Method, system and equipment for measuring and calculating adjustable load capacity of data center based on space-time balance

Abstract

The invention is suitable for the technical field of load capacity measurement and calculation, and provides a method, a system and equipment for measuring and calculating the adjustable load capacity of a data center based on space-time balance, and the method comprises the steps: obtaining the actual load of an excitation type demand response user in a preset time; constructing a mathematical model taking the adjustable load capacity of each data center as a target; determining constraint conditions; the constraint conditions comprise capability constraints of each data center for processing interactive data loads and batch processing data loads within a preset time, and indoor temperature constraints of each data center within the preset time; the extra IT equipment power consumption constraint is generated when the active server operates at the power higher than the power required for processing the preset load capacity; the cooling system power consumption constraint of each data center within the preset time is generated; determining the minimum power consumption and the maximum power consumption of each data center in the preset time, obtaining the adjustable load capacity of each data center, and achieving the efficient utilization of resources and the stable operation of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of load capacity calculation, and in particular to a method, system and device for calculating the adjustable load capacity of a data center based on time-space balance. Background Art

[0002] As electricity users' demand for online services continues to increase, companies need to ensure that their systems can withstand high loads and maintain stable operation to avoid user loss and reputation loss caused by system crashes or slow responses. In addition, effective user load capacity management can also help companies optimize resource utilization, reduce costs, and improve competitiveness. In recent years, with the continuous development of new technologies such as cloud computing, big data, and artificial intelligence, user load capacity measurement has also been evolving. Cloud computing provides elasticity and flexibility, allowing the system to dynamically adjust resource configuration as needed. Big data and artificial intelligence technologies provide more precise methods and tools for user load measurement, making user load capacity measurement more accurate and reliable.

[0003] In the prior art, there are some problems with the method of calculating user load capacity in data centers. For example, relying solely on the rated power of the equipment for calculation may ignore the dynamic changes in the actual operation of the equipment, because the power consumption of the equipment is different under different working conditions, resulting in inaccurate calculations. Furthermore, the use of simulation models for calculations does not take into account a variety of complex factors. One is the time factor. Since time factors such as business cycles and equipment update cycles are not considered, the calculation results may not accurately reflect the actual adjustable capacity in different time periods. The second is the spatial factor. For example, ignoring the layout of the computer room and regional differences will lead to insufficient understanding of load distribution. Since data centers in different regions are affected by factors such as climate, the adjustment capabilities of cooling equipment are different. The lack of consideration of spatial differences will make the overall calculation inaccurate and unable to effectively tap the load adjustment potential of data centers in different spaces and times.

[0004] In view of this, a method, system and device for calculating the adjustable load capacity of a data center based on time-space balance are needed. Summary of the invention

[0005] The embodiments of the present application provide a method, system and device for calculating the adjustable load capacity of a data center based on spatiotemporal balance, which are used to solve the problem of being unable to effectively tap the load regulation potential of a data center in different spaces and times.

[0006] A first aspect of an embodiment of the present application provides a method for calculating adjustable load capacity of a data center based on time-space balance, comprising:

[0007] Obtain the actual load of incentive demand response users within a preset time;

[0008] Constructing a mathematical model targeting the adjustable load capacity of each data center according to the actual load;

[0009] Determining constraints of the mathematical model, the constraints including the ability of each data center to process interactive data load and batch data load within the preset time, the indoor temperature constraint of each data center within the preset time, the additional IT equipment power consumption constraint generated when the active server runs at a power higher than the power required to process the preset load, and the cooling system power consumption constraint of each data center within the preset time;

[0010] Based on the mathematical model and the constraint conditions, the minimum power consumption and the maximum power consumption of each data center during the preset time are determined to obtain the adjustable load capacity of each data center.

[0011] Furthermore, the step of obtaining the actual load of the incentive-type demand response user within a preset time includes:

[0012]

[0013] Where: are the actual load, rigid load, movable load and reducible load of user i at time t, and are the load limits of user i at time t, is the sum of the loads that can be translated by user i, is the maximum value of load reduction that can be achieved by user i.

[0014] Furthermore, the constructing of a mathematical model targeting the adjustable load capacity of each data center according to the actual load includes:

[0015]

[0016] Where: is the total power consumption of data center i at time t, is the power consumption of IT equipment in data center i at time t, is the cooling system power consumption of data center i at time t, is the power consumption of other equipment in data center i.

