Server power consumption prediction method and device based on service scene

Abstract

The invention provides a server power consumption prediction method and device based on a service scene, relates to the field of artificial intelligence, can also be used in the financial field, and comprises the steps: determining a service field to which a server belongs and a server node type according to an obtained service application name and a service node type; determining service-related power consumption parameters and node-related power consumption parameters of the server according to the service field to which the server belongs and the node type of the server; and performing server power consumption prediction based on a service scene according to the service-related power consumption parameters and the node-related power consumption parameters. According to the method, statistics and model calculation can be carried out by collecting power consumption of different types of bank business application servers, and power consumption prediction of the servers which are not online is realized.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence and can be used in the field of intelligent operation and maintenance and the financial field. Specifically, it is a method and device for predicting server power consumption based on business scenarios. Background Art

[0002] With the rapid development of information technology, the massive server equipment in the computer room of the bank data center has become an important foundation for the technical fields such as big data and cloud computing. Due to the rapid growth of the number of server resources brought about by the architecture transformation, a large amount of computer room resources are needed to deploy servers. The unreasonable planning and deployment of computer room servers, insufficient utilization of cabinet resources and other resource waste problems are prominent, which also cause many problems such as limited business growth and increased operating costs. At the same time, the traditional bank data center server relies on experience and server configuration in the server rack planning process, and the shortcomings include at least:

[0003] First, the standardized server cabinets in the computer room have an upper limit on the power consumption of the devices that can be installed. If the power consumption of the server exceeds the limit, the switch will be disconnected, which will then cause the server to crash. When the server is put on the shelf, the operation and maintenance personnel may plan and deploy the server based on experience. Only after the system environment and business applications are officially launched can they truly understand the real-time power consumption of the corresponding server and the power consumption of the cabinet. If the power consumption exceeds the limit, the overused server needs to be removed from the shelf. If the cabinet power is insufficient, the remaining power resources need to be re-planned, which can easily cause repeated work and affect the rapid launch of business applications and the efficient use of cabinet resources.

[0004] Second, calculating server power consumption based on server configuration is also a commonly used power consumption model prediction method. However, the power consumption model calculated based on server configuration requires understanding the server's CPU, memory, hard disk, network card and other hardware configurations and quantities. The calculated server power consumption is inaccurate, and after the server is online, the power consumption model prediction often has large deviations due to differences in server resource utilization, which cannot reflect the actual server power consumption.

[0005] This section is intended to provide a background or context to the embodiments of the invention recited in the claims, and no description herein is admitted to be prior art by inclusion in this section. Summary of the invention

[0006] In response to the problems in the prior art, the present application provides a server power consumption prediction method and device based on business scenarios, which can realize power consumption prediction of servers that are not online by collecting statistics and model calculations of power consumption of servers applying different types of banking business.

[0007] In order to solve the above technical problems, this application provides the following technical solutions:

[0008] In a first aspect, the present application provides a method for predicting server power consumption based on a business scenario, comprising:

[0009] Determine the business domain and server node type to which the server belongs according to the acquired business application name and business node type;

[0010] Determine the service-related power consumption parameters and node-related power consumption parameters of the server according to the service domain to which the server belongs and the server node type;

[0011] The server power consumption is predicted based on the business scenario according to the business-related power consumption parameters and the node-related power consumption parameters.

[0012] Further, the determining of the service-related power consumption parameters and node-related power consumption parameters of the server according to the service domain and server node type to which the server belongs includes:

[0013] Determine the model according to the business domain to which the server belongs and the server node type matching the corresponding power consumption parameters;

[0014] The business domain to which the server belongs and the server node type are input into the power consumption parameter determination model to obtain the business-related power consumption parameters and the node-related power consumption parameters.

[0015] Furthermore, the step of pre-building the power consumption parameter determination model includes:

[0016] Perform periodic statistics on historical service-related power parameters and historical node-related power parameters to obtain corresponding statistical values;

[0017] The power consumption parameter determination model is constructed according to the statistical value.

