine room, so as to reduce a PUE value of a data center on the premise of meeting normal operation of IT equipment of the data center machine room.
In order to achieve the above object, in one aspect, an embodiment of the present specification provides a data center room energy saving control method, including:
acquiring a plurality of parameter sets of a data center machine room; each parameter group comprises a power measured value of each IT equipment cabinet and a temperature setting value group corresponding to each refrigeration equipment;
respectively inputting the plurality of parameter sets into a preset machine room total power prediction model to obtain a machine room total power prediction value corresponding to each temperature setting value group;
performing temperature field distribution simulation on the data center machine room by using the temperature set value group to obtain machine room temperature field distribution;
determining a target temperature setting value group according to the temperature field distribution of the machine room; the target temperature setting value group is a temperature setting value group corresponding to the smallest total power prediction value of the machine room when the temperature of the air inlet of each IT equipment cabinet meets a preset condition;
and controlling each refrigerating device according to the target temperature set value group.
In an embodiment of the present specification, the performing, by using the temperature setpoint set, a temperature field distribution simulation for the data center equipment room includes:
sorting temperature setting value groups corresponding to the total power prediction value of the machine room according to the sequence from small to large of the total power prediction value of the machine room;
and taking out a temperature setting value group from the sequence, and simulating the distribution of the machine room temperature field of the data center machine room under the temperature setting value group by using a preset machine room temperature field simulation model.
In an embodiment of this specification, the determining a target temperature setting value group according to the distribution of the machine room temperature field includes:
extracting the air inlet temperature of each IT equipment cabinet from the temperature field distribution of the machine room under the temperature setting value set;
judging whether the air inlet temperature of each IT equipment cabinet is within a preset cabinet air inlet temperature range or not;
and when the temperature of the air inlet of each IT equipment cabinet is within the temperature range of the air inlet of the cabinet, taking the temperature setting value set as a target temperature setting value set.
In an embodiment of the present specification, the method further includes:
when the temperature of the air inlets of the IT equipment cabinets is out of the range of the temperature of the air inlets of the cabinets, taking out the next temperature setting value group from the sequence in sequence, and simulating the machine room temperature field distribution of the data center machine room under the next temperature setting value group by using the machine room temperature field simulation model;
and determining a target temperature setting value set according to the machine room temperature field distribution of the next temperature setting value set.
In an embodiment of this specification, the acquiring multiple parameter sets of the data center room includes:
and when the data center machine room meets the energy-saving control starting condition, acquiring a plurality of parameter sets of the data center machine room.
In an embodiment of the present specification, the data center room satisfies an energy saving control start condition, and includes one or more of the following:
the change rate of the measured value of the total power of the IT equipment cabinets of the data center machine room exceeds a preset change rate threshold;
local hot spots appear in the data center machine room;
and reaching the energy-saving optimization time preset for the data center machine room.
In an embodiment of this specification, the total power prediction model of the machine room is obtained in advance by:
acquiring historical data of power measured values of the IT equipment cabinets, historical data of temperature set values of the refrigeration equipment and historical data of total power measured values of machine rooms of the data center machine room;
and training a neural network model by taking the historical data of the power measured values and the historical data of the temperature set values as input and the historical data of the total power measured values of the machine room as target output to obtain a total power prediction model of the machine room.
In an embodiment of the present specification, the neural network model includes a deep learning neural network model.
In an embodiment of the present specification, the machine room temperature field simulation model includes a CFD simulation model.
On the other hand, an embodiment of the present specification further provides an energy saving control device for a data center machine room, including:
the acquisition module is used for acquiring a plurality of parameter sets of the data center machine room; each parameter group comprises a power measured value of each IT equipment cabinet and a temperature setting value group corresponding to each refrigeration equipment;
the prediction module is used for respectively inputting the plurality of parameter groups into a preset machine room total power prediction model to obtain a machine room total power prediction value corresponding to each temperature setting value group;
the simulation module is used for simulating the temperature field distribution of the data center machine room by utilizing the temperature set value group to obtain the temperature field distribution of the machine room;
the determining module is used for determining a target temperature setting value group according to the temperature field distribution of the machine room; the target temperature setting value group is a temperature setting value group corresponding to the smallest total power prediction value of the machine room when the temperature of the air inlet of each IT equipment cabinet meets a preset condition;
and the control module is used for controlling each refrigerating device according to the target temperature setting value group.
In another aspect, the embodiments of the present specification further provide a computer device, which includes a memory, a processor, and a computer program stored on the memory, and when the computer program is executed by the processor, the computer program executes the instructions of the above method.
In another aspect, the present specification further provides a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor of a computer device to execute the instructions of the method.
