Calculation power resource charging method and device based on electricity price of calculation power station, and electronic equipment

Abstract

The invention is suitable for the technical field of computing power charging, and provides a computing power resource charging method and device based on the electricity price of a computing power station, and electronic equipment, and the method comprises the steps: obtaining the computing power resource application information of a user; the computing power resource application information comprises a to-be-applied computing power resource and a computing power resource type of the to-be-applied computing power resource, determining a plurality of computing power stations according to the to-be-applied computing power resource, and for each computing power station, calculating the computing power cost of the to-be-applied computing power resource according to the electricity price of the computing power station for the computing power resource type. According to the invention, the accuracy of computing power resource charging can be improved.

Description

Technical Field

[0001] The present application belongs to the technical field of computing power billing, and in particular, relates to a computing power resource billing method, device, electronic device and computer-readable storage medium based on the electricity price of a computing power station. Background Art

[0002] Computing power is a comprehensive indicator that combines multiple factors such as computing power, transportation power, and storage power. At present, computing power billing mainly adopts the market-based autonomous pricing model. There are three common billing models for computing power stations (data centers): one is to charge according to resource usage, measure the computing indicators during use, and settle the usage of CPU, GPU and other resources based on the set unit price; the second is to charge according to the usage cycle, that is, the usage period can be charged according to monthly, quarterly, annual and other cycles; the third is to charge according to service packages, which include different general computing services, intelligent computing services, and supercomputing services. The current computing power billing method is relatively subjective and cannot accurately bill computing power resources. Summary of the invention

[0003] The embodiments of the present application provide a computing power resource billing method, device and electronic device based on the electricity price of a computing power station, which can improve the accuracy of computing power resource billing.

[0004] In a first aspect, an embodiment of the present application provides a computing power resource billing method based on the electricity price of a computing power station, including:

[0005] Obtaining computing resource application information of the user; the computing resource application information includes the computing resources to be applied for and the computing resource type of the computing resources to be applied for;

[0006] Determine multiple computing power stations according to the computing power resources to be applied for;

[0007] For each of the computing power stations, the computing power fee of the computing power resource to be applied is calculated according to the electricity price of the computing power station for the computing power resource type.

[0008] Optionally, determining a plurality of computing power stations according to the computing power resources to be applied for includes:

[0009] Determine the computing power weight of the computing power resource to be applied for according to the pre-trained computing power resource model; wherein the computing power resource model is trained according to the historical application computing power resources of different computing power stations in the set of computing power stations to be matched, the computing power resource model is used to learn the importance of the historical application computing power resources to the historical application users, and the computing power weight is used to indicate the importance of the computing power resource to be applied for to the user;

[0010] Based on the computing power weights of the computing power resources to be applied for, multiple computing power stations are determined from the set of computing power stations to be matched.

[0011] Optionally, the determining a plurality of computing power stations from the set of computing power stations to be matched based on the computing power weights of the computing power resources to be applied for includes:

[0012] Determine the remaining computing power resources of different computing power stations in the set of computing power stations to be matched, wherein the computing power resource type of the remaining computing power resources is the same as the computing power resource type of the computing power resources to be applied for;

[0013] The multiple computing power stations are determined from the set of computing power stations to be matched according to the remaining computing power resources and the computing power weights corresponding to the remaining computing power resources, wherein the computing power weights corresponding to the remaining computing power resources are the same as the computing power weights of the computing power resources to be applied for.

[0014] Optionally, determining the plurality of computing power stations from the set of computing power stations to be matched according to the remaining computing power resources and the computing power weights corresponding to the remaining computing power resources includes:

[0015] Selecting a preset number of candidate computing power stations from the set of computing power stations to be matched according to the remaining computing power resources and the computing power weight;

[0016] Obtaining historical usage information of the candidate computing power station;

[0017] Performing risk analysis on the candidate computing power station according to the historical usage information; the risk analysis includes one or more of fault analysis, resource utilization analysis and power outage analysis;

[0018] A plurality of computing power stations are selected from the candidate computing power stations according to the result of the risk analysis.

[0019] Optionally, the computing power resource type includes a hardware type and / or a network type, and for each of the computing power stations, calculating the computing power fee of the computing power resource to be applied for according to the electricity price of the computing power station for the computing power resource type includes:

[0020] For each of the computing power stations, when the computing power resource type of the computing power resource to be applied for is hardware type, determine the first type of equipment in the computing power station; wherein the first type of equipment refers to equipment associated with the computing power resource to be applied for of the hardware type;

[0021] Determining a first amount of power consumed by the first category of devices;

[0022] Performing computing power billing on the computing power resources to be applied for according to the first power amount and the electricity price of the computing power station;

[0023] or,

[0024] For each of the computing power stations, when the computing power resource type of the computing power resource to be applied for is a network type, determine a second type of device in the computing power station; wherein the second type of device refers to a device associated with the computing power resource to be applied for of the network type;

[0025] determining a second amount of power consumed by the second type of equipment;

[0026] The computing power resources to be applied for are charged for computing power according to the second power quantity and the electricity price of the computing power station.