[0017] Furthermore, the capacity constraints of each data center for processing interactive data load and batch data load within the preset time include:

[0018] The capacity constraints of each data center to handle interactive data loads:

[0019]

[0020] Where: mit is the number of active servers in data center i processing interactive data load at time t, is the interactive data load distributed from the front-end portal server s to data center i at time t, n F is the total number of front-end portal servers, u i is the service rate of the server in data center i, v I is the delay bound for interactive data load, is the interactive data load arriving at the front-end portal server s at time t;

[0021] The capacity constraints of each data center to handle batch data loads:

[0022]

[0023] Where: m iqt is the number of active servers in data center i processing batch data load q at time t, is the number of batch data loads q transferred to data center i for processing in period t, is the delay bound for the batch data load q, and are time periods, Φ iqt is the number of batch data loads q arriving at data center i at time t.

[0024] Furthermore, the indoor temperature constraints of each data center within the preset time include:

[0025]

[0026] Where: and are the indoor and outdoor temperatures of data center i at time t, b 3i and b 4i are the equivalent thermal resistance and thermal capacity of data center i, respectively. it is the cooling power of the cooling system of data center i at time t, and are the minimum and maximum allowable indoor temperatures of data center i, respectively.

[0027] Furthermore, additional IT equipment power constraints that arise when active servers are running at higher power than required to handle the preset load include:

[0028]

[0029] Where: is the total number of servers in data center i, nB is the number of types of batch data load, The additional IT equipment power consumption generated when active servers are running at a higher power than required to handle a certain amount of data load. and are the rated power and idle power of each active server in data center i, respectively.

[0030] Furthermore, the power consumption constraints of the cooling systems of the data centers within the preset time include:

[0031]

[0032] Where: b 1i and b 2i are the empirical parameters of the cooling system power consumption model in data center i, is the upper limit of the cooling system power consumption in data center i.

[0033] Furthermore, the step of determining the minimum power consumption and the maximum power consumption of each data center during the preset time based on the mathematical model and the constraint condition to obtain the adjustable load capacity of each data center includes:

[0034] The power reduction space of data center i at time t is:

[0035]

[0036] The power increase space of data center i at time t is:

[0037]

[0038] Where: is the baseline power of data center i at time t.

[0039] A second aspect of the embodiment of the present application provides a system for calculating adjustable load capacity of a data center based on time-space balance, including:

[0040] An actual load acquisition unit, used to acquire the actual load of the incentive-type demand response user within a preset time;

[0041] A mathematical model building unit, used to build a mathematical model with the adjustable load capacity of each data center as a target according to the actual load;

[0042] a constraint determination unit, configured to determine the constraint of the mathematical model, wherein the constraint includes a capacity constraint of each data center for processing interactive data load and batch data load within the preset time, an indoor temperature constraint of each data center within the preset time, an additional IT equipment power consumption constraint generated when an active server runs at a power higher than that required to process the preset load, and a cooling system power consumption constraint of each data center within the preset time;

[0043] The adjustable load capacity determination unit is used to determine the minimum power consumption and the maximum power consumption of each data center during the preset time based on the mathematical model and the constraint conditions, so as to obtain the adjustable load capacity of each data center.

[0044] A third aspect of an embodiment of the present application provides a computer device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor, when executing the computer-readable instructions, implements the steps of a method for calculating the adjustable load capacity of a data center based on spatiotemporal balance as described in any one of the above.

[0045] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0046] After obtaining the actual load of the incentive-type demand response user within the preset time, the present invention constructs a mathematical model with the adjustable load capacity of each data center as the target according to the actual load; then, the various constraints of the mathematical model are determined under the condition of time-space balance; finally, based on the mathematical model and the constraints, the minimum power consumption and maximum power consumption of each data center within the preset time are determined to obtain the adjustable load capacity of each data center. The present invention adjusts the batch data load allocated to each data center at each time in each data center by considering the interactive data load allocated to each data center by each front-end portal server in consideration of geographical balance and considering time balance, so as to achieve efficient resource utilization and stable operation of the system while meeting user needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention is a flowchart of an embodiment of a method for calculating the adjustable load capacity of a data center based on time-space balance. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] In this embodiment, the method for calculating the adjustable load capacity of a data center based on time-space balance is used to achieve efficient resource utilization and stable operation of the system. The implementation method in this embodiment can be implemented in the system, in the server, or in the terminal, without specific limitation.