As can be seen from the technical solutions provided by the embodiments of the present specification, in the embodiments of the present specification, a total power prediction value of a machine room corresponding to each temperature setting value group can be obtained by inputting a plurality of parameter groups of a data center machine room into a total power prediction model of the machine room respectively; the temperature field distribution of the data center machine room is simulated by utilizing the temperature set value group, so that the temperature field distribution of the machine room can be obtained; on the basis, the temperature setting value group corresponding to the minimum total power prediction value of the machine room can be selected from the multiple temperature setting value groups according to the temperature field distribution of the machine room, and each refrigeration device is controlled according to the temperature setting value group corresponding to the minimum total power prediction value of the machine room when the temperature of the air inlet of each IT equipment cabinet meets the preset condition; therefore, the energy consumption of the data center is reduced on the premise that the normal operation of the IT equipment of the data center machine room is met, namely, the PUE value of the data center is reduced on the premise that the normal operation of the IT equipment of the data center machine room is met.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort. In the drawings:
fig. 1 shows a flow chart of a method for energy-saving control of a data center room in some embodiments of the present description;
FIG. 2 shows a schematic layout diagram of a data center room in an embodiment of the present specification;
FIG. 3 is a schematic diagram illustrating a total power prediction model for training a computer room in one embodiment of the present disclosure;
FIG. 4 illustrates a flow chart of temperature field distribution simulation and determination of a set of target temperature settings in some embodiments of the present description;
fig. 5 shows a block diagram of a data center room energy-saving control device in some embodiments of the present disclosure;
FIG. 6 shows a block diagram of a computer device in accordance with some embodiments of the present disclosure.
[ description of reference ]
10. A data center machine room;
20. a refrigeration device;
30. an IT equipment cabinet;
51. an acquisition module;
52. a prediction module;
53. a simulation module;
54. a determination module;
55. a control module;
602. a computer device;
604. a processor;
606. a memory;
608. a drive mechanism;
610. an input/output module;
612. an input device;
614. an output device;
616. a presentation device;
618. a graphical user interface;
620. a network interface;
622. a communication link;
624. a communication bus.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.
Data center rooms generally have cooling requirements, and the higher the supply air temperature set value (hereinafter referred to as a temperature set value) of cooling equipment (such as an air conditioner) of the data center room is, the more energy-saving the data center room may be. However, as the temperature setting value is increased, the temperature (hereinafter referred to as air inlet temperature) at the air inlet of the IT equipment cabinet of the data center machine room (i.e., the cabinet of the IT equipment (e.g., server) carrying the data center) is also increased correspondingly, and when the temperature exceeds a certain temperature range; an excessively high temperature tends to accelerate the operation of equipment such as a fan of the IT equipment, thereby increasing the power consumption of the IT equipment. More importantly, the normal operation of the IT equipment is easily affected by the excessive temperature, and even the IT equipment is failed or crashed. Therefore, how to reasonably control the refrigeration equipment of the data center machine room to reduce the PUE value of the data center on the premise of satisfying the normal operation of the IT equipment of the data center machine room becomes a technical problem to be solved urgently.
In view of this, in order to reduce the PUE value of the data center on the premise of satisfying the normal operation of the IT equipment of the data center machine room, the present specification provides a new energy-saving control scheme for the data center machine room.
The present description first provides an embodiment of a data center room energy-saving control method, which may be applied to any suitable computer device. Referring to fig. 1, in some embodiments of the present specification, the method for controlling energy conservation of a data center room may include the following steps:
s101, acquiring a plurality of parameter sets of a data center machine room; each parameter group comprises a power measured value of each IT equipment cabinet and a temperature setting value group corresponding to each refrigeration equipment.
And S102, respectively inputting the plurality of parameter sets into a preset machine room total power prediction model to obtain a machine room total power prediction value corresponding to each temperature setting value group.
S103, simulating the temperature field distribution of the data center machine room by using the temperature set value group to obtain the temperature field distribution of the machine room.
S104, determining a target temperature setting value group according to the distribution of the machine room temperature field; the target temperature setting value group is a temperature setting value group corresponding to the minimum total power prediction value of the machine room when the temperature of the air inlet of each IT equipment cabinet meets the preset condition.
And S105, controlling each refrigerating device according to the target temperature set value.
In the embodiment of the specification, a plurality of parameter sets of a data center machine room are respectively input into a machine room total power prediction model, so that a machine room total power prediction value corresponding to each temperature setting value group can be obtained; the temperature field distribution of the data center machine room is simulated by utilizing the temperature set value group, so that the temperature field distribution of the machine room can be obtained; on the basis, the temperature setting value group corresponding to the minimum total power prediction value of the machine room can be selected from the multiple temperature setting value groups according to the temperature field distribution of the machine room, and each refrigeration device is controlled according to the temperature setting value group corresponding to the minimum total power prediction value of the machine room when the temperature of the air inlet of each IT equipment cabinet meets the preset condition; therefore, the energy consumption of the data center is reduced on the premise that the normal operation of the IT equipment of the data center machine room is met, namely, the PUE value of the data center is reduced on the premise that the normal operation of the IT equipment of the data center machine room is met.
