CN112463558A - Storage performance parameter statistical method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a storage performance parameter statistical method, a device, equipment and a storage medium. The method comprises the following steps: acquiring storage resource parameters of a cloud storage system; inputting the storage resource parameters into a preset statistical model to obtain first performance parameters; the XGboost model is trained by the preset statistical model based on sample storage resource parameters and sample performance parameters corresponding to the sample storage resource parameters; calculating the storage resource parameters based on the test tool to generate second performance parameters; and calculating to obtain a performance parameter statistical result by using the first performance parameter and the second performance parameter. The method can relatively ensure the accuracy of the storage performance parameters obtained by statistics. In addition, the application also provides a storage performance parameter statistical device, equipment and a storage medium, and the beneficial effects are as described above.
Description
Technical Field
The present application relates to the field of cloud storage, and in particular, to a storage performance parameter statistical method, apparatus, device, and storage medium.
Background
Cloud storage is a new concept developed by extending and deriving a cloud computing concept, and refers to a system which integrates a large number of different types of storage devices in a network through application software to cooperatively work through functions such as cluster application, a grid technology or a distributed file system, and provides data storage and service access functions to the outside, so that the data security is ensured, and the storage space is saved.
In order to ensure the reliability of cloud storage, storage performance parameters of cloud storage in different storage scenes need to be predicted, currently, a test tool is often used to perform calculation according to storage resource parameters of a cloud storage system to obtain the storage performance parameters, but in an actual scene, the storage performance parameters are affected by many factors, such as a read-write mode, the capacity and number of disks, a mode of forming the storage system, and the type of the disks. When the storage performance in the cloud computing is tested, the storage performance is interfered by factors such as networks and computing resources, and the accuracy of the storage performance parameters obtained through statistics is difficult to ensure.
Therefore, it is a problem to be solved by those skilled in the art to provide a statistical method for storage performance parameters to relatively ensure the accuracy of the statistical storage performance parameters.
Disclosure of Invention
The application aims to provide a storage performance parameter statistical method, a storage performance parameter statistical device, storage performance parameter statistical equipment and a storage medium.
In order to solve the above technical problem, the present application provides a storage performance parameter statistical method, including:
acquiring storage resource parameters of a cloud storage system;
inputting the storage resource parameters into a preset statistical model to obtain first performance parameters; the XGboost model is trained by the preset statistical model based on sample storage resource parameters and sample performance parameters corresponding to the sample storage resource parameters;
calculating the storage resource parameters based on the test tool to generate second performance parameters;
and calculating to obtain a performance parameter statistical result by using the first performance parameter and the second performance parameter.
Preferably, the generation process of the preset statistical model comprises the following steps:
acquiring a sample storage resource parameter of the cloud storage system and a sample performance parameter corresponding to the sample storage resource parameter;
and inputting the sample storage resource parameters and the sample performance parameters into the XGboost model to generate a preset statistical model.
Preferably, the obtaining of the sample storage resource parameters of the cloud storage system and the sample performance parameters corresponding to the sample storage resource parameters includes:
acquiring sample storage resource parameters of a cloud storage system;
and inputting the sample storage resource parameters into a vbench testing tool to generate sample performance parameters.
Preferably, the sample storage resource parameters and the sample performance parameters are input to the XGBoost model, and a preset statistical model is generated, including:
inputting a sample storage resource parameter and a sample performance parameter into the XGboost model;
and adjusting the training parameters in the XGboost model until the average absolute error of the XGboost model reaches a preset threshold value, and setting the XGboost model as a preset statistical model.
Preferably, the first performance parameter comprises a first IOPS parameter, the second performance parameter comprises a second IOPS parameter, and the performance parameter statistics comprise IOPS statistics.
Preferably, the acquiring storage resource parameters of the cloud storage system includes:
and acquiring a storage resource parameter of the OpenStack cloud storage system.
In addition, this application still provides a storage performance parameter statistics device, includes:
the resource parameter acquisition module is used for acquiring storage resource parameters of the cloud storage system;
the first performance parameter acquisition module is used for inputting the storage resource parameters into a preset statistical model to obtain first performance parameters; the XGboost model is trained by the preset statistical model based on sample storage resource parameters and sample performance parameters corresponding to the sample storage resource parameters;
the second performance parameter acquisition module is used for calculating the storage resource parameters based on the test tool to generate second performance parameters;
and the parameter result generation module is used for calculating by utilizing the first performance parameter and the second performance parameter to obtain a performance parameter statistical result.