[0018] Furthermore, the step of pre-building the power consumption parameter determination model includes:

[0019] Obtain historical service-related power parameters and historical node-related power parameters, historical service-related power consumption parameters and historical node-related power consumption parameters;

[0020] The historical service-related power parameters, the historical node-related power parameters, the historical service-related power consumption parameters and the historical node-related power consumption parameters are input into a neural network for training to obtain the power consumption parameter determination model.

[0021] Furthermore, the predicting of server power consumption based on business scenarios according to the business-related power consumption parameters and the node-related power consumption parameters includes:

[0022] Use historical business-related power consumption parameters, historical node-related power consumption parameters and historical server actual power consumption to fit a multivariate linear regression equation;

[0023] The service-related power consumption parameters and the node-related power consumption parameters are input into the multivariate linear regression equation to obtain the predicted power consumption of the server.

[0024] Furthermore, the predicting of server power consumption based on business scenarios according to the business-related power consumption parameters and the node-related power consumption parameters includes:

[0025] Build a power consumption prediction model using historical business-related power consumption parameters, historical node-related power consumption parameters, and historical server actual power consumption;

[0026] The service-related power consumption parameters and the node-related power consumption parameters are input into the power consumption prediction model to obtain the predicted power consumption of the server.

[0027] In a second aspect, the present application provides a server power consumption prediction device based on a business scenario, comprising:

[0028] A service node determination unit, used to determine the service domain and server node type to which the server belongs according to the acquired service application name and service node type;

[0029] A power parameter determination unit, configured to determine a service-related power consumption parameter and a node-related power consumption parameter of the server according to the service domain to which the server belongs and the server node type;

[0030] The power consumption prediction unit is used to predict the server power consumption based on the business scenario according to the business-related power consumption parameters and the node-related power consumption parameters.

[0031] Furthermore, the power parameter determination unit includes:

[0032] A parameter model matching module, used to match the corresponding power consumption parameter determination model according to the business domain and server node type to which the server belongs;

[0033] The power consumption parameter determination module is used to input the service domain and server node type to which the server belongs into the power consumption parameter determination model to obtain the service-related power consumption parameters and node-related power consumption parameters.

[0034] Furthermore, the power parameter determination unit includes:

[0035] The power statistics module is used to periodically collect statistics on historical service-related power parameters and historical node-related power parameters to obtain corresponding statistical values;

[0036] A power parameter model building module is used to build the power consumption parameter determination model according to the statistical value.

[0037] Furthermore, the power parameter determination unit includes:

[0038] A power consumption parameter determination module, used to obtain historical service-related power parameters and historical node-related power parameters, historical service-related power consumption parameters and historical node-related power consumption parameters;

[0039] The neural network training module is used to input the historical service-related power parameters, the historical node-related power parameters, the historical service-related power consumption parameters and the historical node-related power consumption parameters into the neural network for training to obtain the power consumption parameter determination model.

[0040] Furthermore, the power consumption prediction unit includes:

[0041] A linear regression fitting module is used to fit a multivariate linear regression equation using historical business-related power consumption parameters, historical node-related power consumption parameters, and historical server actual power consumption;

[0042] The first power consumption prediction module is used to input the service-related power consumption parameters and the node-related power consumption parameters into the multivariate linear regression equation to obtain the predicted power consumption of the server.

[0043] Furthermore, the power consumption prediction unit includes:

[0044] A power consumption prediction model building module is used to build a power consumption prediction model using historical business-related power consumption parameters, historical node-related power consumption parameters, and historical server actual power consumption;

[0045] The second power consumption prediction module is used to input the service-related power consumption parameters and the node-related power consumption parameters into the power consumption prediction model to obtain the predicted power consumption of the server.

[0046] In a third aspect, 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 method for predicting server power consumption based on business scenarios when executing the program.

[0047] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for predicting server power consumption based on business scenarios.

[0048] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the server power consumption prediction method based on business scenarios.

