Data center energy-saving control method and system and storage medium

Abstract

The invention provides a data center energy-saving control method and system and a storage medium, and belongs to the technical field of data centers, and the method specifically comprises the steps: carrying out the determination of a reference time period group in similar time period groups of a server through the time distribution data of divided time periods in different similar time period groups, dividing the divided time periods into different time periods based on the time of the divided time periods in different reference time period groups, and determining the server with abnormal heat dissipation in the servers according to the change data of the temperature monitoring data of the servers in different time periods in each reference time period group. When it is determined that there is no abnormal heat dissipation server whose data processing amount fluctuates according to the change condition of the data processing amount of the abnormal heat dissipation server in the data center at different dates, a temperature control strategy of a temperature control device of the data center is determined according to the data processing amount of the abnormal heat dissipation server at different dates. And the energy consumption of temperature control is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of data centers, and in particular, relates to a data center energy-saving control method, system and storage medium. Background Art

[0002] In order to achieve energy-saving control of data centers and reduce the energy consumption of data centers, in the invention patent application CN118981618A "A method and system for energy-saving control of data centers", when the real-time energy consumption is not within the range of historical energy consumption data, the thermal management system determines the interval of the operating temperature value of the main equipment and determines the operation to be performed. This can solve the problem of delayed control parameter update caused by changes in equipment status in the energy-saving system data of existing data centers. However, there are the following technical problems: In the process of energy consumption control in the data center, since the energy consumption data of the data center is related to the data processing volume of different servers in the data center, this leads to the use of real-time energy consumption to determine the temperature control strategy. Once the data processing volume changes, it will inevitably lead to a longer delay in temperature control, making it difficult to meet the requirements for reliable operation of the server.

[0003] In response to the above technical problems, the present application specifically provides a data center energy-saving control method, system and storage medium. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: According to one aspect of the present invention, a data center energy-saving control method is provided.

[0005] A data center energy-saving control method, specifically comprising: S1 obtains historical monitoring data of servers in a data center, uses the historical monitoring data to determine deviations in data processing volume between different divided time periods, and divides the divided time periods into different similar time period groups based on the deviations; S2 uses the time distribution data of the divided time periods in different similar time period groups to determine the reference time period group in the similar time period group of the server, divides the divided time period into different time periods based on the time of the divided time periods in different reference time period groups, and determines that there is a server with abnormal heat dissipation among the servers according to the change data of the temperature monitoring data of the servers between different time periods in each reference time period group, and then proceeds to the next step; S3 determines the changes in data processing volume of the heat dissipation abnormal servers in the data center on different dates, and when it is determined by using the changes that there is no heat dissipation abnormal server with fluctuating data processing volume, the temperature control strategy of the temperature control device of the data center is determined based on the data processing volume of the heat dissipation abnormal servers on different dates.

[0006] The beneficial effects of the present invention are: According to the change data of the temperature monitoring data of the servers between different time periods in each reference time period group, it is determined whether there are servers with abnormal heat dissipation among the servers, thereby realizing the change of the temperature monitoring data of the servers between different time periods in each reference time period group, realizing the screening of servers with poor heat dissipation, and also laying a foundation for determining differentiated temperature control strategies according to whether there are servers with poor heat dissipation in the data center and the distribution data of servers with poor heat dissipation.

[0007] The temperature control strategy of the temperature control device of the data center is determined based on the data processing volume of the servers with abnormal heat dissipation on different dates, thereby taking into account both the distribution data of the servers with abnormal heat dissipation in the data center and the probability of temperature abnormality caused by the data processing volume of the servers with abnormal heat dissipation in the data center, thereby achieving the determination of the temperature control strategy from multiple angles, ensuring the reliability of temperature control and reducing the energy consumption of temperature control.

[0008] A further technical solution is that the historical monitoring data of the server includes the data processing volume of the server in different divided time periods.

[0009] A further technical solution is that the divided time periods are 30 minutes or 1 hour.

