Data center management method and system and storage medium

Abstract

The invention discloses a data center management method and system and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining the power data of a data center server, and calculating the energy consumption of the server; obtaining cold supply data of the data center server; according to the cold supply data and the server energy consumption, an objective function is established; acquiring the utilization rate and task load of the data center server in real time; solving the objective function by adopting a particle swarm algorithm; performing task allocation on the data center according to the solution of the objective function; wherein the power data comprises processor power data, memory power data, disk power data and interface power data. According to the method, the server energy consumption is modeled according to the power data of the data center server, the task load and the cooling capacity of the server are considered, the total energy consumption of the server is modeled and solved, a task allocation scheme which ensures sufficient cooling of each server while the total energy consumption is lowest is obtained, and the energy consumption of the data center is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data center management method, system and storage medium. Background Art

[0002] With the rapid development of cloud computing, data centers have become an important part of the information society. However, data centers have high power consumption and consume a lot of energy during operation, resulting in high operating costs and low system reliability. Therefore, it is necessary to manage data centers and reduce their energy consumption.

[0003] Existing data center energy consumption reduction solutions optimize data center management at a single level, such as reducing the number of deployed servers and performing tasks through a small number of servers. However, a large number of tasks are piled up in a small number of servers, and the server load is heavy. Long-term work will reduce the computing performance of the system, resulting in slow task processing, and the data center still has high energy consumption. Summary of the invention

[0004] The purpose of the present invention is to provide a data center management method, system and storage medium. The present invention models the server energy consumption according to the power data of the data center server, considers the server's task volume and cooling capacity, models and solves the total energy consumption of the server, obtains a task allocation plan that minimizes the total energy consumption while ensuring sufficient cooling for each server, and reduces the energy consumption of the data center.

[0005] The purpose of the present invention is achieved by the following technical means: In a first aspect, the present invention provides a data center management method, comprising the following steps: Obtain power data of data center servers and calculate server energy consumption; Acquiring cooling data of the data center server; Establishing an objective function according to the cooling data and the server energy consumption; Obtaining the utilization rate and workload of the data center server in real time; A particle swarm algorithm is used to solve the objective function; Allocating tasks to data centers according to a solution of the objective function; The power data includes: processor power data, memory power data, disk power data and interface power data.

[0006] Preferably, the obtaining of power data of the data center server and calculating the server energy consumption comprises the following steps: Get the full load power and no load power of the processor; Calculating processor energy consumption according to the full-load power of the processor and the no-load power of the processor; Get the read power, write power and refresh power of the memory; Calculating memory energy consumption according to the read power of the memory, the write power of the memory and the refresh power of the memory; Get the read power, write power and no-load power of the disk; Calculating disk energy consumption according to the read power of the disk, the write power of the disk and the no-load power of the disk; Get the task size and bandwidth size of the interface; Calculating the interface energy consumption according to the task size of the interface and the bandwidth size of the interface; The server energy consumption is calculated according to the processor energy consumption, the memory energy consumption, the disk energy consumption and the interface energy consumption.

[0007] Preferably, the calculation formula of the server energy consumption is as follows: , , , , , in, is the server energy consumption, is the first weight, is the processor power consumption, is the second weight, is the memory energy consumption, is the third weight, is the disk power consumption, is the fourth weight, is the interface energy consumption, is the full load power of the processor, is the no-load power of the processor, is the processor utilization, is the memory read power, is the write power of the memory, is the memory refresh power, is the disk read power, is the write power of the disk, is the no-load power of the disk, is the first performance coefficient of the interface, is the task size of the interface, is the bandwidth of the interface, is the second performance coefficient of the interface.

[0008] Preferably, establishing an objective function according to the cooling data and the server energy consumption comprises the following steps: Calculating the cooling efficiency of the data center server according to the cooling data; Calculating the total energy consumption of the data center server according to the cooling efficiency and the server energy consumption; The objective function is established with the goal of minimizing the total energy consumption of the data center servers.