[0049] In response to the problems in the prior art, the server power consumption prediction method and device based on business scenarios provided by the present application overcome the problem of insufficient accuracy in estimating server power consumption in the existing server shelf planning process of bank data centers. By combining with banking business application scenarios, operation and maintenance personnel can enter business application name, node type, server model and other information on the WEB page, and the system calls built-in parameters and other methods to calculate the server power consumption model for deploying corresponding specific banking business applications through specific algorithms, thereby achieving the purpose of more accurately predicting server power consumption before the data center server is put on the shelf, making effective use of cabinet resources, and avoiding the risk of server downtime in the entire cabinet due to excessive server shelf power consumption, helping bank data center server deployment planning and refined utilization of computer room cabinets, and eliminating the waste of resources caused by unscientific and unreasonable server planning in large commercial bank data centers. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 This is a flow chart of a method for predicting server power consumption based on business scenarios in an embodiment of the present application;

[0052] Figure 2 A flowchart for determining relevant power consumption parameters in an embodiment of the present application;

[0053] Figure 3 One of the flow charts for constructing a power consumption parameter determination model in an embodiment of the present application;

[0054] Figure 4 The second flowchart of constructing a power consumption parameter determination model in an embodiment of the present application;

[0055] Figure 5 This is one of the flow charts for performing power consumption prediction in an embodiment of the present application;

[0056] Figure 6 This is the second flowchart of power consumption prediction in the embodiment of the present application;

[0057] Figure 7 This is a structural diagram of a server power consumption prediction device based on a business scenario in an embodiment of the present application;

[0058] Figure 8 This is one of the structural diagrams of the power parameter determination unit in the embodiment of the present application;

[0059] Fig. 9 This is the second structural diagram of the power parameter determination unit in the embodiment of the present application;

[0060] Fig.10 This is the third structural diagram of the power parameter determination unit in the embodiment of the present application;

[0061] Fig.11 This is one of the structural diagrams of the power consumption prediction unit in the embodiment of the present application;

[0062] Fig.12 This is the second structural diagram of the power consumption prediction unit in the embodiment of the present application;

[0063] Fig.13 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application;

[0064] Fig.14 This is a schematic diagram of a server usage scenario evaluation subsystem in an embodiment of the present application;

[0065] Fig.15 This is a schematic diagram of a parameter configuration subsystem based on a banking business scenario in an embodiment of the present application;

[0066] Fig.16 This is a schematic diagram of the server power consumption model prediction process based on the banking business scenario in an embodiment of the present application. DETAILED DESCRIPTION

[0067] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0068] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0069] Provide users with corresponding operation entrances for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered.

[0070] In one embodiment, see Figure 1 In order to collect statistics and model calculations of the power consumption of different types of banking application servers and realize the power consumption prediction of unlaunched servers, the present application provides a server power consumption prediction method based on business scenarios, including:

[0071] S101: Determine the business domain and server node type to which the server belongs according to the acquired business application name and business node type;

[0072] S102: Determine a service-related power consumption parameter and a node-related power consumption parameter of the server according to the service domain to which the server belongs and the server node type;

[0073] S103: Perform server power consumption prediction based on business scenarios according to the business-related power consumption parameters and the node-related power consumption parameters.

[0074] It is understandable that in the embodiment of the present application, an interactive WEB page can be provided to provide the bank data center operation and maintenance personnel with a server power consumption prediction model based on banking business applications, so that the operation and maintenance personnel can make server power consumption predictions based on business characteristics after obtaining the server usage scenarios and plan the server racks reasonably. The platform (which can be used as the execution subject of the embodiment of the present application) is deployed through the server side, supports concurrent access by multiple users, does not require local installation, and has high flexibility.

[0075] See also Fig.14 , Fig.15 The server power consumption prediction method based on business scenarios (including a server power consumption prediction model based on banking business scenarios) provided in the embodiment of the present application mainly includes: a server usage scenario evaluation subsystem 101, a parameter configuration subsystem based on banking business scenarios 102 and a server power consumption prediction subsystem 103. The platform obtains the server power consumption measurement parameters used by the server in a specific banking business scenario by constructing a server usage scenario evaluation system. The server power consumption prediction model is used to calculate the server power consumption data in a specific (demand) banking business scenario, providing a decision-making basis for operation and maintenance personnel to plan the server rack in the computer room.

