CN115454793A - Method, device and storage medium for determining hardware requirement of machine - Google Patents
Method, device and storage medium for determining hardware requirement of machine Download PDFInfo
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Abstract
The application provides a method, a computer device and a storage medium for determining hardware requirements of a machine, wherein the method comprises the following steps: acquiring hardware performance characteristic data of a target machine; inputting hardware performance characteristic data into an evaluation model corresponding to the target micro-service component to obtain performance information of the target micro-service component; the evaluation model is an analysis model obtained by training according to the relation between hardware performance characteristic data of the target machine and performance information of the target micro-service component; and determining the hardware requirement of the target machine according to the performance information of the target micro-service component, the relation between the target micro-service component and the target software and the performance requirement of the target software. According to the method, the performance information of the target micro-service component can be obtained by inputting the abundant dimensional information of the hardware performance characteristic data of the machine into the evaluation model corresponding to the target micro-service component, and the hardware requirement of the target machine is further determined.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, a computer device, and a storage medium for determining hardware requirements of a machine.
Background
With the development of internet technology, more and more enterprises (i.e. service providers) not only provide services for general customers, but also deliver their services to customers in the form of personalized delivery to social institutions or other enterprises. Privatized delivery, as the name implies, refers to the deployment of services in the private environment of a customer. However, in a specific delivery scenario, because the difference between the on-site server environment and the environment during product development is large, different virtual environments and different hardware configurations lead to difficulty in accurately determining the hardware requirement of a machine during delivery of a privatized product, and further lead to poor delivery effect.
The existing method for determining the hardware requirement of a machine is mainly to test the performance data of each micro-service component under the condition that a certain type of hardware server is used as a standard configuration in the product research and development stage, and output a machine evaluation method of the product under different scenes by combining a product deployment architecture. And further, under a privatized delivery scene, according to the configuration parameters of the server, the performance of the micro service component is roughly estimated by a simple linear evaluation method, so that the hardware requirement of the machine is obtained, and the method is difficult to accurately determine the hardware requirement of the machine.
Disclosure of Invention
The application provides a method, computer equipment and storage medium for determining hardware requirements of a machine, which can accurately determine the hardware requirements of the machine.
In order to solve the above technical problem, a first technical solution provided by the present application is: provided is a method for evaluating the performance of a micro-service component, the method comprising:
a method of determining hardware requirements of a machine, the method comprising:
acquiring hardware performance characteristic data of a target machine;
inputting the hardware performance characteristic data into an evaluation model corresponding to a target micro-service component to obtain performance information of the target micro-service component; the evaluation model is an analysis model obtained by training according to the relation between the hardware performance characteristic data of the target machine and the performance information of the target micro-service component;
and determining the hardware requirement of the target machine according to the performance information of the target micro-service component, the relation between the target micro-service component and the target software and the performance requirement of the target software after deployment.
In order to solve the above technical problem, a second technical solution provided by the present application is: an apparatus for determining hardware requirements of a machine is provided, comprising:
the acquisition module is used for acquiring hardware performance characteristic data of a target machine;
the input module is used for inputting the hardware performance characteristic data into an evaluation model corresponding to a target micro-service component to obtain performance information of the target micro-service component; the evaluation model is an analysis model obtained by training according to the relation between the hardware performance characteristic data of the target machine and the performance information of the target micro-service component;
and the determining module is used for determining the hardware requirement of the target machine according to the performance information of the target micro-service component, the relation between the target micro-service component and the target software and the performance requirement of the target software after deployment.
In order to solve the above technical problems, a third technical solution provided by the present application is: there is provided a computer device, the computer device comprising:
a memory and a processor;
wherein the memory is connected with the processor and used for storing programs;
the processor is configured to implement the steps of the method of determining hardware requirements of a machine as described in any one of the above by executing a program stored in the memory.
In order to solve the above technical problems, a fourth technical solution provided by the present application is: there is provided a storage medium storing a computer program which, when executed by a processor, causes the processor to carry out the steps of the method of determining hardware requirements of a machine as claimed in any one of the preceding claims.