[0027] Optionally, the determining a first amount of power consumed by the first category of devices includes:

[0028] In the case where the first type of device includes a server, determining the computing requirements and storage requirements of the computing resources to be applied for;

[0029] Determine a first power consumption of the server according to the computing demand and the storage demand, and determine the power consumption of the server according to the first power consumption and the usage time of the server;

[0030] Determine a second power consumption of an auxiliary device of the server by using the first power consumption, and determine the device power of the auxiliary device of the server according to the second power consumption and the usage time of the auxiliary device of the server, wherein the auxiliary device of the server is a device that assists the server in working;

[0031] The first power is obtained by summing up the power of the server and the power of the auxiliary devices of the server.

[0032] Optionally, the determining the second amount of power consumed by the second category of devices includes:

[0033] In the case where the second type of device includes a switch, determining a third power consumption of the switch and a power consumption of the switch according to the usage information of the computing power resources to be applied for;

[0034] Determine a fourth power consumption of an auxiliary device of the switch by using the third power consumption, and determine the device power of the auxiliary device of the switch according to the fourth power consumption and the usage time of the auxiliary device of the switch, wherein the auxiliary device of the switch is a device that assists the switch in working;

[0035] The second power is obtained by summing up the power of the switch and the power of the auxiliary device of the switch.

[0036] In a second aspect, an embodiment of the present application provides a computing resource billing device based on the electricity price of a computing power station, including:

[0037] A computing power resource application information acquisition module is used to obtain the computing power resource application information of the user; the computing power resource application information includes the computing power resources to be applied for and the computing power resource type of the computing power resources to be applied for;

[0038] A computing power station determination module, used to determine multiple computing power stations according to the computing power resources to be applied for;

[0039] The computing power resource billing module is used to calculate the computing power fee of the computing power resource to be applied for each computing power station according to the electricity price of the computing power station for the computing power resource type.

[0040] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the computing power resource billing method based on the electricity price of the computing power station as described in the first aspect above when executing the computer program.

[0041] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the computing power resource billing method based on the electricity price of the computing power station as described in the first aspect above.

[0042] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, the electronic device executes the computing power resource billing method based on the electricity price of the computing power station as described in any one of the first aspects above.

[0043] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0044] In the embodiment of the present application, the computing power billing for the computing power resources to be applied for is performed by the electricity prices of different computing power stations, which can improve the accuracy of computing power billing. Specifically, multiple computing power stations are determined according to the computing power resource application information. When the computing power billing for the computing power resources to be applied for different computing power stations is performed, the computing power costs of the computing power resources to be applied for in different computing power stations are calculated according to the computing power resource types, which means that for the computing power resources to be applied for different types of computing power resources, the corresponding types of computing power billing can be carried out in a targeted manner, thereby improving the accuracy of computing power billing. At the same time, the computing power cost is determined according to the electricity price of the computing power station, indicating that the computing power billing for the computing power resources to be applied can be performed according to the electricity prices of different computing power stations, which means that for the computing power resources to be applied for different types of computing power resources, the computing power billing can be unified in the form of electricity price, further improving the standardization and accuracy of computing power billing. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 It is a flowchart of a computing power resource billing method based on the electricity price of a computing power station provided in an embodiment of the present application;

[0047] Figure 2 It is a structural schematic diagram of a computing power resource billing device based on the electricity price of a computing power station provided in an embodiment of the present application;

[0048] Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0050] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0051] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0052] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0053] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0054] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0055] As the base of computing power, computing power stations (data centers) often have to consider construction costs and operating costs when charging computing power. However, due to the lack of unified standards for construction costs and operating costs of computing power stations, for example, the western region has a greater advantage over the eastern region in terms of construction costs due to low labor costs and land costs. At the same time, in terms of operating costs, due to the serious east-west differences in electricity prices, the electricity prices in the western region also have a greater advantage over the eastern region. Therefore, the computing power billing of different computing power stations mainly adopts an independent pricing model, which has large differences. At the same time, due to the large number of types of computing power resources and the inconsistent billing standards of various computing power stations, when using the independent pricing model for computing power billing, the applicant (such as users, enterprises, etc.) cannot know the specific pricing method, nor is it clear whether the current computing power billing is reasonable; for the supplier (computing power station, data center, etc.), since there is no unified standard, it is also unknown whether the current computing power billing is appropriate. Therefore, the current computing power billing method is relatively subjective, with inconsistent standards and the problem of being unable to accurately bill computing power resources.

[0056] In order to improve the accuracy of computing power resource billing, the present application provides a computing power resource billing method based on the electricity price of the computing power station.

[0057] Figure 1 A flow chart of a computing resource billing method based on the electricity price of a computing station provided in an embodiment of the present application is shown, and the details are as follows:

[0058] S1. Obtain the computing power resource application information of the user; the computing power resource application information includes the computing power resources to be applied for and the computing power resource type of the computing power resources to be applied for.