[0076] Specifically, the server usage scenario evaluation subsystem 101 includes two parts: a front-end page and a back-end database. The front-end WEB page provides operation and maintenance personnel with interactive input windows including but not limited to specific business application names such as mobile banking and credit systems; specific node types such as application servers and database servers; and server model information of different brands and types. The back-end database stores the banking business application names, node types, and server model data involved, so that the platform can determine the business field and server node type to which the back-end server belongs based on the business application names and business node types obtained from the front-end.

[0077] This application overcomes the problem of insufficient accuracy in estimating server power consumption in the existing planning process of server racks in bank data centers. By combining with banking business application scenarios, operation and maintenance personnel enter business application name, node type, server model and other information on the WEB page, and the system calls built-in parameters and other methods to match the server power consumption model corresponding to the specific banking business application through a specific algorithm, thereby achieving the purpose of more accurately predicting server power consumption before the data center server is racked. Its technical effects and advantages include at least:

[0078] 1. The power consumption of standard cabinet servers can be predicted at the initial stage of deployment, which improves the accuracy of server planning and deployment. When the server is put on the shelf, the operation and maintenance personnel can accurately predict the power consumption of the servers to be put on the shelf with clear business applications in the system. There is no situation where the power consumption exceeds the limit and the overused servers need to be removed from the shelf again. It also eliminates the situation where the remaining power resources need to be re-planned due to insufficient cabinet power usage, so as to achieve the goal of rapid launch of business applications and efficient use of cabinet resources.

[0079] 2. Server power consumption prediction does not depend on server configuration. The server configuration depends only on the actual needs of banking business applications. At the same time, the system's built-in parameters have objectively reflected the characteristics of server usage scenarios based on banking business applications and node types. There is no need to obtain CPU usage, memory usage and other data from the server side for evaluation, which improves the convenience of server power consumption prediction.

[0080] From the above description, it can be seen that the server power consumption prediction method based on business scenarios provided by the present application overcomes the problem of insufficient accuracy of server power consumption estimation in the existing bank data center server planning process. By combining with the banking business application scenario, operation and maintenance personnel can enter the business application name, node type, server model and other information on the WEB page, and the system calls built-in parameters and other methods to calculate the server power consumption model corresponding to the specific banking business application deployed through a specific algorithm, thereby achieving the purpose of more accurately predicting the server power consumption before the data center server is put on the shelf, making effective use of cabinet resources, and avoiding the risk of server downtime in the entire cabinet due to excessive power consumption of the server on the shelf, helping the bank data center server deployment planning and refined use of computer room cabinets, eliminating the waste of resources caused by unscientific and unreasonable planning of servers in the data centers of large commercial banks.

[0081] In one embodiment, see Figure 2 , the determining of the service-related power consumption parameters and node-related power consumption parameters of the server according to the service domain to which the server belongs and the server node type includes:

[0082] S201: Determine a model by matching corresponding power consumption parameters according to the business domain to which the server belongs and the server node type;

[0083] S202: Input the service domain to which the server belongs and the server node type into the power consumption parameter determination model to obtain the service-related power consumption parameters and the node-related power consumption parameters.

[0084] Specifically, see Figure 3 The step of pre-building the power consumption parameter determination model includes: periodically performing statistics on historical service-related power parameters and historical node-related power parameters to obtain corresponding statistical values ​​(S301); and building the power consumption parameter determination model according to the statistical values ​​(S302).

[0085] Specifically, see Figure 4 The step of pre-building the power consumption parameter determination model includes: obtaining historical service-related power parameters and historical node-related power parameters, historical service-related power consumption parameters and historical node-related power consumption parameters (S401); inputting the historical service-related power parameters, the historical node-related power parameters, the historical service-related power consumption parameters and historical node-related power consumption parameters into a neural network for training to obtain the power consumption parameter determination model (S402).