[0010] A further technical solution is that the deviation of the data processing amount between the divided time periods is determined based on the deviation of the data processing amount between the divided time periods.

[0011] A further technical solution is to divide the divided time periods into different similar time period groups, specifically including: Based on the data processing volumes between different divided time periods, divided time periods in the same data processing volume range are divided into the same similar time period group.

[0012] A further technical solution is that the time distribution data of the divided time periods includes distribution data of the divided time periods on different dates.

[0013] A further technical solution is that a method for determining a reference time period group in the similar time period group is: Based on the time of the divided time periods in different similar time period groups, the divided time periods are divided into different time periods based on the preset interval time periods; Determine the valid time periods in the time periods according to the number of divided time periods in the different time periods; It is determined whether the similar time period group is a reference time period group according to the number of valid time periods.

[0014] A further technical solution is that the effective time period is a time period in which the number of divided time periods is greater than the number of preset divided time periods.

[0015] A further technical solution is that a method for determining a temperature control strategy of a temperature control device of the data center is: Based on the data processing amount of the abnormal heat dissipation server on different dates, determine the date when the data processing amount in the abnormal heat dissipation server is greater than the preset data processing amount, and use it as the high-load date; Determine high load proportion factors of different heat dissipation abnormality servers according to the proportion of the number of high load dates of different heat dissipation abnormality servers; The load abnormality factor of the data center is determined based on the sum of the high load proportional factors of different heat dissipation abnormal servers, and the temperature control strategy of the temperature control device of the data center is determined by using the load abnormality factor.

[0016] A further technical solution is to use the load abnormality factor to determine the temperature control strategy of the temperature control device of the data center, specifically including: When the load abnormality factor is greater than a preset abnormality factor threshold, the temperature control device is temperature-regulated using the set temperature threshold; When the load abnormality factor is not greater than a preset abnormality factor threshold, the temperature control threshold of the temperature control device is determined based on the load abnormality factor according to a preset mapping function, and the temperature control threshold is used to adjust the temperature of the temperature control device.

[0017] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned data center energy-saving control method when running the computer program.

[0018] In a third aspect, the present invention provides a computer storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the above-mentioned data center energy-saving control method.

[0019] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0022] Figure 1 is a flow chart of a data center energy saving control method; Figure 2 is a flow chart of a method for determining a reference time period group in a similar time period group; Figure 3 is a flow chart of a method for determining a server with abnormal heat dissipation in a server; Figure 4 It is a flow chart of a method for determining a server with abnormal heat dissipation having fluctuations in data processing volume; Figure 5 is a flow chart of a method for determining a temperature control strategy of a temperature control device of a data center; Figure 6 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0024] If the temperature control strategy is determined by real-time energy consumption, once the data processing volume changes, it will inevitably lead to a delay in temperature control, making it difficult to meet the requirements for reliable operation of the server.

[0025] Similar time period group: Based on the data processing volume between different divided time periods, the divided time periods in the same data processing volume range are divided into the same similar time period group.

[0026] Reference time period group: Divide the divided time periods in the similar time period group into different time periods, determine the valid time periods in the time period according to the number of divided time periods in different time periods, and take the time period with more than 10 divided time periods as the valid time period, and take the similar time period group with more than 15 valid time periods as the reference time period group.

[0027] Server with abnormal heat dissipation: The temperature variation between the current time period and other time periods is determined based on the variation in the temperature monitoring data of the server between different time periods in each reference time period group. The reference time period group whose average temperature variation between the current time period and other time periods is greater than 3 degrees Celsius is regarded as a temperature variation abnormality group. When the proportion of the temperature variation abnormality group in the reference time period group is greater than 0.7, the server is determined to be a server with abnormal heat dissipation.