In the embodiment of the present specification, one temperature setting value group corresponding to each refrigeration device in each parameter group means: in a data center room, each refrigeration device is currently set with a set of temperature set points. For example, in the exemplary embodiment shown in fig. 3, the data center room 10 has a rectangular structure, two cooling devices 20 (four cooling devices 20 in total) are respectively installed on the left and right sides of the data center room 10, and 48 regularly arranged IT equipment cabinets 30 are installed in the data center room 10. Assuming that the current temperature setting values of the four refrigeration devices 20 are a ═ 18 ℃, B ═ 18 ℃, C ═ 18 ℃, and D ═ 18 ℃, respectively, then { a ═ 18 ℃, B ═ 18 ℃, C ═ 18 ℃, D ═ 18 ℃ } may be used as a temperature setting value group; assuming that the current temperature setting values of the four refrigeration apparatuses 20 are a ═ 18 ℃, B ═ 19 ℃, C ═ 20 ℃, and D ═ 21 ℃, respectively, then { a ═ 18 ℃, B ═ 19 ℃, C ═ 20 ℃, D ═ 21 ℃ } may be another temperature setting value group.
In this embodiment, the measured power value of each IT equipment rack in each parameter set may be: and (3) power measured values of all IT equipment cabinets in the data center machine room under the same temperature set value group. Also taking the exemplary embodiment shown in fig. 3 as an example, assuming that the temperature setting values of the four refrigeration apparatuses 20 are a being 18 ℃, B being 18 ℃, C being 18 ℃ and D being 18 ℃, respectively, under the temperature setting value group, the power measured values { P11, P12, …, P148} of the 48 IT equipment cabinets 30 in the data center room 10 can be collected. Assuming that the temperature setting values of the four refrigeration devices 20 are a being 18 ℃, B being 19 ℃, C being 20 ℃ and D being 21 ℃, respectively, the power measured values { P21, P22, …, P248} of the 48 IT equipment racks 30 in the data center room 10 can be collected under the temperature setting value set.
Thus, { a ═ 18 ℃, B ═ 18 ℃, C ═ 18 ℃, D ═ 18 ℃ } and { P11, P12, …, P148} may be combined into one parameter set; { a ═ 18 ℃, B ═ 19 ℃, C ═ 20 ℃, D ═ 21 ℃ } and { P21, P22, …, P248} may be combined into another parameter set; and so on.
In some embodiments of the present description, to find the optimal set of temperature settings (i.e., the target set of temperature settings), each possible set of temperature settings can be combined within a selectable range of set temperatures. For example, in an embodiment of the present specification, assuming that there are 5 refrigeration devices in a data center room, the selectable temperature range is set to 18 ℃ to 27 ℃, 1 degree is set as the adjustment range, and there are 10 choices (i.e., 18 ℃, 19 ℃, 20 ℃, 21 ℃, 22 ℃, 23 ℃, 24 ℃, 25 ℃, 26 ℃, 27 ℃) for each refrigeration device. Thus, can be combined out
A set of temperature setting values. Thus, the set of temperature setting values in each parameter set can be determined by a value assignment.
Of course, the above is merely an example, and in other embodiments of the present description, a part of temperature setting value groups may be selected as a candidate set of an optimal temperature setting value group from all possible temperature setting value groups according to actual needs. Therefore, this is not the only limitation in this specification.
In some embodiments of the present description, the Power measured value of each IT equipment cabinet in each parameter group may be acquired by a Power Distribution Unit (PDU) or the like when each refrigeration equipment operates at a corresponding temperature setting value and a temperature field in a data center room reaches a steady state or relatively steady state Distribution.
The total power prediction model of the machine room reflects that in the data center machine room: the corresponding relation among the power of each IT equipment cabinet, the temperature set value of each refrigeration equipment and the total power of the machine room. Therefore, the plurality of parameter sets are respectively input into the preset machine room total power prediction model to obtain the machine room total power prediction value corresponding to each temperature setting value group, namely, each time one parameter set is input into the machine room total power prediction model, a corresponding machine room total power prediction value can be obtained through calculation so as to be used for optimizing the optimal temperature setting value group subsequently.
In some embodiments of the present disclosure, the total power prediction model of the machine room may be a pre-training model, so as to improve the prediction accuracy of the total power prediction model of the machine room. The total power prediction model of the machine room can be obtained by training in the following mode in advance:
1) and acquiring historical data of power measured values of the IT equipment cabinets, historical data of temperature set values of the refrigeration equipment, and historical data of total power measured values of the machine rooms of the data center machine room.
2) And training a neural network model (for example, a deep learning neural network model shown in fig. 2) by taking the historical data of the power measured values and the historical data of the temperature set values as input and the historical data of the total power measured values of the machine room as target output, so as to obtain the total power prediction model of the machine room.