According to the method, the computer device and the storage medium for determining the hardware requirement of the machine, the performance information of the target micro-service component can be obtained by inputting the rich dimension information of the hardware performance characteristic data of the machine into the evaluation model corresponding to the target micro-service component based on the obtained rich dimension information, and the hardware requirement of the target machine can be further determined.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic illustration of steps of a method of determining hardware requirements of a machine provided by an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating steps of a method for constructing an evaluation model according to an embodiment of the present application;
FIG. 3 is a schematic block diagram of an apparatus for determining hardware requirements of a machine according to an embodiment of the present application;
FIG. 4 is a schematic block diagram of a computer device provided by an embodiment of the present application;
fig. 5 is a schematic structural diagram of a storage medium provided in the present application.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The flowcharts shown in the figures are illustrative only and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution order may be changed according to the actual situation.
It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that, for the convenience of clearly describing the technical solutions of the embodiments of the present application, the words "first", "second", and the like are used in the embodiments of the present application to distinguish the same items or similar items with basically the same functions and actions. For example, the first recognition model and the second recognition model are only used for distinguishing different callback functions, and the sequence of the callback functions is not limited. Those skilled in the art will appreciate that the terms "first," "second," etc. do not denote any order or quantity, nor do the terms "first," "second," etc. denote any order or importance.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic diagram illustrating steps of a method for evaluating performance of a microservice component according to an embodiment of the present application. As shown in fig. 1, the method for evaluating the performance of the micro service component includes step S11 and step S12, wherein the method for evaluating the performance of the micro service component can be applied to a computer device for evaluating the performance of the micro service component.
S11: hardware performance characteristic data of the target machine is obtained.
When the evaluation of the performance of the micro service component is carried out, the inventor finds that the hardware performance characteristic data of the machine is most strongly correlated with the evaluation of the micro service component, and the rich dimensional information can realize accurate evaluation of the performance of the micro service component. Therefore, the hardware performance characteristic data of the target machine can be acquired and input into the evaluation model corresponding to the target micro-service component, so that the performance of the micro-service component can be accurately evaluated.
It should be noted that the micro service is to refine the service function of the network traffic collection and analysis system, and the service of the network traffic collection and analysis system may be divided into five service types of collection, storage/transmission, analysis, visualization and self-management according to the functional boundary, where each service type includes multiple micro service functions.
And setting a corresponding micro service component for each micro service, wherein the micro service and the micro service components are in one-to-one correspondence. Each micro-service component is provided with an independently running process, and the application function of the corresponding micro-service is completed through the process. Each micro service component can be independently deployed, a plurality of micro service components can be flexibly combined, and due to the independent deployment and flexible combination characteristics of the micro service components, the flow acquisition and analysis system based on the micro service components can realize real-time deployment and flexibly configure various application service functions, so that the upgrading and deployment efficiency is improved.
The distributed system design is adopted among all the micro service components, all the micro service components in the system are completely decoupled with other micro service components, and the access among the micro service components is realized through a series of remote access protocols. Each microservice component provides a plurality of API (Application Programming Interface) interfaces to the outside, which are implemented by a web-based, independently deployable API layer. Each micro service component comprises one or more service units, and the service units respectively and independently realize different service logics in the corresponding micro services.
In practical application, because the micro-service components can be independently deployed, upgraded and maintained, a plurality of micro-service components can be flexibly combined, and thus, the application function of a plurality of corresponding micro-service combinations is realized. In other project development, if the function of the system is repeated with the flow collection and analysis system, the related micro service components can be directly reused.
Optionally, the hardware performance characteristic data of the target machine may be acquired by a hardware characteristic acquisition tool. The hardware performance characteristic data at least comprises one or more combinations of disk write data, disk read data, network write data, network read data, integer calculation data, floating point calculation data, prime number data, sequencing data, encryption data, SSE data and matrix operation data.
It should be noted that, the hardware performance characteristic collecting tool is similar to a score running tool, that is, a user may test the hardware performance characteristic of the target machine through the hardware performance characteristic collecting tool, so as to obtain the hardware performance characteristic data of the target machine.