[0086] It is understandable that in the parameter configuration subsystem 102 based on the banking business scenario, the power a of a specific model server is stored. The data acquisition method can be the one-hour average value when the server is turned on and no business is running. Based on the parameters i related to the power consumption of each business application and the physical server, the acquisition method of this type of data includes but is not limited to the statistical value obtained by periodically counting the power consumption of the server used by the specific business application. The statistical method can be to sample the power consumption data once every two hours within 24×n hours, sort the sampling results by size and remove the n maximum values ​​to form the sampled data a i , take a i The maximum value of i / a, or an estimate obtained after training with a large number of samples using methods such as machine learning (neural network training).

[0087] Server node type and power consumption related parameters j. The acquisition method of this type of data includes but is not limited to the statistical values ​​obtained by periodically counting the servers used by a specific node type. The statistical method can be to sample power consumption data at fixed intervals within k consecutive hours, and the average value of the sampling results forms the sampling data a. j , let j = a j / a, or an estimate obtained after training with a large number of samples using methods such as machine learning (neural network training).

[0088] The above process can be understood as periodically performing statistics on historical business-related power parameters and historical node-related power parameters to obtain corresponding statistical values, including: within a continuous time period, data sampling of the historical business-related power parameters and historical node-related power parameters at preset intervals to obtain sampling results; taking the average value of the sampling results to obtain the sampled data as the statistical value.

[0089] Among them, the so-called "determine the model according to the business field to which the server belongs and the server node type matching the corresponding power consumption parameters" can be understood as different business fields may have different node types, and their corresponding model parameters are different. Therefore, servers in different business fields and different node types have different models when determining power consumption parameters.

[0090] From the above description, it can be seen that the server power consumption prediction method based on business scenarios provided in the present application can determine the business-related power consumption parameters and node-related power consumption parameters of the server according to the business field to which the server belongs and the server node type.

[0091] In one embodiment, see Figure 5 , performing server power consumption prediction based on business scenarios according to the business-related power consumption parameters and the node-related power consumption parameters, including:

[0092] S501: Fitting a multivariate linear regression equation using historical service-related power consumption parameters, historical node-related power consumption parameters, and historical server actual power consumption;

[0093] S502: Input the service-related power consumption parameters and the node-related power consumption parameters into the multivariate linear regression equation to obtain the predicted power consumption of the server.

[0094] In one embodiment, see Figure 6 , performing server power consumption prediction based on business scenarios according to the business-related power consumption parameters and the node-related power consumption parameters, including:

[0095] S601: Building a power consumption prediction model using historical service-related power consumption parameters, historical node-related power consumption parameters, and historical server actual power consumption;

[0096] S602: Input the service-related power consumption parameters and the node-related power consumption parameters into the power consumption prediction model to obtain the predicted power consumption of the server.

[0097] It can be understood that in the server power consumption prediction subsystem 103, the power consumption of the application server for the specific business scenario to be launched is predicted, and the calculation method can be server power consumption = M×i×a+N×j×a. Among them, a is the power of the actual server when there is no business load, and the power value can be the average value of the server being turned on and running for one hour without business. M and N are business and node type related factors. The method of determining the related factors includes but is not limited to the use of multivariate linear regression fitting and machine learning (neural network training) and other methods. Historical data will be used, and the specific algorithm can be referred to the prior art. See. Fig.16 Taking the expansion of database server for mobile banking business application as an example, the operation and maintenance personnel call the server usage scenario evaluation subsystem 101, select the business scenario - mobile banking, node type - database server and server model information on the WEB page, and the backend returns the corresponding field data in the database, and inquires the business application and physical server power consumption related parameters i, server node type and power consumption related parameters j, and influencing factor parameters M and N in the parameter configuration subsystem 102 based on the banking business scenario, and then passes the parameters to the server power consumption prediction subsystem 103. The server power consumption prediction subsystem 103 queries the server model power parameter b from the parameter configuration subsystem 102 (only when the server model and configuration are the same, the power parameter a = b), and the system predicts that the power consumption of the server to be put on the shelf after it goes online = M×i×b+N×j×b. After obtaining the data, the server prediction power consumption data is returned to the front-end page of the server usage scenario evaluation subsystem 101 for output display. The operation and maintenance personnel can plan the computer room cabinet and actually deploy the server based on the data.