[0028] Abnormal heat dissipation server with fluctuations in data processing volume: Based on the data processing volume of the abnormal heat dissipation server in the data center on different dates, determine the average of the data processing volume on different dates, and use it as the benchmark data volume. Determine the deviation date in the date according to the deviation between the data processing volume on different dates and the benchmark data volume. When the proportion of the deviation dates is greater than 0.2, the server is determined to be a abnormal heat dissipation server with fluctuations in data processing volume.

[0029] Temperature control strategy: the date when the data processing volume in the heat dissipation abnormality server is greater than the preset data processing volume is regarded as the high-load date, the high-load proportional factors of different heat dissipation abnormality servers are determined according to the proportion of the number of high-load dates of different heat dissipation abnormality servers, the load abnormality factor of the data center is determined based on the sum of the high-load proportional factors of different heat dissipation abnormality servers, when the load abnormality factor is greater than the preset abnormality factor threshold, the temperature of the temperature control device is adjusted using the set temperature threshold, when the load abnormality factor is not greater than the preset abnormality factor threshold, the temperature control threshold of the temperature control device is determined based on the load abnormality factor according to the preset mapping function, and the temperature control threshold is used to adjust the temperature of the temperature control device. Embodiment 1 To solve the above problem, according to one aspect of the present invention, Figure 1 As shown, a data center energy-saving control method is provided, which specifically includes: S1 obtains historical monitoring data of servers in a data center, uses the historical monitoring data to determine deviations in data processing volume between different divided time periods, and divides the divided time periods into different similar time period groups based on the deviations; Furthermore, the historical monitoring data of the server includes the data processing volume of the server in different divided time periods.

[0030] Specifically, the divided time periods are 30 minutes or 1 hour.

[0031] It should be noted that the deviation of the data processing amount between the divided time periods is determined according to the deviation of the data processing amount between the divided time periods.

[0032] It can be understood that the divided time periods are divided into different similar time period groups, specifically including: Based on the data processing volumes between different divided time periods, divided time periods in the same data processing volume range are divided into the same similar time period group.

[0033] S2 uses the time distribution data of the divided time periods in different similar time period groups to determine the reference time period group in the similar time period group of the server, divides the divided time period into different time periods based on the time of the divided time periods in different reference time period groups, and determines that there is a server with abnormal heat dissipation among the servers according to the change data of the temperature monitoring data of the servers between different time periods in each reference time period group, and then proceeds to the next step; Specifically, the time distribution data of the divided time periods include distribution data of the divided time periods on different dates.

[0034] It is understandable that if Figure 2 As shown, the method for determining the reference time period group in the similar time period group is: Based on the time of the divided time periods in different similar time period groups, the divided time periods are divided into different time periods based on the preset interval time periods; Determine the valid time periods in the time periods according to the number of divided time periods in the different time periods; It is determined whether the similar time period group is a reference time period group according to the number of valid time periods.

[0035] Furthermore, the effective time period is a time period in which the number of divided time periods is greater than the preset number of divided time periods.

[0036] It should also be noted that when the number of valid time periods in the similar time period group is within a preset valid time period number range, the similar time period group is determined to be a reference time period group.

[0037] In another embodiment, the method for determining the reference time period group in the similar time period group is: Based on the time of the divided time periods in different similar time period groups, the divided time periods are divided into different time periods based on the preset interval time periods; Based on the number of divided time periods in different time periods, an average value of the number of divided time periods in different time periods is determined and used as the number average value; It is determined whether the similar time period group is a reference time period group according to the quantity average value.

[0038] In another embodiment, the method for determining the reference time period group in the similar time period group is: S11, based on the time of the divided time periods in the similar time period group and based on the preset interval time period, the divided time period is divided into different time periods, based on the number of divided time periods in the different time periods, the valid time periods in the time period are determined, and the reference value coefficients of different valid time periods are determined based on the number of time periods in the different valid time periods; S12: determining the intervals between different adjacent valid time periods based on the distribution data of different valid time periods, and determining the distribution dispersion coefficient of the valid time periods based on the average value of the intervals between different adjacent valid time periods; S13 determines the reference coefficient evaluation amount of the similar time period group according to the average value of the reference value coefficients of different effective time periods and the product of the distribution dispersion coefficient of the effective time periods, and uses the reference coefficient evaluation amount to determine whether it is a reference time period group.