[0050] Embodiment 1

[0051] See also Figure 1 The steps of a method for calculating the adjustable load capacity of a data center based on time-space balance in the present invention are as follows:

[0052] S11. Obtaining the actual load of the incentive demand response user within a preset time;

[0053] The load modeling of a single incentive demand response user is as follows:

[0054]

[0055] Where: are the actual load, rigid load, movable load and reducible load of user i at time t, and are the load limits of user i at time t, is the sum of the loads that can be translated by user i, is the maximum value of load reduction that can be achieved by user i.

[0056] S12. Constructing a mathematical model targeting the adjustable load capacity of each data center based on the actual load;

[0057] First, the assumptions for modeling are proposed. The specific details are as follows:

[0058] 1. Assume that a group of geographically distributed data centers belong to the same operator, each data center is connected to a different power system node and is powered only by the grid.

[0059] 2. Assume that the servers in each data center are homogeneous.

[0060] 3. Consider both interactive data load (delay-sensitive data load) and batch data load (delay-tolerant data load). Considering the high service delay tolerance and computing requirements of batch data load, it is assumed that batch data load is only processed locally.

[0061] 4. Assume that the data load allocation decision, cooling system power control decision and power system scheduling decision are updated once in each time period, and the time period (such as 1 hour) is greater than the service delay bound of the interactive data load and less than the service delay bound of the batch data load.

[0062] 5. In each time period, the collaborative optimization of three data center load regulation methods is considered: geographically balanced data load (GWB, i.e., data load distribution / transfer among data centers through data links, which is suitable for delay-sensitive data loads), time-scheduled data load (HWS, i.e., data load distribution / transfer among time periods before the delay tolerance limit, which is suitable for delay-tolerant data loads), and heat storage using building thermal inertia (TSTI).

[0063] A mathematical model with the adjustable load capacity of each data center as the target is constructed. The input of the model includes the amount of interactive data load that arrives, the amount of batch data load that arrives, and the outdoor temperature. The output of the model is the power consumption of each data center. The power consumption of the data center can be divided into three parts: IT equipment power consumption, cooling system power consumption, and other equipment power consumption. Specifically, IT equipment processes interactive and batch data loads to ensure the QoS of users; the cooling system provides cooling power to remove the heat generated by IT equipment and other equipment and the heat transferred from the outdoors to maintain the internal ambient temperature of the data center; other equipment includes lighting systems and power transmission systems to assist the normal operation of the data center. Using GWB, the interactive data load assigned to each data center by each front-end portal server can be adjusted; using HWS, the batch data load assigned to each data center at each time can be adjusted; using TSTI, the cooling power demand of each data center at each time can be adjusted.

[0064] Without considering the auxiliary energy system, the data center load can be modeled as the superposition of three power consumption components. The mathematical model expression is as follows:

[0065]

[0066] Where: is the total power consumption of data center i at time t, is the power consumption of IT equipment in data center i at time t, is the cooling system power consumption of data center i at time t, is the power consumption of other equipment in data center i, which is usually modeled as a constant.

[0067] S13. Determine constraints of the mathematical model, the constraints including the capacity constraints of each data center to process interactive data load and batch data load within a preset time, the indoor temperature constraints of each data center within a preset time, the additional IT equipment power consumption constraints generated when the active server runs at a power higher than the power required to process the preset load, and the cooling system power consumption constraints of each data center within a preset time;

[0068] 1. Considering geographically balanced data load, given constraints: According to the M / M / 1 queuing model, the processing delay time of interactive data load is calculated, and the following constraints must be met:

[0069]

[0070] Converting the above formula into a linear form, we get the following constraints on the ability of each data center to handle interactive data load:

[0071]

[0072] Interactive data loads must satisfy data load balancing constraints:

[0073]

[0074] In the above formula: m it is the number of active servers in data center i processing interactive data load at time t, is the interactive data load distributed from the front-end portal server s to data center i at time t, n F is the total number of front-end portal servers, u i is the service rate of the server in data center i, v I is the delay bound for interactive data load, is the interactive data load arriving at the front-end portal server s at time t.

[0075] 2. Considering the balanced data load in time, the constraints are given: the batch data load is only processed locally, and the delay bound of the batch data load q is set to If in the time period The entire batch data load arriving within the time period can be If the internal processing is done, its QoS can be satisfied, and the above constraints are expressed as:

[0076]

[0077] The processing capacity constraint for batch data load is expressed as follows:

[0078]

[0079] Where: m iqt is the number of active servers in data center i processing batch data load q at time t, is the number of batch data loads q transferred to data center i for processing in period t, Φ iqt is the number of batch data loads q arriving at data center i at time t.