[0009] Preferably, the total energy consumption of the data center server is calculated as follows: , , in, is the total energy consumption of the data center server, is the server energy consumption, For cooling energy consumption, is the running time, For cooling efficiency; The objective function is expressed as follows: , , , , in, is the objective function, For the The total energy consumption of the server, is the number of servers, For the The utilization of the servers, For the The cooling energy consumption of each server is is the cooling threshold, For the The task size of each server, For the task volume.

[0010] Preferably, the objective function is solved by using a particle swarm algorithm, comprising the following steps: Initialize particles randomly to obtain the initial population; Calculate the fitness value of the particle; According to the fitness value of the particle, updating the optimal fitness value of the individual and the optimal fitness value of the group; According to the optimal fitness value of the individual and the optimal fitness value of the group, updating the speed of the particle and the position of the particle; Repeatedly updating the velocity of the particle and the position of the particle until the number of iterations reaches a maximum number of iterations; Outputting a solution of the objective function to obtain a task allocation amount for the data center server; The calculation formula of the fitness value is as follows: , in, is the fitness value, For the The total energy consumption of the server, is the number of servers.

[0011] Preferably, updating the speed and position of the particle according to the optimal fitness value of the individual and the optimal fitness value of the group comprises the following steps: updating the speed of the particle according to the optimal fitness value of the individual and the optimal fitness value of the group to obtain a first update speed; Update the position of the particle according to the first update speed to obtain a first updated position; Calculating an updated fitness value of the particle according to the first updated position; Subtract the updated fitness value from the optimal fitness value of the individual to obtain a fitness difference value; When the fitness difference is greater than or equal to a preset difference, taking the first updated speed as the speed of the particle, and taking the first updated position as the position of the particle; When the fitness difference is less than the preset difference, a mutation algorithm is used to update the first update speed and the first update position to obtain a second update speed and a second update position, and the second update speed is used as the speed of the particle, and the second update position is used as the position of the particle.

[0012] In a second aspect, the present invention provides a data center management system, applying the above-mentioned data center management method, including: a server energy consumption calculation module, a data center cooling module, an objective function establishment module, a data real-time monitoring module, an objective function solving module and a task allocation module; The server energy consumption calculation module is used to obtain power data of the data center server and calculate the server energy consumption; The data center cooling module is used to obtain cooling data of the data center server; The objective function establishment module is used to establish an objective function according to the cooling data and the server energy consumption; The data real-time monitoring module is used to obtain the utilization rate and task volume of the data center server in real time; The objective function solving module is used to solve the objective function using a particle swarm algorithm; The task allocation module is used to allocate tasks to the data center according to the solution of the objective function.

[0013] In a third aspect, the present invention provides an electronic device, comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes the above-mentioned data center management method.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the above-mentioned data center management method.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention models the energy consumption of the servers according to the power data of the data center servers, considers the task volume and cooling capacity of the servers, models and solves the total energy consumption of the servers, obtains a task allocation scheme that minimizes the total energy consumption while ensuring sufficient cooling for each server, and reduces the energy consumption of the data center. The present invention solves the objective function through the particle swarm algorithm. When it falls into the local optimal problem, it adopts the mutation algorithm to update the position and speed of the particles, which improves the solving speed while ensuring the quality of the solution, obtains the optimal task allocation plan, and reduces the energy consumption of the data center. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 A flowchart of a data center management method provided in this embodiment; Figure 2 A schematic diagram of a process for obtaining power data of a data center server and calculating server energy consumption in step S1 provided in this embodiment; Figure 3Step S3 provided in this embodiment is a flow chart of establishing an objective function according to cooling data and server energy consumption; Figure 4 Step S5 provided in this embodiment is a flow chart of using a particle swarm algorithm to solve the objective function; Figure 5 Step S54 provided in this embodiment is a flow chart of updating the speed and position of the particle according to the optimal fitness value of the individual and the optimal fitness value of the group; Figure 6 A schematic diagram of the structure of a data center management system provided in this embodiment; Figure 7 A schematic diagram of the structure of an electronic device provided in this embodiment. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0021] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0022] Embodiment 1 This embodiment provides a data center management method, such as Figure 1 As shown, the following steps are included: S1, obtain the power data of the data center server and calculate the server energy consumption; S2, obtaining cooling data of the data center server; S3, establish the objective function based on the cooling data and server energy consumption; S4, obtains the utilization rate and task volume of the data center server in real time; S5, using particle swarm algorithm to solve the objective function; S6, assigning tasks to data centers based on the solution of the objective function; The power data includes: processor power data, memory power data, disk power data and interface power data.