[0039] Furthermore, the reference value coefficient is determined based on the ratio of the number of time periods in the valid time period to the number of preset time periods.

[0040] It should also be noted that the distribution dispersion coefficient of the effective time period is determined according to the product of the average value of the interval lengths between different adjacent effective time periods and a preset proportional factor.

[0041] Further, the divided time period is divided into different time periods, specifically including: Based on the preset interval time period, the preset interval time period corresponding to the divided time period is determined, and the preset interval time period corresponding to the divided time period is divided into corresponding time periods.

[0042] Specifically, Figure 3 As shown, the method for determining the server with abnormal heat dissipation among the servers is: Determine the change amount of the temperature monitoring data of the server between different time periods based on the change data of the temperature monitoring data of the server between different time periods in each reference time period group; Determine the temperature variation between the current time period and other time periods according to the variation of the temperature monitoring data of the server between different time periods, and use the temperature variation to determine the temperature variation abnormality group in the reference time period group; Whether the server is a heat dissipation abnormality server is determined based on the number of the temperature change abnormality groups.

[0043] Furthermore, the current time period is the time period closest to the current moment.

[0044] It should be noted that the temperature variation abnormal group is a reference time period group in which the average value of the temperature variation between the current time period and other time periods is greater than a preset variation.

[0045] Optionally, a method for determining a server with abnormal heat dissipation among the servers is: Determine the change amount of the temperature monitoring data of the server between different time periods based on the change data of the temperature monitoring data of the server between different time periods in each reference time period group; Determine the temperature variation between the current time period and other time periods according to the variation of the temperature monitoring data of the server between different time periods, and determine the temperature variation anomaly coefficient of the reference time group in combination with the interval between the current time period and other time periods; Whether the server is a heat dissipation abnormality server is determined by using average values ​​of temperature variation abnormality coefficients of different reference time groups.

[0046] Furthermore, when the average value of the temperature variation abnormality coefficients of the server in different reference time groups is greater than a preset variation coefficient threshold, the server is determined to be a heat dissipation abnormality server.

[0047] Optionally, a method for determining a server with abnormal heat dissipation among the servers is: S21 determines the change amount of the temperature monitoring data of the server between different time periods based on the change data of the temperature monitoring data of the server between different time periods in each reference time period group; S22 determines the temperature variation between the current time period and other time periods according to the variation of the temperature monitoring data of the server between different time periods, and determines the temperature variation anomaly coefficient of the reference time group in combination with the interval between the current time period and other time periods; Optionally, the above step S22 includes the following contents: S221 determines the temperature variation between the current time period and other time periods according to the variation of the temperature monitoring data of the server between different time periods. When the temperature variation in different reference time groups is within the preset variation interval, it is determined that the server does not belong to the abnormal heat dissipation server. When there is a reference time group whose temperature variation is not within the preset variation interval, the process proceeds to step S222. S222 determines the temperature variation anomaly coefficient of the reference time group based on the temperature variation between the current time period and other time periods and the interval between the current time period and other time periods. When the temperature variation anomaly coefficients of different reference time groups are all within the preset anomaly coefficient interval, it is determined that the server does not belong to the heat dissipation abnormal server. When there is a reference time group whose temperature variation anomaly coefficient is not within the preset anomaly coefficient interval, the process proceeds to step S223. S223 takes the reference time group whose temperature change abnormality coefficient is not within the preset abnormality coefficient range as the temperature change abnormality group. When the number of the temperature change abnormality groups is greater than the preset number of abnormality groups, it is determined that the server belongs to a heat dissipation abnormality server. When the number of the temperature change abnormality groups is not greater than the preset number of abnormality groups, proceed to step S23.