[0059] In the embodiment of the present application, the above-mentioned user refers to the applicant of computing power resources, including but not limited to individual users, corporate users, etc. The above-mentioned computing power resource application information is used to record the computing power resources required by the user, and may include the computing power resources to be applied for and the computing power resource types of the computing power resources to be applied for. Of course, the above-mentioned computing power resource application information may also include the usage information of the computing power resources to be applied for (such as the usage time), user information, user demand information (such as the requirements for the computing power station), etc., which are not limited here.

[0060] In an optional embodiment of the present application, the above-mentioned computing resources to be applied for may include hardware resources (number of chips (cores), memory size (G), storage size (G)), network resources (domain name, port), operating system, database, middleware, etc.

[0061] S2. Determine multiple computing power stations based on the computing power resources to be applied for.

[0062] In the embodiment of the present application, since the types and quantities of computing resources to be applied for are different, the number of computing stations and the richness of their computing resources are also different, so determining multiple computing stations through the computing resources to be applied for can improve the accuracy of computing resource billing. For example, at least two computing stations are determined based on the computing resources to be applied for to improve the accuracy of computing resource billing.

[0063] In some embodiments, multiple computing power stations can be determined based on the importance of the computing power resources to be applied for the user. For example, if the user attaches more importance to hardware resources such as CPU resources and GPU resources, multiple computing power stations with relatively abundant CPU resources and GPU resources can be selected. Alternatively, multiple computing power stations can be screened out based on the current computing power resource billing information of the above-mentioned computing power resources to be applied for by different computing power stations. For example, the usage price of the computing power resources to be applied can be determined based on the usage time (or usage amount) of the computing power resources to be applied and the computing power resource billing information corresponding to the computing power resources to be applied, and multiple computing power stations can be selected based on the usage price of the computing power resources to be applied, wherein the above-mentioned computing power resource billing information may include CPU resource fees, GPU resource fees, memory resource fees, hard disk resource fees, storage time fees, IP usage fees, etc.

[0064] S3. For each of the above computing power stations, calculate the computing power fee of the above computing power resources to be applied for according to the electricity price of the above computing power station for the above computing power resource type.

[0065] In the embodiment of the present application, for each computing power station, since the location of the computing power station is different and the computing power resource types of different computing power resources to be applied for are also different, the computing power billing of the computing power resources to be applied for of different computing power resource types is performed through the electricity price of the computing power station. The computing power billing can be unified into the form of electricity price, and each computing power station can have a unified standard, so as to perform computing power billing more accurately.

[0066] In the embodiment of the present application, the computing power billing for the computing power resources to be applied for is performed by the electricity prices of different computing power stations, which can improve the accuracy of computing power billing. Specifically, multiple computing power stations are determined according to the computing power resource application information. When the computing power billing for the computing power resources to be applied for different computing power stations is performed, the computing power costs of the computing power resources to be applied for in different computing power stations are calculated according to the computing power resource types, which means that for the computing power resources to be applied for different types of computing power resources, the corresponding types of computing power billing can be carried out in a targeted manner, thereby improving the accuracy of computing power billing. At the same time, the computing power cost is determined according to the electricity price of the computing power station, indicating that the computing power billing for the computing power resources to be applied can be performed according to the electricity prices of different computing power stations, which means that for the computing power resources to be applied for different types of computing power resources, the computing power billing can be unified in the form of electricity price, further improving the standardization and accuracy of computing power billing.

[0067] In the embodiment of the present application, the above-mentioned determination of multiple computing power stations according to the above-mentioned computing power resources to be applied for includes:

[0068] Determine the computing power weight of the computing power resource to be applied for according to the pre-trained computing power resource model; wherein the computing power resource model is trained according to the historical application computing power resources of different computing power stations in the set of computing power stations to be matched, the computing power resource model is used to learn the importance of the historical application computing power resources to the historical application users, and the computing power weight is used to indicate the importance of the computing power resource to be applied for to the user;

[0069] Based on the computing power weights of the computing power resources to be applied for, multiple computing power stations are determined from the set of computing power stations to be matched.

[0070] In some embodiments, the computing power resource model can be obtained through neural network training. For example, the neural network can be a multi-layer perceptron (MLP) or a convolutional neural network (CNN). During the training process, the model parameters can be optimized based on the historically applied computing power resources and their corresponding computing power stations using optimization algorithms such as cross entropy loss function and gradient descent. Through repeated iterative training, the computing power resource model can learn the best combination of different historically applied computing power resources, and obtain the importance of different historically applied computing power resources to historically applied users, so that the computing power weight obtained by the computing power resource model can determine the importance of the computing power resources to be applied for the user.

[0071] In some other embodiments, the computing power resource model may also be one of a logistic regression model (Logisticregression, LR), a random forest model (Random Forest, RF) and an extreme gradient boosting model (ExtremeGradientBoosting, XGBoost).