[0080] 3. Based on the law of conservation of energy and the equivalent thermal parameter model of the building, establish the relationship between the cooling power of the refrigeration system and the external environmental parameters, the power consumption of IT equipment, and the power consumption of other systems:

[0081]

[0082] Expressed in discrete time intervals, the following formula can be obtained:

[0083]

[0084] The indoor temperature of data center i must meet the constraints:

[0085]

[0086] Where: and are the indoor and outdoor temperatures of data center i at time t, b 3i and b 4i are the equivalent thermal resistance and thermal capacity of data center i, respectively. it is the cooling power of the cooling system of data center i at time t, and are the minimum and maximum allowable indoor temperatures of data center i, respectively.

[0087] 4. Consider IT equipment power consumption. The IT equipment power consumption is shown in the following formula. The first and second terms are positively correlated with the number of active servers and the amount of data load processed, respectively (derived from the linear relationship between IT equipment power consumption and CPU utilization). The third term represents the additional IT equipment power consumption generated when the active servers run at a higher power than required to process a certain amount of data load (for example, the servers run at a higher frequency):

[0088]

[0089] The number of active servers in the data center and The constraints are as follows:

[0090]

[0091] In the above formula: are the number of edge switches, aggregation switches, and core switches in data center i, respectively. are the rated powers of edge switches, aggregation switches, and core switches in data center i, respectively. is the total number of servers in data center i, n B is the number of types of batch data load, The additional IT equipment power consumption generated when active servers are running at a higher power than required to handle a certain amount of data load. and are the rated power and idle power of each active server in data center i, respectively.

[0092] 5. Considering the power consumption of the cooling system, simplify the modeling as a linear function of the cooling capacity, as shown in the following formula:

[0093]

[0094] There are upper and lower limits on the cooling system power consumption, as shown in the following formula:

[0095]

[0096] In the above formula: b 1i and b 2i are the empirical parameters of the cooling system power consumption model in data center i,

[0097] is the upper limit of the cooling system power consumption in data center i.

[0098] S14. Based on the mathematical model and constraint conditions, the minimum power consumption and the maximum power consumption of each data center during the preset time are determined to obtain the adjustable load capacity of each data center.

[0099] For a preset time period t, data center i can operate under minimum and maximum power consumption conditions, that is, the adjustable capacity range of the data center. The calculation of both is a linear programming problem.

[0100] Therefore, the minimum power consumption of data center i at time t is:

[0101]

[0102] The maximum power consumption of data center i at time t is:

[0103]

[0104] According to the reasonable calculation of the above steps, when the power baseline is determined, the power reduction space of data center i at time t is as follows:

[0105]

[0106] According to the reasonable calculation of the above steps, when the power baseline is determined, the power increase space of data center i at time t is as follows:

[0107]

[0108] Where: is the baseline power of data center i at time t.

[0109] The above embodiment uses a detailed calculation and reasoning process to consider the geographical balance of the interactive data load allocated to each data center by each front-end portal server and to consider the time balance to adjust the batch data load allocated to each data center at each moment, so as to achieve efficient resource utilization and stable operation of the system while meeting user needs.

[0110] Embodiment 2

[0111] A system for calculating adjustable load capacity of a data center based on time-space balance in the present invention comprises the following steps:

[0112] An actual load acquisition unit, used to acquire the actual load of the incentive-type demand response user within a preset time;

[0113] A mathematical model building unit, used for building a mathematical model targeting the adjustable load capacity of each data center according to the actual load;

[0114] a constraint determination unit, used to determine the constraint of the mathematical model, the constraint including the capacity constraint of each data center to process interactive data load and batch data load within a preset time, the indoor temperature constraint of each data center within a preset time, the additional IT equipment power consumption constraint generated when the active server runs at a power higher than the power required to process the preset load, and the cooling system power consumption constraint of each data center within a preset time;

[0115] The adjustable load capacity determination unit is used to determine the minimum power consumption and maximum power consumption of each data center within a preset time based on a mathematical model and constraint conditions, and obtain the adjustable load capacity of each data center.

[0116] For the specific definition of the system, please refer to the definition of the method above, which will not be repeated here. Each module in the above system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0117] Embodiment 3

[0118] The present invention provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above method are implemented.

[0119] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0120] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0121] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0122] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for calculating the adjustable load capacity of a data center based on time-space balance, characterized in that: include: Obtain the actual load of incentive demand response users within a preset time; Constructing a mathematical model targeting the adjustable load capacity of each data center according to the actual load; Determining constraints of the mathematical model, the constraints including the ability of each data center to process interactive data load and batch data load within the preset time, the indoor temperature constraint of each data center within the preset time, the additional IT equipment power consumption constraint generated when the active server runs at a power higher than the power required to process the preset load, and the cooling system power consumption constraint of each data center within the preset time; Based on the mathematical model and the constraint conditions, the minimum power consumption and the maximum power consumption of each data center during the preset time are determined to obtain the adjustable load capacity of each data center.