[0048] S23 determines the weight coefficients of different reference time groups according to the number of time periods of different reference time groups, and determines the temperature change evaluation amount of the server in combination with the temperature change abnormality coefficients of different reference time groups, and uses the temperature change evaluation amount of the server to determine whether the server is a heat dissipation abnormality server.

[0049] Optionally, the above step S23 includes the following contents: S231 determines the weight coefficients of different reference time groups according to the number of time periods of different reference time groups. When the sum of the weight coefficients of the temperature change abnormality groups is greater than the preset weight coefficient, it is determined that the server belongs to a heat dissipation abnormality server. When the sum of the weight coefficients of the temperature change abnormality groups is not greater than the preset weight coefficient, the process proceeds to step S232. S232: When there is no temperature variation abnormality group whose weight coefficient is greater than the preset weight coefficient threshold, the process proceeds to step S233; when there is a temperature variation abnormality group whose weight coefficient is greater than the preset weight coefficient threshold, the process proceeds to step S234; S233 obtains the number of temperature change abnormal groups. When the number of temperature change abnormal groups is less than the preset group number threshold, it is determined that the server does not belong to the heat dissipation abnormal server. When the number of temperature change abnormal groups is not less than the preset group number threshold, the process proceeds to step S234. S234 determines the weight coefficients of different reference time groups according to the number of time periods of different reference time groups, and determines the temperature change evaluation amount of the server in combination with the temperature change abnormality coefficients of different reference time groups, and uses the temperature change evaluation amount of the server to determine whether the server is a heat dissipation abnormality server.

[0050] Furthermore, when there is no server with abnormal heat dissipation among the servers, the temperature of the temperature control device of the data center is controlled by using a preset temperature control strategy. Specifically, the temperature adjustment threshold of the temperature control device is determined according to a preset temperature threshold corresponding to the total daily energy consumption of different servers in the data center, and the temperature of the temperature control device is adjusted using the temperature adjustment threshold.

[0051] It should also be noted that the preset temperature threshold is determined based on a preset corresponding relationship between the daily average total energy consumption and the temperature threshold.

[0052] S3 determines the changes in data processing volume of the heat dissipation abnormal servers in the data center on different dates, and when it is determined by using the changes that there is no heat dissipation abnormal server with fluctuating data processing volume, the temperature control strategy of the temperature control device of the data center is determined based on the data processing volume of the heat dissipation abnormal servers on different dates.

[0053] Specifically, Figure 4 As shown, the method for determining the abnormal heat dissipation server with fluctuation in data processing volume is: Based on the data processing volume of the server with abnormal heat dissipation in the data center on different days, determine the average of the data processing volume on different days, and use it as the benchmark data volume; Determine the deviation date among the dates according to the deviation between the data processing amount on different dates and the benchmark data amount; Based on the number ratio of the deviation dates, it is determined whether the server is a heat dissipation abnormality server with fluctuations in data processing volume.

[0054] Further, when the proportion of the number of the deviation dates is greater than the proportion of the number of preset dates, it is determined that the server is a heat dissipation abnormality server with fluctuations in data processing volume.

[0055] It is understandable that when there is a server with abnormal heat dissipation and fluctuations in data processing, the temperature of the temperature control device is adjusted by using a set temperature threshold.

[0056] Specifically, Figure 5 As shown, the method for determining the temperature control strategy of the temperature control device of the data center is: Based on the data processing amount of the abnormal heat dissipation server on different dates, determine the date when the data processing amount in the abnormal heat dissipation server is greater than the preset data processing amount, and use it as the high-load date; Determine high load proportion factors of different heat dissipation abnormality servers according to the proportion of the number of high load dates of different heat dissipation abnormality servers; The load abnormality factor of the data center is determined based on the sum of the high load proportional factors of different heat dissipation abnormal servers, and the temperature control strategy of the temperature control device of the data center is determined by using the load abnormality factor.