[0072] Optionally, the determining of multiple computing power stations from the set of computing power stations to be matched based on the computing power weights of the computing power resources to be applied for includes:

[0073] Determine the remaining computing power resources of different computing power stations in the above-mentioned set of computing power stations to be matched, wherein the computing power resource type of the above-mentioned remaining computing power resources is the same as the computing power resource type of the above-mentioned computing power resources to be applied for;

[0074] The above-mentioned multiple computing power stations are determined from the above-mentioned set of computing power stations to be matched according to the above-mentioned remaining computing power resources and the computing power weights corresponding to the above-mentioned remaining computing power resources, wherein the computing power weights corresponding to the above-mentioned remaining computing power resources are the same as the computing power weights of the above-mentioned computing power resources to be applied for.

[0075] In some embodiments, the above-mentioned remaining computing power resources refer to idle computing power resources in the computing power station that have the same computing power resource type as the computing power resources to be applied for. Multiple computing power stations can be determined from the set of computing power stations to be matched through the remaining computing power resources and the computing power weights corresponding to the remaining computing power resources. For example, the remaining computing power resources and the computing power weights corresponding to the remaining computing power resources are multiplied, and multiple computing power stations with larger multiplication results are selected.

[0076] In the embodiment of the present application, the above-mentioned determining the above-mentioned multiple computing power stations from the above-mentioned set of computing power stations to be matched according to the above-mentioned remaining computing power resources and the computing power weights corresponding to the above-mentioned remaining computing power resources includes:

[0077] Select a preset number of candidate computing power stations from the set of computing power stations to be matched according to the remaining computing power resources and the computing power weights;

[0078] Obtain the historical usage information of the above candidate computing power stations;

[0079] Performing risk analysis on the candidate computing power stations based on the historical usage information; the risk analysis includes one or more of failure analysis, resource utilization analysis, and power outage analysis;

[0080] According to the results of the above risk analysis, multiple computing power stations are selected from the above candidate computing power stations.

[0081] In some embodiments, a preset number of candidate computing power stations can be selected from the set of computing power stations to be matched according to the remaining computing power resources and computing power weights. For example, the computing power stations in the set of computing power stations to be matched are arranged in order from large to small according to the product of the remaining computing power resources and the computing power weights, and then a preset number of computing power stations are selected as candidate computing power stations. The above-mentioned historical usage information includes the equipment usage information of the candidate computing power stations within a period of time (such as six months), which may include indicators such as resource utilization, performance and availability, and may also include power outage data, fault data, etc. of the candidate computing power stations within a period of time. Since the location of the computing power station, the type of equipment, and the stability of the equipment power supply will affect the use of the computing power station, the risk analysis of the candidate computing power stations is carried out through historical usage information. Since the risk analysis includes one or more of fault analysis, resource utilization analysis, and power outage analysis, fault analysis refers to the analysis of whether the computing power station (or the equipment in the computing power station) may fail within a certain period of time in the future (for example, the use time of the computing power resources to be applied for), and the probability of failure when failure may occur; resource utilization analysis refers to the analysis of the utilization efficiency of different computing power resources by the computing power station within a certain period of time in the future; power outage analysis refers to the analysis of whether the computing power station may have a power outage within a certain period of time in the future, and the probability of power outage when power outage may occur. Through the above risk analysis, different computing power stations can be comprehensively evaluated from different dimensions, so computing power stations can be selected more accurately to ensure the accuracy of computing power station selection.

[0082] In an optional embodiment, a neural network may be trained based on the above-mentioned historical usage information, and the trained neural network may be used to perform risk analysis on different candidate computing power stations, thereby accurately selecting computing power stations.

[0083] In an embodiment of the present application, the above-mentioned computing power resource types include hardware and / or network types. Correspondingly, the computing power resources to be applied for in the hardware type may include one or more of CPU resources, GPU resources, memory resources, hard disk resources, etc.; the computing power resources to be applied for in the network type may include one or more of domain names, ports, etc.

[0084] In the embodiment of the present application, for each of the computing power stations, the computing power fee of the computing power resource to be applied for is calculated according to the electricity price of the computing power station for the computing power resource type, including:

[0085] For each of the above computing power stations, when the computing power resource type of the computing power resources to be applied for is hardware type, determine the first type of equipment in the above computing power station; wherein the first type of equipment refers to equipment associated with the computing power resources to be applied for of the hardware type;

[0086] Determining a first amount of power consumed by the first category of equipment;

[0087] Performing computing power billing on the computing power resources to be applied for according to the first amount of electricity and the electricity price of the computing power station;

[0088] or,

[0089] For each of the above computing power stations, when the computing power resource type of the above computing power resources to be applied for is a network type, determine the second type of equipment in the above computing power station; wherein the above second type of equipment refers to equipment associated with the above computing power resources to be applied for of the network type;

[0090] Determining a second amount of power consumed by the second type of equipment;

[0091] The computing power resources to be applied for are charged based on the second electricity quantity and the electricity price of the computing power station.