2. The method for calculating the adjustable load capacity of a data center based on time-space balance according to claim 1 is characterized in that: The obtaining of the actual load of the incentive-type demand response user within a preset time includes: Where: are the actual load, rigid load, movable load and reducible load of user i at time t, and are the load limits of user i at time t, is the sum of the loads that can be translated by user i, is the maximum value of load reduction that can be achieved by user i.

3. The method for calculating the adjustable load capacity of a data center based on time-space balance according to claim 1, characterized in that: The step of constructing a mathematical model targeting the adjustable load capacity of each data center according to the actual load includes: Where: is the total power consumption of data center i at time t, is the power consumption of IT equipment in data center i at time t, is the cooling system power consumption of data center i at time t, is the power consumption of other equipment in data center i.

4. The method for calculating the adjustable load capacity of a data center based on time-space balance according to claim 1, characterized in that: The capacity constraints of each data center for processing interactive data load and batch data load within the preset time include: The capacity constraints of each data center to handle interactive data loads: Where: m it is the number of active servers in data center i processing interactive data load at time t, is the interactive data load distributed from the front-end portal server s to data center i at time t, n F is the total number of front-end portal servers, u i is the service rate of the server in data center i, v I is the delay bound for interactive data load, is the interactive data load arriving at the front-end portal server s at time t; The capacity constraints of each data center to handle batch data loads: Where: m iqt is the number of active servers in data center i processing batch data load q at time t, is the number of batch data loads q transferred to data center i for processing in period t, is the delay bound for the batch data load q, and / 2+1 are time periods, Φ iqt is the number of batch data loads q arriving at data center i at time t.

5. The method for calculating the adjustable load capacity of a data center based on time-space balance according to claim 1, characterized in that: The indoor temperature constraints of each data center within the preset time include: Where: and are the indoor and outdoor temperatures of data center i at time t, b 3i and b 4i are the equivalent thermal resistance and thermal capacity of data center i, respectively. it is the cooling power of the cooling system of data center i at time t, and are the minimum and maximum allowable indoor temperatures of data center i, respectively.

6. The method for calculating the adjustable load capacity of a data center based on time-space balance according to claim 1, characterized in that: Additional IT equipment power constraints that arise when active servers are running at higher power than required to handle the expected load include: Where: is the total number of servers in data center i, n B is the number of types of batch data load, The additional IT equipment power consumption generated when active servers are running at a higher power than required to handle a certain amount of data load. and are the rated power and idle power of each active server in data center i, respectively.

7. The method for calculating the adjustable load capacity of a data center based on time-space balance according to claim 1, characterized in that: The power consumption constraints of the cooling systems of each data center within the preset time include: Where: b 1i and b 2i are the empirical parameters of the cooling system power consumption model in data center i, is the upper limit of the cooling system power consumption in data center i.

8. The method for calculating the adjustable load capacity of a data center based on time-space balance according to claim 1, characterized in that: The step of determining the minimum power consumption and the maximum power consumption of each data center during operation within the preset time based on the mathematical model and the constraint conditions to obtain the adjustable load capacity of each data center includes: The power reduction space of data center i at time t is: The power increase space of data center i at time t is: Where: is the baseline power of data center i at time t.

9. A data center adjustable load capacity measurement system based on time-space balance, characterized in that: include: An actual load acquisition unit, used to acquire the actual load of the incentive-type demand response user within a preset time; A mathematical model building unit, used to build a mathematical model with the adjustable load capacity of each data center as a target according to the actual load; a constraint determination unit, configured to determine the constraint of the mathematical model, wherein the constraint includes a capacity constraint of each data center for processing interactive data load and batch data load within the preset time, an indoor temperature constraint of each data center within the preset time, an additional IT equipment power consumption constraint generated when an active server runs at a power higher than that required to process the preset load, and a cooling system power consumption constraint of each data center within the preset time; The adjustable load capacity determination unit is used to determine the minimum power consumption and the maximum power consumption of each data center during the preset time based on the mathematical model and the constraint conditions, so as to obtain the adjustable load capacity of each data center.

10. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, it implements the steps of the method for calculating the adjustable load capacity of a data center based on time-space balance as described in any one of claims 1-8.

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