[0057] Further, the temperature control strategy of the temperature control device of the data center is determined by using the load abnormality factor, specifically including: When the load abnormality factor is greater than a preset abnormality factor threshold, the temperature control device is temperature-regulated using the set temperature threshold; When the load abnormality factor is not greater than a preset abnormality factor threshold, the temperature control threshold of the temperature control device is determined based on the load abnormality factor according to a preset mapping function, and the temperature control threshold is used to adjust the temperature of the temperature control device. Embodiment 2 In the second aspect, as Figure 6 As shown, the present invention provides a computer system, comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned data center energy-saving control method when running the computer program.

[0058] Optionally, the above step S11 includes the following contents: S111 is based on the number of divided time periods in the similar time period group. When the number of divided time periods in the similar time period group is less than a preset number threshold, it is determined that the similar time period group does not belong to the reference time period group. When the number of divided time periods in the similar time period group is not less than the preset number threshold, the process proceeds to step S112. S112: based on the number of divided time periods in different time periods, if it is determined that there is no valid time period in the time period, then it is determined that the similar time period group does not belong to the reference time period group; if it is determined that there is a valid time period in the time period, then the process proceeds to step S113; S113: using the time period quantities of different valid time periods to determine the total time period quantity of different valid time periods; when the total time period quantity is less than a preset total quantity threshold, determining that the similar time period group belongs to a reference time period group; and when the total time period quantity is not less than the preset total quantity threshold, proceeding to step S114; S114 determines the reference value coefficients of different valid time periods based on the number of time periods in different valid time periods. When the sum of the reference value coefficients of different valid time periods is less than the preset value coefficient threshold, it is determined that the similar time period group does not belong to the reference time period group. When the sum of the reference value coefficients of different valid time periods is not less than the preset value coefficient threshold, proceed to step S12.

[0059] Optionally, the above step S12 includes the following contents: S121 determines that when the maximum value of the interval duration between the effective time periods is within the preset interval duration range based on the distribution data of different effective time periods, the similar time period group does not belong to the reference time period group. When the maximum value of the interval duration between the effective time periods is not within the preset interval duration range, the process proceeds to step S122; S122 obtains valid time periods whose interval duration is greater than the preset interval duration, and uses them as interval time periods. When the number of interval time periods is greater than the time period quantity setting value, the process proceeds to step S123. When the number of interval time periods is not greater than the time period quantity setting value, it is determined that the similar time period group does not belong to the reference time period group. S123 uses the average value of the interval lengths between different adjacent valid time periods to determine the distribution dispersion coefficient of the valid time period. When the distribution dispersion coefficient of the valid time period is less than the preset dispersion coefficient threshold, it is determined that the similar time period group belongs to the reference time period group. When the distribution dispersion coefficient of the valid time period is not less than the preset dispersion coefficient threshold, proceed to step S12. Embodiment 3 In a third aspect, the present invention provides a computer storage medium having a computer program stored thereon. When the computer program is executed in a computer, the computer is caused to execute the above-mentioned data center energy-saving control method.