Of course, the deep learning neural network model shown in fig. 2 is only an exemplary illustration, and in other embodiments of the present description, other neural network models (feedforward neural network, convolutional neural network, recursive neural network, etc.), even other suitable machine learning models, and the like may also be employed. Therefore, this is not limited in this specification, and may be selected according to actual needs.
In a data center room, IT equipment configured on a cabinet consumes power and generates heat due to the functions of calculation, storage and the like, and in order to ensure the normal operation of the IT equipment, refrigeration equipment is generally required to be configured to reduce the ambient temperature of the IT equipment. The cold supply of the refrigeration equipment enables the temperature of the air inlet of each cabinet to be kept within a desired temperature range (for example, the temperature range specified by the national standard GB50174 is 18-27 ℃, and the temperature range can be specifically set according to actual conditions). The temperature set value of the refrigeration equipment is adjusted, so that the temperature of the air inlet of each cabinet in the data center machine room can be changed, and the temperature field distribution in the machine room is changed. On the other hand, due to the change of the temperature of the air inlet of the cabinet, the power consumption of the IT equipment can also change.
Therefore, after the total power prediction value of the machine room corresponding to each temperature setting value group is obtained by respectively inputting the plurality of parameter groups into the preset total power prediction model of the machine room, it is not feasible to select the optimal temperature setting value group only from the perspective of the total power of the machine room. That is, the temperature setting value group corresponding to the minimum value of the total power prediction value of the machine room may not necessarily satisfy the temperature range of the air inlet of the cabinet (i.e., may not necessarily satisfy the environmental problem required by the normal operation of the IT equipment). Therefore, the temperature set value group is required to be used for carrying out temperature field distribution simulation on the data center machine room, so as to obtain machine room temperature field distribution, and the target temperature set value group is determined by combining the machine room temperature field distribution.
Generally, the distribution of the temperature field of the machine room in the embodiment of the present specification may refer to spatial distribution of the temperature field of the machine room, so that for any specified point in the machine room (for example, an air inlet position of a cabinet), a corresponding temperature value may be determined according to the distribution of the temperature field of the machine room.
In some embodiments of the present description, a suitable machine room temperature field simulation model may be utilized to realize temperature field distribution simulation for a data center machine room, so as to facilitate reducing implementation cost. For example, in an embodiment of the present description, a computer room temperature field simulation model may be constructed by using a Computational Fluid Dynamics (CFD) simulation model, so as to simulate the temperature field distribution of the data center computer room. In an exemplary embodiment of the present specification, the CFD simulation model may employ Ansys CFX software, Fluent software, Phoenics software, or FloEFD software, etc.
The method for constructing the vertical machine room temperature field simulation model by using the CFD simulation model is to configure parameters of the CFD model before use. In general, the required configuration parameters may include, for example: the physical size of the machine room, the size and the layout of the cabinet, the layout of the refrigeration equipment of the machine room, the layout of IT equipment and the like. Parameters that may be automatically imported through an Application Programming Interface (API) may include: actual measurement power of IT equipment of each cabinet and temperature set values of each refrigeration equipment. After the parameters are imported, the temperature field distribution of the machine room can be obtained through the simulation calculation of the simulation model of the temperature field of the machine room.
Referring to fig. 4, in some embodiments of the present disclosure, the simulating the temperature field distribution of the data center room by using the temperature setpoint set may include:
s401, sorting the temperature setting value groups corresponding to the total power prediction value of the machine room according to the sequence from small to large of the total power prediction value of the machine room.
And the temperature setting value group corresponding to the lower total power prediction value of the machine room has a higher probability of being the optimal temperature setting value group. After the temperature setting value groups corresponding to the total power predicted value of the machine room are sorted according to the sequence from small to large of the total power predicted value of the machine room, the optimal temperature setting value group can be obtained generally by processing the temperature setting value groups which are sorted most in the front. Therefore, the calculation amount can be greatly reduced, and the processing efficiency is improved.
For example, in an exemplary embodiment, the temperature setting value groups corresponding to the total power prediction value of the machine room are sorted in the order from small to large according to the total power prediction value of the machine room, so that the sorting result shown in table 1 below can be obtained.
TABLE 1
S402, one temperature setting value group is taken out from the sequence.
In the embodiments of the present specification, taking a set of temperature setting values in order from the ordering means: and selecting corresponding temperature setting value groups according to the sequence that the total power predicted value of the machine room is from small to large in the sequence. For example, taking Table 1 above as an example, P 12 At a minimum, it is the first order, then P 12 Set of corresponding temperature settings (i.e. { T } 1,12 、T 2,12 、…、T n,12 }) is removed first.
And S403, simulating the machine room temperature field distribution of the data center machine room under the temperature setting value set by using a preset machine room temperature field simulation model.