[0098] From the above description, it can be seen that the service scenario-based server power consumption prediction method provided in the present application can perform service scenario-based server power consumption prediction based on the service-related power consumption parameters and node-related power consumption parameters.

[0099] It should be noted that the virtual reality-based bank training system and training method thereof provided in the embodiment of the present invention can be used in the financial field, and can also be used in any technical field other than the financial field. The embodiment of the present invention does not limit the application field of the virtual reality-based bank training system and training method thereof.

[0100] Based on the same inventive concept, the embodiment of the present application also provides a server power consumption prediction device based on a business scenario, which can be used to implement the method described in the above embodiment, as described in the following embodiment. Since the principle of solving the problem by the server power consumption prediction device based on the business scenario is similar to that of the server power consumption prediction method based on the business scenario, the implementation of the server power consumption prediction device based on the business scenario can refer to the implementation of the method based on software performance benchmark determination, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0101] In one embodiment, see Figure 7 In order to collect statistics and model calculations on the power consumption of different types of banking application servers and realize power consumption prediction of servers that are not online, the present application provides a server power consumption prediction device based on business scenarios, including: a business node determination unit 701, a power parameter determination unit 702 and a power consumption prediction unit 703.

[0102] A service node determination unit 701 is used to determine the service domain and server node type to which the server belongs according to the acquired service application name and service node type;

[0103] A power parameter determination unit 702, configured to determine a service-related power consumption parameter and a node-related power consumption parameter of the server according to the service domain to which the server belongs and the server node type;

[0104] The power consumption prediction unit 703 is used to predict the server power consumption based on the service scenario according to the service-related power consumption parameters and the node-related power consumption parameters.

[0105] In one embodiment, see Figure 8 The power parameter determination unit 702 includes: a parameter model matching module 801 and a power consumption parameter determination module 802.

[0106] A parameter model matching module 801 is used to match the corresponding power consumption parameter determination model according to the business domain and server node type to which the server belongs;

[0107] The power consumption parameter determination module 802 is used to input the service domain and server node type to which the server belongs into the power consumption parameter determination model to obtain the service-related power consumption parameters and node-related power consumption parameters.

[0108] In one embodiment, see Fig. 9 The power parameter determination unit 702 includes: a power statistics module 901 and a power parameter model construction module 902.

[0109] The power statistics module 901 is used to periodically collect statistics on historical service-related power parameters and historical node-related power parameters to obtain corresponding statistical values;

[0110] The power parameter model building module 902 is used to build the power consumption parameter determination model according to the statistical value.

[0111] In one embodiment, see Fig.10 , the power parameter determination unit 702 includes:

[0112] The power consumption parameter determination module 1001 is used to obtain historical service-related power parameters and historical node-related power parameters, historical service-related power consumption parameters and historical node-related power consumption parameters;

[0113] The neural network training module 1002 is used to input the historical service-related power parameters, the historical node-related power parameters, the historical service-related power consumption parameters and the historical node-related power consumption parameters into a neural network for training to obtain the power consumption parameter determination model.

[0114] In one embodiment, see Fig.11 The power consumption prediction unit 703 includes: a linear regression fitting module 1101 and a first power consumption prediction module 1102.

[0115] The linear regression fitting module 1101 is used to fit a multivariate linear regression equation using historical service-related power consumption parameters, historical node-related power consumption parameters, and historical server actual power consumption;

[0116] The first power consumption prediction module 1102 is used to input the service-related power consumption parameters and the node-related power consumption parameters into the multivariate linear regression equation to obtain the predicted power consumption of the server.

[0117] In one embodiment, see Fig.12 The power consumption prediction unit 703 includes: a power consumption prediction model construction module 1201 and a second power consumption prediction module 1202.