[0060] Optionally, the method for determining the abnormal heat dissipation server with fluctuation in data processing volume is: Based on the data processing volume of the server with abnormal heat dissipation in the data center on different days, determine the median of the data processing volume on different days, and use it as the benchmark data volume; When the deviations between the data processing amounts on different dates and the benchmark data amount are all within the preset deviation range, it is determined that the server is not a server with abnormal heat dissipation whose data processing amount fluctuates; When there is a date when the deviation between the data processing volume and the benchmark data volume is not within the preset deviation range: The date whose deviation between the data processing amount and the reference data amount is not within the preset deviation interval is used as the deviation date in the date, and when the number of the deviation dates is greater than the preset deviation date number threshold, it is determined that the server is a heat dissipation abnormal server with fluctuation in data processing amount; When the number of the deviation dates is not greater than the preset deviation date number threshold: Determine the deviation coefficients of different deviation dates based on the deviations of the data processing volumes on different deviation dates from the reference data volume, and when the sum of the deviation coefficients of different deviation dates is greater than the preset deviation coefficient, determine that the server is a heat dissipation abnormality server with fluctuations in data processing volume; When the sum of the deviation coefficients of different deviation dates is not greater than the preset deviation coefficient: When the sum of the deviation coefficients of different deviation dates is not within the preset deviation coefficient range: It is determined that the server is not a server with abnormal heat dissipation whose data processing volume fluctuates; When the sum of the deviation coefficients of different deviation dates is within the preset deviation coefficient range: The server deviation coefficient is determined based on the number ratio of the deviation dates and the average value of the deviation coefficients of different deviation dates, and the server deviation coefficient is used to determine whether the server is a heat dissipation abnormality server with fluctuations in data processing volume.

[0061] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0062] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A data center energy saving control method, characterized in that: Specifically include: Acquire historical monitoring data of servers in the data center, determine deviations of data processing volumes between different divided time periods using the historical monitoring data, and divide the divided time periods into different similar time period groups based on the deviations; Determine the reference time period group in the similar time period group of the server by using the time distribution data of the divided time periods in different similar time period groups, divide the divided time periods into different time periods based on the time of the divided time periods in different reference time period groups, and when it is determined that there is a server with abnormal heat dissipation in the server according to the change data of the temperature monitoring data of the server between different time periods in each reference time period group, proceed to the next step; Determine the change in data processing volume of the heat dissipation abnormality server in the data center on different dates, and use the change to determine that there is no heat dissipation abnormality server with fluctuation in data processing volume, and determine the temperature control strategy of the temperature control device of the data center based on the data processing volume of the heat dissipation abnormality server on different dates.

2. The data center energy saving control method according to claim 1, characterized in that: The historical monitoring data of the server includes the data processing volume of the server in different divided time periods.

3. The data center energy saving control method according to claim 2, characterized in that: The divided time periods are 30 minutes or 1 hour.

4. The data center energy saving control method according to claim 1, characterized in that: The deviation of the data processing amount between the divided time periods is determined according to the deviation of the data processing amount between the divided time periods.

5. The data center energy saving control method according to claim 4, characterized in that: Dividing the divided time periods into different similar time period groups specifically includes: Based on the data processing volumes between different divided time periods, divided time periods in the same data processing volume range are divided into the same similar time period group.

6. The data center energy saving control method according to claim 1, characterized in that: The method for determining the reference time period group in the similar time period group is: Based on the time of the divided time periods in different similar time period groups, the divided time periods are divided into different time periods based on the preset interval time periods; Determine the valid time periods in the time periods according to the number of divided time periods in different time periods; It is determined whether the similar time period group is a reference time period group according to the number of valid time periods.

7. The data center energy saving control method according to claim 6, characterized in that: The effective time period is a time period in which the number of divided time periods is greater than the preset number of divided time periods.

8. The data center energy saving control method according to claim 1, characterized in that: The method for determining the temperature control strategy of the temperature control device of the data center is: Based on the data processing amount of the abnormal heat dissipation server on different dates, determine the date when the data processing amount in the abnormal heat dissipation server is greater than the preset data processing amount, and use it as the high-load date; Determine high load proportion factors of different heat dissipation abnormality servers according to the proportion of the number of high load dates of different heat dissipation abnormality servers; The load abnormality factor of the data center is determined based on the sum of the high load proportional factors of different heat dissipation abnormal servers, and the temperature control strategy of the temperature control device of the data center is determined by using the load abnormality factor.

9. A computer system comprising: A memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes a data center energy-saving control method as described in any one of claims 1 to 8 when running the computer program.

10. A computer storage medium having a computer program stored thereon, wherein when the computer program is executed in a computer, Instruct a computer to execute a data center energy-saving control method as described in any one of claims 1 to 8.

Images