In the embodiment of the present specification, simulating the machine room temperature field distribution of the data center machine room under the temperature setting value set by using a preset machine room temperature field simulation model refers to: and inputting the selected temperature setting value group and the power measured value of each IT equipment cabinet under the temperature setting value group as parameters into a machine room temperature field simulation model, and performing simulation calculation on the machine room temperature field simulation model to obtain the distribution of the machine room temperature field. In some cases, the machine room temperature field distribution may be a gridded machine room temperature field distribution (in the gridded machine room temperature field distribution, each spatial position may be represented by a corresponding cubic grid carrying temperature values), so that IT may be beneficial to quickly extract the air inlet temperature of the IT equipment cabinet from the machine room temperature field distribution.
Referring to fig. 4, in some embodiments of the present disclosure, the determining a target temperature setting value set according to the machine room temperature field distribution may include:
s404, extracting the air inlet temperature of each IT equipment cabinet from the machine room temperature field distribution under the temperature setting value set.
In the embodiment of the specification, the temperature field distribution reflects the temperature value distribution of all points of the data center computer room. When the air inlet position point of each IT equipment cabinet is appointed, the air inlet temperature of each IT equipment cabinet can be automatically extracted from the machine room temperature field distribution under the temperature setting value set.
S405, judging whether the air inlet temperature of each IT equipment cabinet is within a preset cabinet air inlet temperature range.
The temperature range of the air inlet of the cabinet can be set according to actual conditions, for example, in an embodiment of the present specification, the temperature range specified by the national standard GB50174, which is 18 to 27 ℃, can be used as the temperature range of the air inlet of the cabinet, and IT is determined whether the temperature of the air inlet of each IT equipment cabinet is within the range of 18 to 27 ℃.
S406, when the air inlet temperature of each IT equipment cabinet is within the range of the air inlet temperature of the cabinet, taking the temperature setting value set as a target temperature setting value set.
When the temperature of the air inlet of each IT equipment cabinet is outside the range of the temperature of the air inlet of the cabinet, the above step S402 may be skipped to, that is, the next temperature setting value group may be sequentially taken out from the sequence, and the room temperature field distribution of the data center room under the next temperature setting value group is simulated by using the room temperature field simulation model; and then determining a target temperature setting value set according to the machine room temperature field distribution of the next temperature setting value set.
For example, as exemplified in Table 1Example when P is used 12 Set of corresponding temperature settings (i.e. { T } 1,12 、T 2,12 、…、T n,12 }) and the machine room temperature field simulation model, and finds out that the machine room temperature field distribution of the data center machine room is: the temperature of the air inlet of one or more IT equipment cabinets is within the temperature range of the air inlet of the cabinet, which indicates that: albeit at { T 1,12 、T 2,12 、…、T n,12 Lower data center room is most energy-saving, but cannot meet the temperature environment requirements of all IT equipment cabinets in the data center room, { T } 1,5 、T 2,5 、…、T n,5 Cannot be set as a target temperature setting value. At this time, P can be selected in order 5 Set of corresponding temperature settings (i.e. { T } 1,12 、T 2,12 、…、T n,12 }) continue to find the set of target temperature settings, recursion in order.
In some embodiments of the present specification, the obtaining of the plurality of parameter sets of the data center room may be: and when the data center machine room meets the energy-saving control starting condition, acquiring a plurality of parameter sets of the data center machine room. Therefore, the refrigeration equipment of the data center machine room can be started as required, and further energy conservation of the data center machine room is facilitated. The energy-saving control starting condition of the data center machine room can be set according to actual needs, and the energy-saving control starting condition is not limited in the specification.
For example, in an embodiment of the present specification, the condition that the data center room satisfies the energy saving control start condition may be: the change rate of the measured value of the total power of the IT equipment cabinets in the data center machine room exceeds a preset change rate threshold (for example, 5% and the like). Due to the influence of the external environment and/or the workload of the IT equipment, the total power of the IT equipment cabinets of the data center machine room generally changes dynamically, and when the dynamic change exceeds a certain range, the operation of the IT equipment may be influenced. Therefore, when the change rate of the measured value of the total power of the IT equipment cabinets in the data center machine room exceeds the preset change rate threshold value, the energy-saving control of the data center machine room can be started.
For another example, in an embodiment of the present specification, the condition that the data center machine room satisfies the energy-saving control starting condition may also be: local hot spots appear in the data center machine room.
In a data center machine room, the IT equipment installed in each cabinet has different power consumption (caused by differences in the number of servers, the types of servers, the load of the loaded service, and the like), different machine room environments (caused by differences in the layout of refrigeration equipment at the tail end of the machine room, the layout of the cabinets, and the airflow organization), and different demands of each cabinet on cooling capacity. In this case, if a local hot spot occurs in the computer room, IT is a common practice to move the IT equipment in the area to another place of the data center computer room, or to move part of the load (such as the number of virtual machines) on the IT equipment in the area to another cabinet of the data center computer room. However, this relocation method may cause service interruption in the data center, and is costly. Therefore, when a local hot spot of the data center machine room occurs, the energy-saving control on the data center machine room can be started, so that the cost of eliminating the hot spot is reduced, and the energy consumption of the data center machine room is saved.