Further, the hardware performance characteristic data of the target machine is not limited, and other more hardware performance characteristic data can be input or trained in the process of inputting the evaluation model and establishing the subsequent evaluation model. Thus, the accuracy of the performance evaluation of the micro service component can be further improved.
S12: inputting hardware performance characteristic data into an evaluation model corresponding to the target micro-service component to obtain performance information of the target micro-service component; the evaluation model is an analysis model obtained by training according to the relation between the hardware performance characteristic data of the target machine and the performance information of the target micro-service component.
And inputting the hardware performance characteristic data of the target machine into an evaluation model corresponding to the target micro-service component to obtain the performance information of the target micro-service component, namely, realizing the evaluation of the performance of the target micro-service component. The evaluation model is an analysis model obtained by training according to the relation between the hardware performance characteristic data of the target machine and the performance information of the target micro-service component.
S13: and determining the hardware requirement of the target machine according to the performance information of the target micro-service component, the relation between the target micro-service component and the target software and the performance requirement of the deployed target software.
Specifically, after the performance information of the target micro-service component is obtained, the relationship between the target micro-service component and the corresponding software and the performance requirement of the deployed target software can be further obtained. And then determining the hardware requirement of the target machine according to the performance information of the target micro-service component, the relation between the target micro-service component and the corresponding software and the performance requirement of the deployed target software.
It should be noted that the target software is composed of several micro service components, that is, the performance information of several micro service components in the target software constitutes the performance information of the target software. Therefore, the performance information of each micro-service component in the target software can be obtained in sequence, and further the performance information of the target software can be obtained.
Specifically, the hardware performance characteristic data of the target machine may be obtained first, and then the hardware performance characteristic data is sequentially input into the evaluation model corresponding to each micro-service component of the target software, so as to obtain the performance information of the plurality of micro-service components. Therefore, the performance information of the target software can be determined according to the performance information of the micro service components.
It can be understood that, in an actual delivery scenario, a customer may have a customized performance requirement for software to be deployed, where the performance requirement may be a requirement of response time or a requirement of the number of concurrent users, and the application is not limited thereto. Furthermore, the hardware requirement of the target machine can be determined according to the performance information of the target micro-service component, the relationship between the target micro-service component and the corresponding software and the performance requirement of the deployed target software.
In the embodiment of the application, the hardware performance characteristic data of the target machine can be acquired and input into the evaluation model corresponding to the target micro-service component, so that the performance of the target micro-service component is obtained. By acquiring the abundant dimensional information of the hardware performance characteristic data, the performance of the target micro-service component can be accurately evaluated, and the hardware requirement of the target machine can be further determined.
Optionally, referring to fig. 2, fig. 2 is a schematic step diagram of a method for constructing an evaluation model according to an embodiment of the present application. As shown in fig. 2, before acquiring the hardware performance characteristic data of the target machine, the method further includes:
step 201: acquiring hardware performance characteristic data of a target machine in different machine scenes, and acquiring performance characteristic constraint parameters corresponding to the target micro-service component.
The evaluation of the performance of the micro-service component is mainly influenced by the machine scene and the performance characteristic constraint of the micro-service component. Different machine scenes comprise different virtualization modes and/or different hardware configuration parameters, and the evaluation of the micro service components is greatly influenced by the factors, so that the evaluation results of the micro service components in different machine scenes are different greatly. Therefore, when an evaluation model corresponding to the target micro-service component is established, hardware performance characteristic data of the target machine under different machine scenes can be obtained.
It should be noted that the different machine scenarios include different virtualization manners and/or different hardware configuration parameter configurations. The virtualization refers to virtualizing one computer into a plurality of logical computers by a virtualization technology. Depending on the purpose of virtualization, the virtualization method may include the following four categories: platform virtualization, resource virtualization, storage virtualization, and presentation layer virtualization. The platform virtualization is virtualization aiming at a computer and an operating system and is divided into server virtualization and desktop virtualization; resource virtualization is virtualization performed for a specific computing resource, for example, storage virtualization, network resource virtualization; application virtualization includes emulation, simulation, interpretation techniques, etc., for example, a Java virtual machine is typically virtualized at the application layer; presentation layer virtualization is similar in application to application virtualization, except that the application in presentation layer virtualization runs on a server and the client displays only the UI interface and user operations of the application.