[0023] It should be noted that there are multiple servers in the data center, and the energy management of the data center can be broken down into the management of each server separately. The parameters of each part of each server are different, resulting in different energy consumption of each server. Therefore, it is necessary to obtain the power data of the server and measure the energy consumption of the server according to the power data of the server. The power data of the data center server includes the power data of the processor, memory, disk and interface, and the server energy consumption is estimated by the above power data. In addition, the data center cools each server to ensure that each server works at a suitable temperature. The utilization rate of the data center server refers to the utilization rate of the processor of the server at that time, and the task volume of the data center server refers to the size of the task that the server needs to process at that time; the above two data are used to measure the working status of the server. In this embodiment, when considering the energy consumption of the server, the energy consumption required for cooling is also considered. Since the energy consumption required for cooling is comprehensively considered according to the workload of the server and the cooling situation, therefore, according to the cooling data and the server energy consumption, the objective function is established and solved with the goal of minimizing the total energy consumption, so as to obtain a task allocation scheme with the minimum total energy consumption considering the server performance and the task volume.

[0024] In this embodiment, the server energy consumption is modeled according to the power data of the data center server, the server task volume and cooling capacity are taken into consideration, and the total energy consumption of the server is modeled and solved to obtain a task allocation plan that minimizes the total energy consumption while ensuring sufficient cooling for each server, thereby reducing the energy consumption of the data center.

[0025] In some embodiments, step S1, power data of a data center server is obtained and server energy consumption is calculated, such as Figure 2 As shown, the following steps are included: S11, obtaining the full-load power and no-load power of the processor; S12, calculating the processor energy consumption according to the full load power of the processor and the no-load power of the processor; S13, obtaining the read power, write power and refresh power of the memory; S14, calculating memory energy consumption according to the memory read power, the memory write power and the memory refresh power; S15, obtaining the read power, write power and no-load power of the disk; S16, calculating the disk energy consumption according to the disk read power, the disk write power and the disk no-load power; S17, obtaining the task size and bandwidth size of the interface; S18, calculating the interface energy consumption according to the task size of the interface and the bandwidth size of the interface; S19, calculates the server energy consumption according to the processor energy consumption, memory energy consumption, disk energy consumption and interface energy consumption.

[0026] In some embodiments, the server energy consumption is calculated as follows: , , , , , in, is the server energy consumption, is the first weight, is the processor power consumption, is the second weight, is the memory energy consumption, is the third weight, is the disk power consumption, is the fourth weight, is the interface energy consumption, is the full load power of the processor, is the no-load power of the processor, is the processor utilization, is the memory read power, is the write power of the memory, is the memory refresh power, is the disk read power, is the write power of the disk, is the no-load power of the disk, is the first performance coefficient of the interface, is the task size of the interface, is the bandwidth of the interface, is the second performance coefficient of the interface.

[0027] It should be noted that full load power refers to the maximum power of the part, no-load power refers to the power of the part when it is idle, and the utilization rate of the processor refers to the percentage of the processor load; the energy consumption calculated by the memory through the reading, writing and refreshing power of the memory and the disk through the reading, writing and no-load power of the disk also reflects its utilization rate. The first performance coefficient and the second performance coefficient of the interface are determined according to different interfaces and can be regarded as a constant. Since the energy consumption calculation time required for the four parts is different, but the time can be obtained according to the amount of assigned tasks, therefore, processing the four energy consumption models by setting weight coefficients is conducive to calculating the total energy consumption of the server.

[0028] In some embodiments, step S3, based on the cooling data and the server energy consumption, an objective function is established, such as Figure 3 As shown, the following steps are included: S31, calculating the cooling efficiency of the data center server according to the cooling data; S32, calculating the total energy consumption of the data center server according to the cooling efficiency and the server energy consumption; S33, establishing an objective function based on minimizing the total energy consumption of the data center servers.