[0118] The power consumption prediction model building module 1201 is used to build a power consumption prediction model using historical service-related power consumption parameters, historical node-related power consumption parameters and historical server actual power consumption;

[0119] The second power consumption prediction module 1202 is used to input the service-related power consumption parameters and the node-related power consumption parameters into the power consumption prediction model to obtain the predicted power consumption of the server.

[0120] From the hardware level, in order to collect statistics and model calculations of the power consumption of different types of banking application servers to achieve the power consumption prediction of unlaunched servers, the present application provides an embodiment of an electronic device for implementing all or part of the content of the server power consumption prediction method based on business scenarios, and the electronic device specifically includes the following content:

[0121] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the server power consumption prediction device based on business scenarios and related devices such as core business systems, user terminals and related databases; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but this embodiment is not limited to this. In this embodiment, the logic controller can be implemented with reference to the embodiment of the server power consumption prediction method based on business scenarios and the embodiment of the server power consumption prediction device based on business scenarios in the embodiment, and the contents are merged here, and the repeated parts are not repeated.

[0122] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0123] In practical applications, part of the server power consumption prediction method based on business scenarios can be executed on the electronic device side as described above, or all operations can be completed in the client device. The selection can be made based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0124] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0125] Fig.13 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig.13 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig.13 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0126] In one embodiment, the server power consumption prediction method function based on business scenarios may be integrated into the central processor 9100. The central processor 9100 may be configured to perform the following control:

[0127] S101: Determine the business domain and server node type to which the server belongs according to the acquired business application name and business node type;

[0128] S102: Determine a service-related power consumption parameter and a node-related power consumption parameter of the server according to the service domain to which the server belongs and the server node type;

[0129] S103: Perform server power consumption prediction based on business scenarios according to the business-related power consumption parameters and the node-related power consumption parameters.

[0130] From the above description, it can be seen that the server power consumption prediction method based on business scenarios provided by the present application overcomes the problem of insufficient accuracy of server power consumption estimation in the existing bank data center server planning process. By combining with the banking business application scenario, operation and maintenance personnel can enter the business application name, node type, server model and other information on the WEB page, and the system calls built-in parameters and other methods to calculate the server power consumption model corresponding to the specific banking business application deployed through a specific algorithm, thereby achieving the purpose of more accurately predicting the server power consumption before the data center server is put on the shelf, making effective use of cabinet resources, and avoiding the risk of server downtime in the entire cabinet due to excessive power consumption of the server on the shelf, helping the bank data center server deployment planning and refined use of computer room cabinets, eliminating the waste of resources caused by unscientific and unreasonable planning of servers in the data centers of large commercial banks.

[0131] In another embodiment, the server power consumption prediction device based on business scenarios can be configured separately from the central processing unit 9100. For example, the data composite transmission device based on business scenarios server power consumption prediction device can be configured as a chip connected to the central processing unit 9100, and the function of the server power consumption prediction method based on business scenarios can be realized through the control of the central processing unit.

[0132] like Fig.13As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Fig.13 In addition, the electronic device 9600 may also include Fig.13 For components not shown, reference may be made to the prior art.

[0133] like Fig.13 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0134] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0135] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0136] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0137] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0138] The communication module 9110 is a transmitter / receiver 9110 that sends and receives signals via an antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0139] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0140] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps in the server power consumption prediction method based on a business scenario in the above-mentioned embodiment, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all steps of the server power consumption prediction method based on a business scenario in the above-mentioned embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0141] S101: Determine the business domain and server node type to which the server belongs according to the acquired business application name and business node type;

[0142] S102: Determine a service-related power consumption parameter and a node-related power consumption parameter of the server according to the service domain to which the server belongs and the server node type;

[0143] S103: Perform server power consumption prediction based on business scenarios according to the business-related power consumption parameters and the node-related power consumption parameters.