The hardware configuration is used to represent the basic hardware configuration of the load balancing product, for example, the indexes of parameters such as CPU, GPU, memory, hard disk, network combination, etc., that is, the parameters of the hardware configuration can be used to evaluate the performance of the machine.
Furthermore, performance characteristic constraint parameters corresponding to the target micro service component can be obtained. Wherein, the performance characteristic constraint parameter corresponding to the target micro service component can be used for expressing the limit of the business function of the target micro service component.
Optionally, in order to obtain the performance characteristic constraint parameter corresponding to the target micro service component, the service function of the target micro service component may be obtained first, and then the corresponding performance characteristic constraint parameter is obtained based on the service function of the target micro service component.
It should be noted that the micro service components are independent individuals, each micro service component has a service function that is implemented correspondingly, and the service functions corresponding to different micro service components are different. The service functions corresponding to the micro service components respectively comprise a corresponding performance characteristic constraint, such as the specified response time or the availability index.
Step 202: and testing the performance information of the target micro-service component in each machine scene under the constraint of the performance characteristic constraint parameters.
Step 203: and establishing a relation between hardware performance characteristic data and corresponding performance information under each machine scene to obtain training sample data.
Step 204: and constructing an evaluation model corresponding to the target micro-service component according to the training sample data.
Under the constraint of the target micro-service component performance, the performance information of the target micro-service component in each machine scene can be tested. Therefore, the relation between the hardware performance characteristic data and the corresponding performance information under each machine scene can be established, and training sample data can be obtained. Furthermore, an evaluation model corresponding to the target micro-service component can be constructed according to the training sample data.
Optionally, the specific way of testing the performance information of the target micro-service component in each machine scenario is not limited in the present application, for example, the performance information of the hardware performance characteristic data of the target micro-service component in each machine scenario may be tested by using a single-process single-service testing method or a multi-process single-service testing method under the constraint of the performance characteristic constraint parameter.
It should be noted that the essence of the performance testing of microservice components is to completely isolate a microservice from all other services or resources on which it depends, and from the perspective of "users" outside the microservice to see if the microservice can provide the desired output. The single-process single-service test is to load all services and dependence simulators into the same process for testing without using a network; the multi-process single-service test is that the simulated external dependence is placed outside the process of the micro-service, and the test is carried out through real network connection and calling.
Optionally, the category of the evaluation model corresponding to the target microservice component may be a regression analysis model. The regression analysis model is a statistical analysis model for determining the interdependent quantitative relationship between two or more variables, and can clearly express the relationship between hardware performance characteristic data and corresponding performance information in each machine scene. The common regression models include linear regression models, logistic regression models, polynomial regression models, logistic regression models, and the like.
In the present application, the type of the evaluation model corresponding to the target micro service component is not limited, and other suitable types such as a gray correlation model may be used.
In the embodiment of the application, the corresponding performance information of each machine scene can be tested based on the hardware performance characteristic data of the target machine in different machine scenes and the performance characteristic constraint parameters of the micro service assembly, so that the relation between the hardware performance characteristic data of the target machine in different machine scenes and the performance information is established, and an evaluation model corresponding to the performance of the micro service assembly is established based on the relation. The evaluation model refers to the abundant dimensional information of the hardware performance characteristic parameters of the target machine, so that the evaluation result of the performance of the micro-service component can be accurately output through the evaluation model.
According to the evaluation method for the performance of the micro-service component, the abundant dimensional information of the hardware performance characteristic data of the machine can be acquired, and the information is input into the evaluation model corresponding to the target micro-service component, so that the performance of the micro-service component can be accurately output. In addition, the performance of each micro-service component in the target software can be evaluated in sequence to obtain the evaluation result of the performance of each micro-service component, so that the evaluation result of the target software is obtained, and the hardware requirement of the target machine is further determined. Because the method references the abundant dimensional information of the hardware performance characteristic data of the target machine, the obtained evaluation result of the target micro-service component and the hardware requirement result of the target machine are more accurate.