[0029] In some embodiments, the total energy consumption of the data center server is calculated as follows: , , in, is the total energy consumption of the data center server, is the server energy consumption, For cooling energy consumption, is the running time, For cooling efficiency; The objective function formula is as follows: , , , , in, is the objective function, For the The total energy consumption of the server, is the number of servers, For the The utilization of the servers, For the The cooling energy consumption of each server is is the cooling threshold, For the The task size of each server, For the task volume.

[0030] It should be noted that the cooling data refers to the cooling temperature of the data center server. The cooling efficiency and running time can be calculated empirically. In this way, the total energy consumption of the server can be obtained based on the server energy consumption and cooling energy consumption. By summing the total energy consumption of each server, the objective function is constructed with this minimization as the goal. The constraints of the objective function are divided into three constraints. The first is to constrain the utilization rate of each server not to exceed 1, that is, the server has sufficient capacity to process tasks. The second is that the cooling cannot exceed the upper limit of the cooling module, that is, the cooling energy consumption is less than or equal to the cooling threshold. The third is that the sum of the tasks assigned to each server needs to be equal to the amount of tasks to be assigned.

[0031] In some embodiments, in step S5, a particle swarm algorithm is used to solve the objective function, such as Figure 4 As shown, the following steps are included: S51, randomly initialize particles to obtain the initial population; S52, calculating the fitness value of the particle; S53, according to the fitness value of the particle, updating the optimal fitness value of the individual and the optimal fitness value of the group; S54, updating the speed and position of the particle according to the optimal fitness value of the individual and the optimal fitness value of the group; S55, repeatedly updating the particle velocity and the particle position until the number of iterations reaches the maximum number of iterations; S56, outputting the solution of the objective function to obtain the task allocation of the data center server; The calculation formula of fitness value is as follows: , in, is the fitness value, For the The total energy consumption of the server, is the number of servers.

[0032] It should be noted that by obtaining the utilization rate and task volume of the server, the energy consumption state of the data center at that moment is determined. At this time, it is only necessary to determine the task allocation of each server, and the total energy consumption of the server can be calculated through the total energy consumption calculation formula of the data center server. Therefore, the position of the population particle is the vector composed of the task allocation of each server. By continuously optimizing the population particles, the optimal task allocation scheme is obtained. The fitness value is the inverse of the logarithm of the total energy consumption of the server. Minimizing the objective function requires maximizing the fitness value. When updating the optimal fitness value, the larger of the two fitness values ​​is updated.

[0033] In some embodiments, step S54 updates the speed and position of the particle according to the optimal fitness value of the individual and the optimal fitness value of the group, such as Figure 5 As shown, the following steps are included: S541, updating the speed of the particle according to the optimal fitness value of the individual and the optimal fitness value of the group to obtain a first update speed; S542, updating the position of the particle according to the first update speed to obtain a first updated position; S543, calculating an updated fitness value of the particle according to the first updated position; S544, subtracting the updated fitness value from the optimal fitness value of the individual to obtain a fitness difference value; S545, when the fitness difference is greater than or equal to the preset difference, taking the first updated speed as the speed of the particle, and taking the first updated position as the position of the particle; S546, when the fitness difference is less than the preset difference, a mutation algorithm is used to update the first update speed and the first update position to obtain a second update speed and a second update position, and the second update speed is used as the speed of the particle, and the second update position is used as the position of the particle.

[0034] It should be noted that, considering the lack of diversity in the initial population, when it is found that the updated particle population is premature, the particle population is disturbed by the mutation algorithm to prevent the algorithm from entering the local optimal situation. Specifically, the update of the particle speed and position depends on the fitness value. When the change in the fitness value is small, the algorithm may enter the local optimal situation. Therefore, after the particle speed and position are updated, the difference between the updated fitness value and the individual's optimal fitness value is calculated. When the difference is large, it indicates normal convergence. At this time, the updated position and update speed are optimized; when the difference is less than the preset difference, the mutation algorithm is used to further change the particle position and speed based on the updated position and update speed.

[0035] The formula of the mutation algorithm is as follows: , in, is the second update speed, is the coefficient of variation, The first update speed.