[0092] In some embodiments, computing resources to be applied for different computing resource types can be charged for computing power in the form of electricity prices through different types of associated devices in the computing power station. For example, in the case where the computing resource type of the computing power resources to be applied is hardware, since the computing power resources to be applied for hardware need to be directly operated through the corresponding first-class devices (such as servers, disks, etc.), the computing power of the hardware computing resources to be applied can be charged for computing power by combining the power consumed by the first-class devices with the electricity price of the computing power station; in the case where the computing resource type of the computing power resources to be applied is network, since the computing power resources to be applied for network need to be indirectly operated by the corresponding second-class devices (such as switches, etc.), the computing power of the network computing resources to be applied can be charged for computing power by combining the power consumed by the second-class devices with the electricity price of the computing power station. The accuracy of computing power billing can be improved by charging the computing power of the corresponding computing resource types through different types of devices in the computing power station.

[0093] In the embodiment of the present application, the determining of the first amount of power consumed by the first type of device includes:

[0094] In the case where the first type of device includes a server, determining the computing requirements and storage requirements of the computing resources to be applied for;

[0095] Determine a first power consumption of the server according to the computing demand and the storage demand, and determine the power consumption of the server according to the first power consumption and the usage time of the server;

[0096] Determine a second power consumption of an auxiliary device of the server by using the first power consumption, and determine the device power of the auxiliary device of the server according to the second power consumption and the usage time of the auxiliary device of the server, wherein the auxiliary device of the server is a device that assists the work of the server;

[0097] The first power is obtained by summing up the power of the server and the power of the auxiliary devices of the server.

[0098] In some embodiments, when the first type of equipment includes a server, the above computing demand is used to reflect the computing power required by the computing resources to be applied for, for example, it can be expressed by the number of floating point operations (FLOPS); the above storage demand is used to reflect the storage capacity required by the computing resources to be applied for, for example, it can be expressed by the capacity of the disk array. The above first power consumption is used to reflect the power consumption required by the server to achieve the above computing demand and the above storage demand. At the same time, in order to ensure the normal operation of the server, the auxiliary equipment of the server is required to perform auxiliary work, including cooling equipment, power supply equipment, etc. By summarizing the power consumed by the server and the auxiliary equipment of the server, the power consumption required by the hardware computing resources to be applied for can be accurately reflected.

[0099] In an optional embodiment, it is assumed that the computing demand of the computing power resources to be applied for is the computing power demand of CPU resources, which is related to three factors: the number of CPU cores, the main frequency of a single core, and the floating-point computing capability of a single CPU cycle. The corresponding calculation formula is: [single-precision computing power] = [number of CPU cores] * [main frequency of a single core] * [floating-point computing capability of a single CPU cycle], where [single-precision computing power] represents the computing demand. In the case of a computing demand of 80T single-precision computing power, if a server is equipped with an Intel E5 processor (2G main frequency, 6 cores), 64 cores, it can provide 64*2*64=8T (FLOPS) of single-precision computing power, which means that 10 servers are needed. Assuming that the power of each server is about 500W, 10 servers are 5000W, and the efficiency index PUE of the computing power station is measured to be 1.2, the total power consumed is 5000W*1.2=6000W, that is, the first power consumption mentioned above, and the power consumption for 1 day is 6kW*24h=144kWh, that is, the server power consumption. Similarly, if the storage demand is 2TB capacity storage, and the power of the disk array corresponding to the storage demand is 500W, the total power consumed is 500W*1.2=600W, and the calculation method of the corresponding server power consumption is similar, which will not be repeated here.

[0100] Further, assuming that the auxiliary equipment of the above-mentioned server includes cooling equipment (such as air conditioning) and power supply equipment (such as uninterruptible power supply (UPS)), the above-mentioned 6000W is the heat that needs to be removed by the cooling equipment. If the energy efficiency ratio of the cooling equipment is 4, the energy consumption of the cooling equipment is 6000W÷4=1500W; if the power supply equipment is 96%, 6000W*(1-96%) is the corresponding loss power consumption. The total power of the cooling equipment (such as air conditioning) and the power supply equipment is the above-mentioned second power consumption. Multiplying it by the corresponding usage time can obtain the equipment power of the server's auxiliary equipment. The corresponding first power can be obtained by adding the server power and the equipment power of the server's auxiliary equipment.

[0101] It should be noted that the PUE of the computing power station efficiency index represents the ratio of the energy consumption of all devices to the energy consumption of IT load devices. Therefore, in order to represent the overall energy consumption of the computing power station, IT load devices such as servers need to be multiplied by PUE, but auxiliary devices do not need to be multiplied by PUE. For example, when the efficiency index PUE of the computing power station is 1.2, 1 can represent the IT load devices in the computing power station, and 0.2 represents the auxiliary devices in the computing power station.