Referring to fig. 3, fig. 3 is a schematic block diagram of an apparatus for determining hardware requirements of a machine according to an embodiment of the present disclosure. As shown in fig. 3, the apparatus 300 for determining hardware requirements of a machine may be configured in a server for executing the aforementioned method for determining hardware requirements of a machine. The device 300 for determining the hardware requirement of the machine comprises an acquisition module 301, an input module 302 and a determination module 303.
The obtaining module 301 is configured to obtain hardware performance characteristic data of a target machine;
the input module 302 is configured to input the hardware performance characteristic data into an evaluation model corresponding to a target micro-service component, so as to obtain performance information of the target micro-service component; the evaluation model is an analysis model obtained by training according to the relation between the hardware performance characteristic data of the target machine and the performance information of the target micro-service component;
the determining module 303 is configured to determine a hardware requirement of the target machine according to the performance information of the target micro service component, a relationship between the target micro service component and target software, and a performance requirement of the target software after deployment.
With continuing reference to fig. 4, fig. 4 is a schematic block diagram of a computer device according to an embodiment of the present disclosure. As shown in fig. 4, the computer device 400 includes one or more processors 401 and a memory 402, and the processors 401 and the memory 402 are connected by a bus, such as an I2C (Inter-integrated Circuit) bus.
Wherein the one or more processors 401, individually or collectively, are operable to perform the steps of the method for evaluating the performance of microservice components provided by the above-described embodiments.
Specifically, the Processor 401 may be a Micro-controller Unit (MCU), a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or the like.
Specifically, the Memory 402 may be a Flash chip, a Read-Only Memory (ROM) magnetic disk, an optical disk, a usb disk, or a removable hard disk.
The processor 401 is configured to run a computer program stored in the memory 402, and when executing the computer program, implement the steps of the evaluation method for the performance of the micro service component provided in the foregoing embodiments.
Illustratively, the processor 401 is adapted to run a computer program stored in the memory 402 and, when executing said computer program, to carry out the steps of:
acquiring hardware performance characteristic data of a target machine;
inputting the hardware performance characteristic data into an evaluation model corresponding to a target micro-service component to obtain performance information of the target micro-service component; the evaluation model is an analysis model obtained by training according to the relation between the hardware performance characteristic data of the target machine and the performance information of the target micro-service component;
and determining the hardware requirement of the target machine according to the performance information of the target micro-service component, the relation between the target micro-service component and corresponding software and the performance requirement of the target software after deployment.
In some embodiments, the processor, prior to implementing the obtaining hardware performance characteristic data of the target machine, is further configured to implement:
acquiring hardware performance characteristic data of the target machine in different machine scenes, and acquiring performance characteristic constraint parameters corresponding to the target micro-service component;
testing the performance information of the target micro-service component in each machine scene under the constraint of the performance characteristic constraint parameter;
establishing a relation between hardware performance characteristic data and corresponding performance information under each machine scene to obtain training sample data;
and constructing an evaluation model of the target micro-service component according to the training sample data.
In some embodiments, the processor, under the constraint of implementing the performance characteristic constraint parameter, tests performance information of the hardware performance characteristic data of the target micro service component in each machine scenario of the target machine, and is specifically configured to implement:
and under the constraint of the performance characteristic constraint parameters, testing the performance information of the hardware performance characteristic data of the target micro-service component under each machine scene by using a single-process single-service test method or a multi-process single-service test method.
In some embodiments, the processor is configured to implement the obtaining of the training sample data, where the training data includes a performance feature constraint corresponding to the target micro service component, and is specifically configured to implement:
acquiring a service function of the target micro-service component;
and acquiring corresponding performance characteristic constraint parameters based on the service functions.
In some embodiments, the processor is specifically configured to implement the obtaining of the hardware performance characteristic data of the target machine by:
acquiring hardware performance characteristic data of a target machine through a hardware characteristic acquisition tool, wherein the hardware performance characteristic data at least comprises disk write data, disk read data, network write data, network read data, integer calculation data, floating point calculation data, prime number data, sequencing data, encryption data, SSE data and matrix operation data.