Preferably, the apparatus further comprises:
the system comprises a sample parameter acquisition module, a data storage module and a data processing module, wherein the sample parameter acquisition module is used for acquiring sample storage resource parameters of the cloud storage system and sample performance parameters corresponding to the sample storage resource parameters;
and the model generation module is used for inputting the sample storage resource parameters and the sample performance parameters into the XGboost model and generating a preset statistical model.
In addition, the present application also provides a storage performance parameter statistics apparatus, including:
a memory for storing a computer program;
a processor for implementing the steps of the statistical method of storage performance parameters as described above when executing the computer program.
In addition, the present application also provides a computer readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the storage performance parameter statistical method as described above.
According to the storage performance parameter statistical method, firstly, storage resource parameters of a cloud storage system are obtained, then the storage resource parameters are input into a preset statistical model obtained by training an XGboost model based on sample storage resource parameters and corresponding sample performance parameters, further, first performance parameters are obtained, a test tool is used for calculating the storage resource parameters to generate second performance parameters, and finally, performance parameter statistical results are obtained through calculation of the first performance parameters and the second performance parameters. According to the method, the preset statistical model is obtained based on XGboost model training to count the storage resource parameters to obtain the first performance parameters, the second performance parameters generated by computing the storage resource parameters based on the testing tool are computed together to obtain the performance parameter statistical results, the performance parameter statistical results can be corrected through the preset statistical model and the testing tool together, and the accuracy of the counted storage performance parameters can be relatively ensured. In addition, the application also provides a storage performance parameter statistical device, equipment and a storage medium, and the beneficial effects are as described above.
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In order to more clearly illustrate the embodiments of the present application, the drawings needed for the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is a flow chart of a statistical method for storage performance parameters disclosed in an embodiment of the present application;
fig. 2 is a schematic structural diagram of a storage performance parameter statistic apparatus according to an embodiment of the present 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 only a part of the embodiments of the present application, and not all the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without any creative effort belong to the protection scope of the present application.
In order to ensure the reliability of cloud storage, storage performance parameters of cloud storage in different storage scenes need to be predicted, currently, a test tool is often used to perform calculation according to storage resource parameters of a cloud storage system to obtain the storage performance parameters, but in an actual scene, the storage performance parameters are affected by many factors, such as a read-write mode, the capacity and number of disks, a mode of forming the storage system, and the type of the disks. When the storage performance in the cloud computing is tested, the storage performance is interfered by factors such as networks and computing resources, and the accuracy of the storage performance parameters obtained through statistics is difficult to ensure.
Therefore, the core of the application is to provide a storage performance parameter statistical method to relatively ensure the accuracy of the statistical storage performance parameters.
In order that those skilled in the art will better understand the disclosure, the following detailed description will be given with reference to the accompanying drawings.
Referring to fig. 1, an embodiment of the present application discloses a storage performance parameter statistical method, including:
step S10: and acquiring storage resource parameters of the cloud storage system.
It should be noted that the storage resource parameters in this step include, but are not limited to, storage resources and communication resources in the cloud storage system, the storage resources include, but are not limited to, disk capacity, disk number, disk read-write mode, and the like, and the communication resources include, but are not limited to, network latency, network bandwidth, and the like.
The purpose of acquiring the storage resource parameters of the cloud storage system in this step is to count the corresponding storage performance parameters based on the storage resource parameters in the subsequent steps.
Step S11: and inputting the storage resource parameters into a preset statistical model to obtain first performance parameters.
The XGboost model is trained by the preset statistical model based on the sample storage resource parameters and the sample performance parameters corresponding to the sample storage resource parameters.
It should be noted that, in this embodiment, the preset statistical model is obtained by training the XGBoost model based on the sample storage resource parameter and the sample performance parameter corresponding to the sample storage resource parameter, so that the preset statistical model can establish a corresponding relationship between the storage resource parameter and the sample performance parameter, and then the step inputs the storage resource parameter into the preset statistical model, and the preset statistical model can analyze the storage resource parameter in an actual scene to obtain the corresponding first performance parameter.
The XGboost (eXtreme Gradient Boosting) model is an integrated learning method, classification and regression tasks are realized by constructing a plurality of decision trees, and the XGboost model has high execution efficiency and precision.
Step S12: and calculating the storage resource parameters based on the testing tool to generate second performance parameters.
After the storage resource parameters are input into the preset statistical model to obtain the first performance parameters, the step is further based on a test tool to calculate the storage resource parameters to generate second performance parameters. The first performance parameter and the second performance parameter are relatively performance parameters obtained in different manners, that is, the storage resource parameter is analyzed through a preset statistical model to obtain the first performance parameter, and the storage resource parameter is analyzed through a test tool to obtain the second performance parameter.
Step S13: and calculating to obtain a performance parameter statistical result by using the first performance parameter and the second performance parameter.