[0102] In the embodiment of the present application, the determining of the second amount of power consumed by the second type of device includes:

[0103] In the case where the second type of device includes a switch, determining the third power consumption of the switch and the power consumption of the switch according to the usage information of the computing power resources to be applied for;

[0104] Determine a fourth power consumption of the auxiliary device of the switch by using the third power consumption, and determine the device power of the auxiliary device of the switch according to the fourth power consumption and the usage time of the auxiliary device of the switch, wherein the auxiliary device of the switch is a device that assists the switch in working;

[0105] The second power is obtained by summing up the power of the switch and the power of the auxiliary equipment of the switch.

[0106] In some embodiments, when the second type of device includes a switch, the usage information of the computing power resources to be applied for represents the usage of the computing power resources to be applied for of the network type, such as the number of ports, the usage time of the ports, etc., and the usage information is linearly related to the power consumption of the switch. At the same time, the normal operation of the switch also requires the assistance of the corresponding auxiliary devices, so the power consumed by the switch and its auxiliary devices can accurately reflect the power consumption required by the computing power resources to be applied for of the network type.

[0107] In an optional embodiment, assuming that the computing power resources to be applied require 4 ports, taking the switch of the computing power station as an ordinary 8-port Gigabit switch as an example, it means that there are 8 ports responsible for data input or output, and its rated power is 5W, that is, the third power consumption, and the working voltage is 100V-240V, 50 / 60Hz. Assuming that the switch works 10 hours a day and 365 days a year, the annual power consumption is approximately 5W*10h*365d=18250Wh. In the case of 4 ports, if the efficiency index PUE of the computing power station is 1.2, the power consumption of the switch is 18250Wh*0.5*1.2. The calculation method of the equipment power of the auxiliary equipment of the switch is similar to the above embodiment, which will not be repeated here.

[0108] In another optional embodiment of the present application, it also includes:

[0109] The above-mentioned multiple computing power stations are billed and evaluated based on the above-mentioned computing power fees.

[0110] In some embodiments, after obtaining the computing power costs of different computing power stations, multiple computing power stations can be evaluated for billing according to the size of the computing power costs. Since the computing power costs are calculated through electricity prices, the billing evaluation of different computing power stations can be performed more accurately. At the same time, further evaluation can be performed based on the computing power costs combined with the results of the above risk analysis, so as to accurately evaluate different computing power stations.

[0111] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0112] Corresponding to the computing power resource billing method based on the computing power station electricity price described in the above embodiment, Figure 2 A structural schematic diagram of a computing power resource billing device based on the electricity price of a computing power station provided in an embodiment of the present application is shown. For the sake of convenience of explanation, only the parts related to the embodiment of the present application are shown.

[0113] Reference Figure 2 The device may be a computing power resource billing device 21 based on the electricity price of a computing power station. The computing power resource billing device 21 based on the electricity price of a computing power station may include a computing power resource application information acquisition module 211, a computing power station determination module 212 and a computing power resource billing module 213.

[0114] Reference Figure 2 The computing power resource billing device 21 based on the computing power station electricity price includes:

[0115] The computing resource application information acquisition module 211 is used to obtain the computing resource application information of the user; the computing resource application information includes the computing resource to be applied for and the computing resource type of the computing resource to be applied for;

[0116] The computing power station determination module 212 is used to determine multiple computing power stations according to the computing power resources to be applied for;

[0117] The computing power resource billing module 213 is used to calculate the computing power fee of the computing power resource to be applied for each computing power station according to the electricity price of the computing power station for the computing power resource type.

[0118] In some embodiments, the computing power station determination module 212 determines multiple computing power stations according to the computing power resources to be applied for through the following steps, including:

[0119] Determine the computing power weight of the computing power resource to be applied for according to the pre-trained computing power resource model; wherein the computing power resource model is trained according to the historical application computing power resources of different computing power stations in the set of computing power stations to be matched, the computing power resource model is used to learn the importance of the historical application computing power resources to the historical application users, and the computing power weight is used to indicate the importance of the computing power resource to be applied for to the user;

[0120] Based on the computing power weights of the computing power resources to be applied for, multiple computing power stations are determined from the set of computing power stations to be matched.

[0121] In some embodiments, the computing power station determination module 212 determines multiple computing power stations from the set of computing power stations to be matched based on the computing power weights of the computing power resources to be applied for through the following steps, including:

[0122] Determine the remaining computing power resources of different computing power stations in the set of computing power stations to be matched, wherein the computing power resource type of the remaining computing power resources is the same as the computing power resource type of the computing power resources to be applied for;

[0123] The multiple computing power stations are determined from the set of computing power stations to be matched according to the remaining computing power resources and the computing power weights corresponding to the remaining computing power resources, wherein the computing power weights corresponding to the remaining computing power resources are the same as the computing power weights of the computing power resources to be applied for.