For another example, in an embodiment of the present specification, the condition that the data center machine room satisfies the energy-saving control starting condition may further be: and reaching the energy-saving optimization time preset for the data center machine room. Seasonal changes in one year can have a large impact on the ambient temperature of the data center room. For example, the temperature in summer is generally high, and if refrigeration equipment of a data center machine room is not turned on, IT is very easy for IT equipment of the data center machine to malfunction or even crash due to over-high temperature. Therefore, one or more energy-saving optimization times can be set for the data center machine room in advance, so that when the energy-saving optimization times are reached, energy-saving control over the data center machine room is started.
In other embodiments of this specification, the data center room satisfying the energy-saving control starting condition may also be a combination of a plurality of conditions, that is, when the data center room satisfies one of the plurality of conditions, the energy-saving control on the data center room is started. For example, in an embodiment of the present specification, when the data center room satisfies one of the following three conditions, the energy saving control on the data center room is turned on:
1) the change rate of the measured value of the total power of the IT equipment cabinets of the data center machine room exceeds a preset change rate threshold;
2) local hot spots appear in the data center machine room;
3) and reaching the energy-saving optimization time preset for the data center machine room.
While the process flows described above include operations that occur in a particular order, it should be appreciated that the processes may include more or less operations that are performed sequentially or in parallel (e.g., using parallel processors or a multi-threaded environment).
Corresponding to the foregoing energy-saving control method for a data center machine room, the present specification provides an embodiment of an energy-saving control device for a data center machine room, and referring to fig. 5, in some embodiments of the present specification, the energy-saving control device for a data center machine room may include:
the obtaining module 51 may be configured to obtain a plurality of parameter sets of a data center room; each parameter group comprises a power measured value of each IT equipment cabinet and a temperature setting value group corresponding to each refrigeration equipment;
the prediction module 52 may be configured to input the multiple parameter sets into a preset total power prediction model of the machine room, respectively, to obtain a total power prediction value of the machine room corresponding to each temperature setting value group;
the simulation module 53 may be configured to perform temperature field distribution simulation on the data center machine room by using the temperature set value group, so as to obtain machine room temperature field distribution;
a determining module 54, configured to determine a target temperature setting value set according to the machine room temperature field distribution; the target temperature setting value group is a temperature setting value group corresponding to the smallest total power prediction value of the machine room when the temperature of the air inlet of each IT equipment cabinet meets a preset condition;
a control module 55 may be configured to control the respective refrigeration units based on the set of target temperature settings.
In some embodiments of the energy-saving control device for a data center room in this specification, the performing, by using the temperature set point group, temperature field distribution simulation for the data center room includes:
sorting temperature setting value groups corresponding to the total power prediction value of the machine room according to the sequence from small to large of the total power prediction value of the machine room;
and taking out a temperature setting value group from the sequence, and simulating the machine room temperature field distribution of the data center machine room under the temperature setting value group by using a preset machine room temperature field simulation model.
In some embodiments of the energy-saving control device for a data center room in this specification, the determining a set of target temperature setting values according to the temperature field distribution of the room includes:
extracting the air inlet temperature of each IT equipment cabinet from the temperature field distribution of the machine room under the temperature setting value set;
judging whether the air inlet temperature of each IT equipment cabinet is within a preset cabinet air inlet temperature range or not;
and when the temperature of the air inlet of each IT equipment cabinet is within the temperature range of the air inlet of the cabinet, taking the temperature setting value set as a target temperature setting value set.
In some embodiments of the energy-saving control device for a data center room in this specification, the determining module 54 may further be configured to:
when the temperature of the air inlets of the IT equipment cabinets is out of the range of the temperature of the air inlets of the cabinets, taking out the next temperature setting value group from the sequence in sequence, and simulating the machine room temperature field distribution of the data center machine room under the next temperature setting value group by using the machine room temperature field simulation model;
and determining a target temperature setting value set according to the machine room temperature field distribution of the next temperature setting value set.
In some embodiments of the energy-saving control device for a data center room in this specification, the acquiring a plurality of parameter sets of the data center room may include:
and when the data center machine room meets the energy-saving control starting condition, acquiring a plurality of parameter sets of the data center machine room.
In some embodiments of the energy-saving control device for a data center room in this specification, the data center room satisfies an energy-saving control starting condition, and may include one or more of the following:
the change rate of the measured value of the total power of the IT equipment cabinets of the data center machine room exceeds a preset change rate threshold;
local hot spots appear in the data center machine room;
and reaching the energy-saving optimization time preset for the data center machine room.