In some embodiments, the category of the assessment model is a regression analysis model.
In some embodiments, different of the machine scenarios include different virtualization approaches and/or different hardware configuration parameters.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a storage medium provided in the present application. The storage medium 5 of the present application stores a computer program 51 capable of implementing all the above-mentioned evaluation methods based on microservice component performance, wherein the computer program 51 may be stored in the storage medium 5 in the form of a software product, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the methods according to the embodiments of the present application. And the aforementioned storage medium 5 includes: various media capable of storing program codes, such as a usb disk, a portable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, or a terminal device, such as a computer, a server, a mobile phone, or a tablet.
While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of determining hardware requirements of a machine, the method comprising:
acquiring hardware performance characteristic data of a target machine;
inputting the hardware performance characteristic data into an evaluation model corresponding to a target micro-service component to obtain performance information of the target micro-service component; the evaluation model is an analysis model obtained by training according to the relation between the hardware performance characteristic data of the target machine and the performance information of the target micro-service component;
and determining the hardware requirement of the target machine according to the performance information of the target micro-service component, the relation between the target micro-service component and the target software and the performance requirement of the target software after deployment.
2. The method of claim 1, wherein prior to obtaining the hardware performance characterization data of the target machine, the method comprises:
acquiring hardware performance characteristic data of the target machine in different machine scenes, and acquiring performance characteristic constraint parameters corresponding to the target micro-service component;
testing the performance information of the target micro-service component in each machine scene under the constraint of the performance characteristic constraint parameters;
establishing a relation between the hardware performance characteristic data and the corresponding performance information under each machine scene to obtain training sample data;
and constructing an evaluation model of the target micro-service component according to the training sample data.
3. The method of claim 2, wherein said testing performance information of hardware performance characteristic data of the target micro-service component in each machine scenario of the target machine under the constraint of the performance characteristic constraint parameter comprises:
and under the constraint of the performance characteristic constraint parameters, testing the performance information of the hardware performance characteristic data of the target micro-service component under each machine scene by using a single-process single-service test method or a multi-process single-service test method.
4. The method of claim 2, wherein the evaluation model is classified as a regression analysis model.
5. The method of claim 2, wherein the obtaining training sample data, wherein the training data includes performance feature constraints corresponding to the target micro-service component, comprises:
acquiring the service function of the target micro-service component;
and acquiring corresponding performance characteristic constraint parameters based on the service functions.
6. The method of claim 2, wherein different machine scenarios comprise different virtualization modes and/or different hardware configuration parameters.
7. The method of any of claims 1-6, wherein the obtaining hardware performance characterization data for the target machine comprises:
acquiring hardware performance characteristic data of a target machine through a hardware characteristic acquisition tool, wherein the hardware performance characteristic data at least comprises one or more combinations of disk write data, disk read data, network write data, network read data, integer calculation data, floating point calculation data, prime number data, sequencing data, encryption data, SSE data and matrix operation data.
8. An apparatus for determining hardware requirements of a machine, comprising:
the acquisition module is used for acquiring hardware performance characteristic data of a target machine;
the input module is used for inputting the hardware performance characteristic data into an evaluation model corresponding to a target micro-service component to obtain performance information of the target micro-service component; the evaluation model is an analysis model obtained by training according to the relation between the hardware performance characteristic data of the target machine and the performance information of the target micro-service component;
and the determining module is used for determining the hardware requirement of the target machine according to the performance information of the target micro-service component, the relation between the target micro-service component and the target software and the performance requirement of the target software after deployment.
9. A computer device, characterized in that the computer device comprises:
a memory and a processor;
wherein the memory is connected with the processor and used for storing programs;
the processor is configured to implement the steps of the method of determining hardware requirements of a machine as claimed in any one of claims 1 to 7 by running a program stored in the memory.
10. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, causes the processor to carry out the steps of the method of determining hardware requirements of a machine according to any one of claims 1-7.
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