[0124] In some embodiments, the computing power station determination module 212 determines the plurality of computing power stations from the set of computing power stations to be matched according to the remaining computing power resources and the computing power weights corresponding to the remaining computing power resources through the following steps, including:

[0125] Selecting a preset number of candidate computing power stations from the set of computing power stations to be matched according to the remaining computing power resources and the computing power weight;

[0126] Obtaining historical usage information of the candidate computing power station;

[0127] Performing risk analysis on the candidate computing power station according to the historical usage information; the risk analysis includes one or more of fault analysis, resource utilization analysis and power outage analysis;

[0128] A plurality of computing power stations are selected from the candidate computing power stations according to the result of the risk analysis.

[0129] In some embodiments, the computing power resource type includes hardware type and / or network type, and the computing power resource billing module 213 calculates the computing power fee of the computing power resource to be applied for each computing power station according to the electricity price of the computing power station for the computing power resource type through the following steps, including:

[0130] For each of the computing power stations, when the computing power resource type of the computing power resource to be applied for is hardware type, determine the first type of equipment in the computing power station; wherein the first type of equipment refers to equipment associated with the computing power resource to be applied for of the hardware type;

[0131] Determining a first amount of power consumed by the first category of devices;

[0132] Performing computing power billing on the computing power resources to be applied for according to the first power amount and the electricity price of the computing power station;

[0133] or

[0134] For each of the computing power stations, when the computing power resource type of the computing power resource to be applied for is a network type, determine a second type of device in the computing power station; wherein the second type of device refers to a device associated with the computing power resource to be applied for of the network type;

[0135] determining a second amount of power consumed by the second type of equipment;

[0136] The computing power resources to be applied for are charged for computing power according to the second power quantity and the electricity price of the computing power station.

[0137] In some embodiments, the computing resource billing module 213 determines the first amount of electricity consumed by the first type of device through the following steps, including:

[0138] In the case where the first type of device includes a server, determining the computing requirements and storage requirements of the computing resources to be applied for;

[0139] Determine a first power consumption of the server according to the computing demand and the storage demand, and determine the power consumption of the server according to the first power consumption and the usage time of the server;

[0140] Determine a second power consumption of an auxiliary device of the server by using the first power consumption, and determine the device power of the auxiliary device of the server according to the second power consumption and the usage time of the auxiliary device of the server, wherein the auxiliary device of the server is a device that assists the server in working;

[0141] The first power is obtained by summing up the power of the server and the power of the auxiliary devices of the server.

[0142] In some embodiments, the computing resource billing module 213 determines the second amount of electricity consumed by the second type of device through the following steps, including:

[0143] In the case where the second type of device includes a switch, determining a third power consumption of the switch and a power consumption of the switch according to the usage information of the computing power resources to be applied for;

[0144] Determine a fourth power consumption of an auxiliary device of the switch by using the third power consumption, and determine the device power of the auxiliary device of the switch according to the fourth power consumption and the usage time of the auxiliary device of the switch, wherein the auxiliary device of the switch is a device that assists the switch in working;

[0145] The second power is obtained by summing up the power of the switch and the power of the auxiliary device of the switch.

[0146] In some other embodiments, the computing power resource billing device 21 based on the computing power station electricity price also includes an evaluation module, and the evaluation module is used to perform billing evaluation on the multiple computing power stations according to the computing power costs.

[0147] It should be noted that the information interaction, execution process, etc. between the devices / units are based on the same concept as the method embodiments of the present application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0148] Figure 3 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 3 As shown, the electronic device 3 of this embodiment includes: at least one processor 30 ( Figure 3 The at least one processor 30 includes a memory 31 and a computer program 33 stored in the memory 31 and executable on the at least one processor 30. When the processor 30 executes the computer program 33, the steps in any of the method embodiments are implemented.

[0149] The electronic device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of the electronic device 3 and does not constitute a limitation of the electronic device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include an input sending device, a network access device, a bus, etc.

[0150] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0151] In some embodiments, the memory 31 may be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 31 may also be an external storage device of the electronic device 3, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 3. Further, the memory 31 may also include both an internal storage unit of the electronic device 3 and an external storage device. The memory 31 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 31 may also be used to temporarily store data that has been sent or is to be sent.

[0152] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the functional units and modules is used as an example. In practical applications, the function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment 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 integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0153] An embodiment of the present application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps in any of the method embodiments when executing the computer program.

[0154] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the various method embodiments can be implemented.

[0155] An embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in the various method embodiments when executing the computer program product.

[0156] 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 present application implements all or part of the process in the embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of each method embodiment when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / electronic device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0157] In the embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0158] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. 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 this application.

[0159] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0160] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0161] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A computing power resource billing method based on the electricity price of a computing power station, characterized in that: include: Obtaining computing resource application information of the user; the computing resource application information includes the computing resources to be applied for and the computing resource type of the computing resources to be applied for; Determine multiple computing power stations according to the computing power resources to be applied for; For each of the computing power stations, the computing power fee of the computing power resource to be applied is calculated according to the electricity price of the computing power station for the computing power resource type.