In some embodiments of the energy-saving control device for a data center room in this specification, the total power prediction model of the room is obtained in advance by:
acquiring historical data of power measured values of the IT equipment cabinets, historical data of temperature set values of the refrigeration equipment and historical data of total power measured values of machine rooms of the data center machine room;
and training a neural network model by taking the historical data of the power measured values and the historical data of the temperature set values as input and the historical data of the total power measured values of the machine room as target output to obtain a total power prediction model of the machine room.
In some embodiments of the energy-saving control device for a data center room in the present specification, the neural network model includes a deep learning neural network model.
In some embodiments of the energy-saving control device for a data center room in this specification, the room temperature field simulation model includes a CFD simulation model.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
The present specification also provides a computer device embodiment. As shown in fig. 6, in some embodiments of the present description, the computer device 602 may include one or more processors 604, such as one or more Central Processing Units (CPUs) or Graphics Processors (GPUs), each of which may implement one or more hardware threads. The computer device 602 may also include any memory 606 for storing any kind of information, such as code, settings, data etc., and in a particular embodiment a computer program on the memory 606 and executable on the processor 604, which computer program, when executed by the processor 604, may perform the instructions according to the above described method. For example, and without limitation, memory 606 may include any one or combination of the following: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent fixed or removable components of computer device 602. In one case, when the processor 604 executes the associated instructions, which are stored in any memory or combination of memories, the computer device 602 may perform any of the operations of the associated instructions. The computer device 602 also includes one or more drive mechanisms 608, such as a hard disk drive mechanism, an optical disk drive mechanism, etc., for interacting with any memory.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products of some embodiments of the specification. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processor to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processor, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processor to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processor to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computer device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computer device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may also be practiced in distributed computing environments where tasks are performed by remote processors that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (13)
1. A data center machine room energy-saving control method is characterized by comprising the following steps:
acquiring a plurality of parameter sets of a data center machine room; each parameter group comprises a power measured value of each IT equipment cabinet and a temperature setting value group corresponding to each refrigeration equipment;
respectively inputting the plurality of parameter sets into a preset machine room total power prediction model to obtain a machine room total power prediction value corresponding to each temperature setting value group;
simulating the temperature field distribution of the data center machine room by using the temperature set value group to obtain the temperature field distribution of the machine room;
determining a target temperature setting value group according to the temperature field distribution of the machine room; the target temperature setting value group is a temperature setting value group corresponding to the smallest total power prediction value of the machine room when the temperature of the air inlet of each IT equipment cabinet meets a preset condition;
and controlling each refrigerating device according to the target temperature set value group.
2. The energy-saving control method for the data center room according to claim 1, wherein the simulating the temperature field distribution of the data center room by using the temperature set value group comprises:
sorting temperature setting value groups corresponding to the total power prediction value of the machine room according to the sequence from small to large of the total power prediction value of the machine room;
and taking out a temperature setting value group from the sequence, and simulating the machine room temperature field distribution of the data center machine room under the temperature setting value group by using a preset machine room temperature field simulation model.
3. The energy-saving control method for the data center machine room according to claim 2, wherein the determining the target temperature setting value group according to the distribution of the machine room temperature field comprises:
extracting the air inlet temperature of each IT equipment cabinet from the temperature field distribution of the machine room under the temperature setting value set;
judging whether the air inlet temperature of each IT equipment cabinet is within a preset cabinet air inlet temperature range or not;
and when the temperature of the air inlet of each IT equipment cabinet is within the temperature range of the air inlet of the cabinet, taking the temperature setting value set as a target temperature setting value set.
4. The energy-saving control method for the data center machine room as claimed in claim 3, further comprising:
when the temperature of the air inlets of the IT equipment cabinets is out of the range of the temperature of the air inlets of the cabinets, taking out the next temperature setting value group from the sequence in sequence, and simulating the machine room temperature field distribution of the data center machine room under the next temperature setting value group by using the machine room temperature field simulation model;
and determining a target temperature setting value set according to the machine room temperature field distribution of the next temperature setting value set.
5. The energy-saving control method for the data center room according to claim 1, wherein the obtaining a plurality of parameter sets of the data center room comprises:
and when the data center machine room meets the energy-saving control starting condition, acquiring a plurality of parameter sets of the data center machine room.
6. The energy-saving control method for the data center machine room as claimed in claim 5, wherein the data center machine room meets the energy-saving control starting condition, and the energy-saving control starting condition comprises one or more of the following conditions:
the change rate of the measured value of the total power of the IT equipment cabinets of the data center machine room exceeds a preset change rate threshold;
local hot spots appear in the data center machine room;
and reaching the energy-saving optimization time preset for the data center machine room.