[0036] In this embodiment, the objective function is solved by a particle swarm algorithm. When a local optimal problem occurs, a mutation algorithm is used to update the position and velocity of the particles, thereby improving the solving speed while ensuring the quality of the solution, obtaining the optimal task allocation solution, and reducing the energy consumption of the data center.

[0037] Embodiment 2 This embodiment provides a data center management system, which applies the above-mentioned data center management method, such as Figure 6 As shown, it includes: server energy consumption calculation module, data center cooling module, objective function establishment module, data real-time monitoring module, objective function solution module and task allocation module; The server energy consumption calculation module is used to obtain the power data of the data center server and calculate the server energy consumption; Data center cooling module, used to obtain cooling data of data center servers; An objective function establishment module is used to establish an objective function based on cooling data and server energy consumption; The data real-time monitoring module is used to obtain the utilization rate and task volume of the data center server in real time; The objective function solving module is used to solve the objective function using a particle swarm algorithm; The task allocation module is used to allocate tasks to data centers according to the solution of the objective function.

[0038] In this embodiment, the server energy consumption is modeled according to the power data of the data center server, the server task volume and cooling capacity are taken into consideration, and the total energy consumption of the server is modeled and solved to obtain a task allocation plan that minimizes the total energy consumption while ensuring sufficient cooling for each server, thereby reducing the energy consumption of the data center.

[0039] It should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of the above modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, each functional module can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0040] Embodiment 3 This embodiment provides an electronic device 2, such as Figure 7 As shown, a processor 21 and a memory 22 are provided, wherein the memory 22 is used to store computer program codes, and the computer program codes include computer instructions. When the processor 21 executes the computer instructions, the electronic device executes the above-mentioned data center management method.

[0041] The electronic device 2 includes a processor 21, a memory 22, an output device 23, and an input device 24. The processor 21, the memory 22, the output device 23, and the input device 24 are coupled via a connector, and the connector includes various interfaces, transmission lines, or buses, etc., which are not limited in the embodiments of the present invention. It should be understood that in various embodiments of the present invention, coupling refers to mutual connection in a specific manner, including direct connection or indirect connection through other devices, for example, through various interfaces, transmission lines, buses, etc.

[0042] The processor 21 may be one or more graphics processing units (GPUs). When the processor 21 is a GPU, the GPU may be a single-core GPU or a multi-core GPU. Optionally, the processor 21 may be a processor group consisting of multiple GPUs, and the multiple processors are coupled to each other via one or more buses. Optionally, the processor 21 may also be other types of processors, etc., which are not limited in the embodiments of the present invention.

[0043] The memory 22 can be used to store computer program instructions and various computer program codes including program codes for executing the scheme of the present invention. Optionally, the memory 22 includes but is not limited to random access memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM), or portable read only memory (CD-ROM), and the memory 22 is used for related instructions and data.

[0044] The input device 24 is used to input data and / or signals, and the output device 23 is used to output data and / or signals. The output device 23 and the input device 24 can be independent devices or an integrated device.

[0045] This embodiment provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the above-mentioned data center management method.

[0046] The above are only specific embodiments of the present invention, which enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A data center management method, characterized in that: The following steps are involved: Obtain power data of data center servers and calculate server energy consumption; Acquiring cooling data of the data center server; Establishing an objective function according to the cooling data and the server energy consumption; Obtaining the utilization rate and workload of the data center server in real time; A particle swarm algorithm is used to solve the objective function; Allocating tasks to data centers according to a solution of the objective function; The power data includes: processor power data, memory power data, disk power data and interface power data.

2. A data center management method according to claim 1, characterized in that: The step of obtaining the power data of the data center server and calculating the server energy consumption includes the following steps: Get the full load power and no load power of the processor; Calculating processor energy consumption according to the full-load power of the processor and the no-load power of the processor; Get the read power, write power and refresh power of the memory; Calculating memory energy consumption according to the read power of the memory, the write power of the memory and the refresh power of the memory; Get the read power, write power and no-load power of the disk; Calculating disk energy consumption according to the read power of the disk, the write power of the disk and the no-load power of the disk; Get the task size and bandwidth size of the interface; Calculating the interface energy consumption according to the task size of the interface and the bandwidth size of the interface; The server energy consumption is calculated according to the processor energy consumption, the memory energy consumption, the disk energy consumption and the interface energy consumption.