2. The computing power resource billing method based on the computing power station electricity price according to claim 1, characterized in that: The determining of a plurality of computing power stations according to the computing power resources to be applied for includes: Determine the computing power weight of the computing power resource to be applied for according to the pre-trained computing power resource model; wherein the computing power resource model is trained according to the historical application computing power resources of different computing power stations in the set of computing power stations to be matched, the computing power resource model is used to learn the importance of the historical application computing power resources to the historical application users, and the computing power weight is used to indicate the importance of the computing power resource to be applied for to the user; Based on the computing power weights of the computing power resources to be applied for, multiple computing power stations are determined from the set of computing power stations to be matched.

3. The computing power resource billing method based on the computing power station electricity price according to claim 2, characterized in that: The determining of a plurality of computing power stations from the set of computing power stations to be matched based on the computing power weights of the computing power resources to be applied for includes: Determine the remaining computing power resources of different computing power stations in the set of computing power stations to be matched, wherein the computing power resource type of the remaining computing power resources is the same as the computing power resource type of the computing power resources to be applied for; The multiple computing power stations are determined from the set of computing power stations to be matched according to the remaining computing power resources and the computing power weights corresponding to the remaining computing power resources, wherein the computing power weights corresponding to the remaining computing power resources are the same as the computing power weights of the computing power resources to be applied for.

4. The computing power resource billing method based on the computing power station electricity price according to claim 3, characterized in that: The determining the plurality of computing power stations from the set of computing power stations to be matched according to the remaining computing power resources and the computing power weights corresponding to the remaining computing power resources includes: Selecting a preset number of candidate computing power stations from the set of computing power stations to be matched according to the remaining computing power resources and the computing power weight; Obtaining historical usage information of the candidate computing power station; Performing risk analysis on the candidate computing power station according to the historical usage information; the risk analysis includes one or more of fault analysis, resource utilization analysis and power outage analysis; A plurality of computing power stations are selected from the candidate computing power stations according to the result of the risk analysis.

5. The computing power resource billing method based on the computing power station electricity price according to any one of claims 1 to 4, characterized in that: The computing power resource type includes hardware type and / or network type. For each computing power station, the computing power fee of the computing power resource to be applied for is calculated according to the electricity price of the computing power station for the computing power resource type, including: For each of the computing power stations, when the computing power resource type of the computing power resource to be applied for is hardware type, determine the first type of equipment in the computing power station; wherein the first type of equipment refers to equipment associated with the computing power resource to be applied for of the hardware type; Determining a first amount of power consumed by the first category of devices; Performing computing power billing on the computing power resources to be applied for according to the first power amount and the electricity price of the computing power station; or, For each of the computing power stations, when the computing power resource type of the computing power resource to be applied for is a network type, determine a second type of device in the computing power station; wherein the second type of device refers to a device associated with the computing power resource to be applied for of the network type; determining a second amount of power consumed by the second type of equipment; The computing power resources to be applied for are charged for computing power according to the second power quantity and the electricity price of the computing power station.

6. The computing power resource billing method based on the computing power station electricity price according to claim 5, characterized in that: The determining a first amount of power consumed by the first category of devices includes: In the case where the first type of device includes a server, determining the computing requirements and storage requirements of the computing resources to be applied for; Determine a first power consumption of the server according to the computing demand and the storage demand, and determine the power consumption of the server according to the first power consumption and the usage time of the server; Determine a second power consumption of an auxiliary device of the server by using the first power consumption, and determine the device power of the auxiliary device of the server according to the second power consumption and the usage time of the auxiliary device of the server, wherein the auxiliary device of the server is a device that assists the server in working; The first power is obtained by summing up the power of the server and the power of the auxiliary devices of the server.

7. The computing power resource billing method based on the computing power station electricity price according to claim 5, characterized in that: The determining the second amount of power consumed by the second type of device includes: In the case where the second type of device includes a switch, determining a third power consumption of the switch and a power consumption of the switch according to the usage information of the computing power resources to be applied for; Determine a fourth power consumption of an auxiliary device of the switch by using the third power consumption, and determine the device power of the auxiliary device of the switch according to the fourth power consumption and the usage time of the auxiliary device of the switch, wherein the auxiliary device of the switch is a device that assists the switch in working; The second power is obtained by summing up the power of the switch and the power of the auxiliary device of the switch.

8. A computing power resource billing device based on the electricity price of a computing power station, characterized in that: include: A computing power resource application information acquisition module is used to obtain the computing power resource application information of the user; the computing power resource application information includes the computing power resources to be applied for and the computing power resource type of the computing power resources to be applied for; A computing power station determination module, used to determine multiple computing power stations according to the computing power resources to be applied for; The computing power resource billing module is used to calculate the computing power fee of the computing power resource to be applied for each computing power station according to the electricity price of the computing power station for the computing power resource type.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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