[0144] From the above description, it can be seen that the server power consumption prediction method based on business scenarios provided by the present application overcomes the problem of insufficient accuracy of server power consumption estimation in the existing bank data center server planning process. By combining with the banking business application scenario, operation and maintenance personnel can enter the business application name, node type, server model and other information on the WEB page, and the system calls built-in parameters and other methods to calculate the server power consumption model corresponding to the specific banking business application deployed through a specific algorithm, thereby achieving the purpose of more accurately predicting the server power consumption before the data center server is put on the shelf, making effective use of cabinet resources, and avoiding the risk of server downtime in the entire cabinet due to excessive power consumption of the server on the shelf, helping the bank data center server deployment planning and refined use of computer room cabinets, eliminating the waste of resources caused by unscientific and unreasonable planning of servers in the data centers of large commercial banks.

[0145] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0146] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0149] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A server power consumption prediction method based on business scenarios, characterized in that: include: Determine the business domain and server node type to which the server belongs according to the acquired business application name and business node type; Determine the service-related power consumption parameters and node-related power consumption parameters of the server according to the service domain to which the server belongs and the server node type; The server power consumption is predicted based on the business scenario according to the business-related power consumption parameters and the node-related power consumption parameters.

2. The server power consumption prediction method based on business scenarios according to claim 1 is characterized in that: The determining of the service-related power consumption parameters and node-related power consumption parameters of the server according to the service domain to which the server belongs and the server node type includes: Determine the model according to the business domain to which the server belongs and the server node type matching the corresponding power consumption parameters; The business domain to which the server belongs and the server node type are input into the power consumption parameter determination model to obtain the business-related power consumption parameters and the node-related power consumption parameters.

3. The server power consumption prediction method based on business scenarios according to claim 2 is characterized in that: The step of pre-building the power consumption parameter determination model includes: Perform periodic statistics on historical service-related power parameters and historical node-related power parameters to obtain corresponding statistical values; The power consumption parameter determination model is constructed according to the statistical value.

4. The method for predicting server power consumption based on business scenarios according to claim 2, characterized in that: The step of pre-building the power consumption parameter determination model includes: Obtain historical service-related power parameters and historical node-related power parameters, historical service-related power consumption parameters and historical node-related power consumption parameters; The historical service-related power parameters, the historical node-related power parameters, the historical service-related power consumption parameters and the historical node-related power consumption parameters are input into a neural network for training to obtain the power consumption parameter determination model.

5. The server power consumption prediction method based on business scenarios according to claim 1 is characterized in that: The performing of server power consumption prediction based on the business scenario according to the business-related power consumption parameters and the node-related power consumption parameters includes: Use historical business-related power consumption parameters, historical node-related power consumption parameters and historical server actual power consumption to fit a multivariate linear regression equation; The service-related power consumption parameters and the node-related power consumption parameters are input into the multivariate linear regression equation to obtain the predicted power consumption of the server.

6. The method for predicting server power consumption based on business scenarios according to claim 1, characterized in that: The performing of server power consumption prediction based on the business scenario according to the business-related power consumption parameters and the node-related power consumption parameters includes: Build a power consumption prediction model using historical business-related power consumption parameters, historical node-related power consumption parameters, and historical server actual power consumption; The service-related power consumption parameters and the node-related power consumption parameters are input into the power consumption prediction model to obtain the predicted power consumption of the server.

7. The method for predicting server power consumption based on business scenarios according to claim 3 is characterized in that: The periodic statistics of historical service-related power parameters and historical node-related power parameters are performed to obtain corresponding statistical values, including: In a continuous time period, data sampling is performed on the historical service-related power parameters and the historical node-related power parameters at preset intervals to obtain sampling results; An average value is taken for the adopted results to obtain sampling data as the statistical value.

8. A server power consumption prediction device based on business scenarios, characterized in that: include: A service node determination unit, used to determine the service domain and server node type to which the server belongs according to the acquired service application name and service node type; A power parameter determination unit, configured to determine a service-related power consumption parameter and a node-related power consumption parameter of the server according to the service domain to which the server belongs and the server node type; The power consumption prediction unit is used to predict the server power consumption based on the business scenario according to the business-related power consumption parameters and the node-related power consumption parameters.

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 program, the steps of the server power consumption prediction method based on business scenarios described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting server power consumption based on business scenarios described in any one of claims 1 to 7 are implemented.

Images