3. A data center management method according to claim 2, characterized in that: The calculation formula of the server energy consumption is as follows: ， ， ， ， ， in, is the server energy consumption, is the first weight, is the processor power consumption, is the second weight, is the memory energy consumption, is the third weight, is the disk power consumption, is the fourth weight, is the interface energy consumption, is the full load power of the processor, is the no-load power of the processor, is the processor utilization, is the memory read power, is the write power of the memory, is the memory refresh power, is the disk read power, is the write power of the disk, is the no-load power of the disk, is the first performance coefficient of the interface, is the task size of the interface, is the bandwidth of the interface, is the second performance coefficient of the interface.

4. A data center management method according to claim 1, characterized in that: The establishing of the objective function according to the cooling data and the server energy consumption comprises the following steps: Calculating the cooling efficiency of the data center server according to the cooling data; Calculating the total energy consumption of the data center server according to the cooling efficiency and the server energy consumption; The objective function is established with the goal of minimizing the total energy consumption of the data center servers.

5. A data center management method according to claim 4, characterized in that: The calculation formula of the total energy consumption of the data center server is as follows: ， ， in, is the total energy consumption of the data center server, is the server energy consumption, For cooling energy consumption, is the running time, For cooling efficiency; The objective function is expressed as follows: ， ， ， ， in, is the objective function, For the The total energy consumption of the server, is the number of servers, For the The utilization of the servers, For the The cooling energy consumption of each server is is the cooling threshold, For the The task size of each server, For the task volume.

6. A data center management method according to claim 4, characterized in that: The objective function is solved by using a particle swarm algorithm, which includes the following steps: Initialize particles randomly to obtain the initial population; Calculate the fitness value of the particle; According to the fitness value of the particle, updating the optimal fitness value of the individual and the optimal fitness value of the group; According to the optimal fitness value of the individual and the optimal fitness value of the group, updating the speed of the particle and the position of the particle; Repeatedly updating the velocity of the particle and the position of the particle until the number of iterations reaches a maximum number of iterations; Outputting a solution of the objective function to obtain a task allocation amount for the data center server; The calculation formula of the fitness value is as follows: ， in, is the fitness value, For the The total energy consumption of the server, is the number of servers.

7. A data center management method according to claim 6, characterized in that: The updating of the speed and position of the particle according to the optimal fitness value of the individual and the optimal fitness value of the group comprises the following steps: updating the speed of the particle according to the optimal fitness value of the individual and the optimal fitness value of the group to obtain a first update speed; Update the position of the particle according to the first update speed to obtain a first updated position; Calculating an updated fitness value of the particle according to the first updated position; Subtract the updated fitness value from the optimal fitness value of the individual to obtain a fitness difference value; When the fitness difference is greater than or equal to a preset difference, taking the first updated speed as the speed of the particle, and taking the first updated position as the position of the particle; When the fitness difference is less than the preset difference, a mutation algorithm is used to update the first update speed and the first update position to obtain a second update speed and a second update position, and the second update speed is used as the speed of the particle, and the second update position is used as the position of the particle.

8. A data center management system, applying a data center management method according to any one of claims 1 to 7, characterized in that: include: Server energy consumption calculation module, data center cooling module, objective function establishment module, data real-time monitoring module, objective function solution module and task allocation module; The server energy consumption calculation module is used to obtain power data of the data center server and calculate the server energy consumption; The data center cooling module is used to obtain cooling data of the data center server; The objective function establishment module is used to establish an objective function according to the cooling data and the server energy consumption; The data real-time monitoring module is used to obtain the utilization rate and task volume of the data center server in real time; The objective function solving module is used to solve the objective function using a particle swarm algorithm; The task allocation module is used to allocate tasks to the data center according to the solution of the objective function.

9. An electronic device, characterized in that: It includes a processor and a memory, the memory is used to store computer program code, the computer program code includes computer instructions, when the processor executes the computer instructions, the electronic device executes a data center management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a data center management method as described in any one of claims 1 to 7.

Images