7. The energy-saving control method for the data center machine room as claimed in claim 1, wherein the total power prediction model of the machine room is obtained in advance by:
acquiring historical data of power measured values of the IT equipment cabinets, historical data of temperature set values of the refrigeration equipment and historical data of total power measured values of machine rooms of the data center machine room;
and training a neural network model by taking the historical data of the power measured values and the historical data of the temperature set values as input and the historical data of the total power measured values of the machine room as target output to obtain a total power prediction model of the machine room.
8. The energy-saving control method for the data center machine room as claimed in claim 7, wherein the neural network model comprises a deep learning neural network model.
9. The energy-saving control method for the data center machine room as claimed in claim 2, wherein the simulation model for the temperature field of the machine room comprises a Computational Fluid Dynamics (CFD) simulation model.
10. The energy-saving control method for the data center machine room according to claim 1, further comprising, after controlling the respective cooling devices according to the set of target temperature setting values:
collecting power measured values of the IT equipment cabinets, and collecting total power measured values of the machine rooms of the data center machine room to serve as sampling data;
and updating the total power prediction model of the machine room according to the sampling data and the target temperature set value.
11. The utility model provides a data center computer lab energy-saving control device which characterized in that includes:
the acquisition module is used for acquiring a plurality of parameter sets of the data center machine room; each parameter group comprises a power measured value of each IT equipment cabinet and a temperature setting value group corresponding to each refrigeration equipment;
the prediction module is used for respectively inputting the plurality of parameter groups into a preset machine room total power prediction model to obtain a machine room total power prediction value corresponding to each temperature setting value group;
the simulation module is used for simulating the temperature field distribution of the data center machine room by utilizing the temperature set value group to obtain the temperature field distribution of the machine room;
the determining module is used for determining a target temperature setting value group according to the temperature field distribution of the machine room; the target temperature setting value group is a temperature setting value group corresponding to the smallest total power prediction value of the machine room when the temperature of the air inlet of each IT equipment cabinet meets a preset condition;
and the control module is used for controlling each refrigerating device according to the target temperature setting value group.
12. A computer arrangement comprising a memory, a processor, and a computer program stored on the memory, characterized in that the computer program, when executed by the processor, executes the instructions of the method according to any one of claims 1-10.
13. A computer storage medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor of a computer device, executes instructions of a method according to any one of claims 1-10.
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| CN113453504B (en) * | 2021-06-22 | 2023-10-10 | 国网江苏省电力有限公司信息通信分公司 | Control method and system for power grid friendly data center refrigerating system |
| CN115875809A (en) * | 2021-09-26 | 2023-03-31 | 中国移动通信集团浙江有限公司 | Energy-saving method and device for heat exchange equipment of machine room and computer readable storage medium |
| CN113779689B (en) * | 2021-11-11 | 2022-04-08 | 军事科学院系统工程研究院网络信息研究所 | Data center machine room equipment layout method based on hydrodynamics simulation |
| CN114064282B (en) * | 2021-11-23 | 2023-07-25 | 北京百度网讯科技有限公司 | Resource mining method and device and electronic equipment |
| CN114364218B (en) * | 2021-12-29 | 2024-05-24 | 深圳市英维克科技股份有限公司 | Fan control method, device, equipment and medium |
| CN115167562A (en) * | 2022-07-04 | 2022-10-11 | 中国电信股份有限公司 | Method and device for controlling temperature of machine room |
| CN115903945B (en) * | 2022-10-18 | 2023-12-01 | 中国人民解放军军事科学院军事科学信息研究中心 | Air conditioner control system, control method, control device and storage medium of machine room |
| CN115776813B (en) * | 2023-02-14 | 2023-04-28 | 南京金宁能源科技有限公司 | Efficient machine room control method and device |
| CN116489978B (en) * | 2023-06-25 | 2023-08-29 | 杭州电瓦特科技有限公司 | Computer lab energy-saving optimization control system based on artificial intelligence |
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| CN102620378B (en) * | 2011-01-27 | 2014-01-15 | 国际商业机器公司 | Method and system for data center energy saving controlling |
| CN104633846A (en) * | 2014-12-30 | 2015-05-20 | 曙光信息产业(北京)有限公司 | Control method and device of air conditioner system |
| CN106949598B (en) * | 2017-03-15 | 2019-07-30 | 华北电力大学 | Network center's machine room energy-saving optimization method when network traffic load changes |
| CN109189190B (en) * | 2018-10-16 | 2020-07-14 | 西安交通大学 | Data center heat management method based on temperature prediction |
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| CN110285532B (en) * | 2019-07-04 | 2021-07-30 | 中国工商银行股份有限公司 | Machine room air conditioner control method, device and system based on artificial intelligence |
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| CN111859615A (en) * | 2020-06-12 | 2020-10-30 | 菲尼克斯(上海)环境控制技术有限公司 | Micro-module data center air conditioner design method based on CFD simulation |
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