The storage resource parameters are input into a preset statistical model to obtain first performance parameters, and the storage resource parameters are operated based on a test tool to generate second performance parameters for common calculation to obtain performance parameter statistical results.
According to the storage performance parameter statistical method, firstly, storage resource parameters of a cloud storage system are obtained, then the storage resource parameters are input into a preset statistical model obtained by training an XGboost model based on sample storage resource parameters and corresponding sample performance parameters, further, first performance parameters are obtained, a test tool is used for calculating the storage resource parameters to generate second performance parameters, and finally, performance parameter statistical results are obtained through calculation of the first performance parameters and the second performance parameters. According to the method, the preset statistical model is obtained based on XGboost model training to count the storage resource parameters to obtain the first performance parameters, the second performance parameters generated by computing the storage resource parameters based on the testing tool are computed together to obtain the performance parameter statistical results, the performance parameter statistical results can be corrected through the preset statistical model and the testing tool together, and the accuracy of the counted storage performance parameters can be relatively ensured.
On the basis of the foregoing embodiment, as a preferred implementation manner, the generating process of the preset statistical model includes:
acquiring a sample storage resource parameter of the cloud storage system and a sample performance parameter corresponding to the sample storage resource parameter;
and inputting the sample storage resource parameters and the sample performance parameters into the XGboost model to generate a preset statistical model.
It should be noted that, when the preset statistical model is generated, first, a sample storage resource parameter of the cloud storage system and a sample performance parameter corresponding to the sample storage resource parameter are obtained, where a corresponding relationship exists between the sample storage resource parameter and the sample performance parameter, after the sample storage resource parameter of the cloud storage system and the sample performance parameter corresponding to the sample storage resource parameter are obtained, the sample storage resource parameter and the sample performance parameter are further input to the XGBoost model, and then, by adjusting a training parameter in the XGBoost model, the XGBoost model after adjusting the training parameter can obtain a performance parameter whose error with the sample performance parameter reaches an expected value according to analysis of the sample storage resource parameter, and the XGBoost model is set as the preset statistical model. The embodiment further ensures the reliability of the generation process of the preset statistical model.
Further, as a preferred embodiment, acquiring a sample storage resource parameter of the cloud storage system and a sample performance parameter corresponding to the sample storage resource parameter includes:
acquiring sample storage resource parameters of a cloud storage system;
and inputting the sample storage resource parameters into a vbench testing tool to generate sample performance parameters.
It should be noted that, in this embodiment, when obtaining the sample storage resource parameters of the cloud storage system and the sample performance parameters corresponding to the sample storage resource parameters, the sample storage resource parameters of the cloud storage system are first obtained, and then the sample performance parameters are generated by the vddbench test tool in a manner of inputting the sample storage resource parameters to the vddbench test tool. Since the vddnch test tool is an I/O (input/output) workload generator, it is used to verify data integrity and measure the performance of direct attach and network attached storage. In the embodiment, the sample performance parameters are generated by inputting the sample storage resource parameters into the vbtech testing tool, so that the reliability of generating the sample performance parameters can be relatively ensured.
Further, as a preferred embodiment, the step of inputting the sample storage resource parameter and the sample performance parameter into the XGBoost model to generate the preset statistical model includes:
inputting a sample storage resource parameter and a sample performance parameter into the XGboost model;
and adjusting the training parameters in the XGboost model until the average absolute error of the XGboost model reaches a preset threshold value, and setting the XGboost model as a preset statistical model.
It should be noted that, in the embodiment, in the process of inputting the sample storage resource parameters and the sample performance parameters to the XGBoost model to train the XGBoost model, the sample storage resource parameters and the sample performance parameters are firstly input to the XGBoost model, and then the training parameters in the XGBoost model are adjusted until the absolute mean error of the XGBoost model reaches the preset threshold, and after the absolute mean error of the XGBoost model reaches the preset threshold, the XGBoost model is set as the preset statistical model. According to the embodiment, the XGboost model is trained based on the preset threshold value, so that the flexibility and the reliability of the process of generating the preset statistical model are further ensured.
Further, as a preferred embodiment, the first performance parameter includes a first IOPS parameter, the second performance parameter includes a second IOPS parameter, and the performance parameter statistic includes an IOPS statistic.
It should be noted that in this embodiment, the performance parameter is specifically an IOPS parameter, and an IOPS (Input/Output Operations Per Second) is a measurement method used for performance testing of a computer storage device (such as a Hard Disk Drive (HDD), a Solid State Disk (SSD), or a Storage Area Network (SAN)), and may be regarded as the number of read/write times Per Second. According to the embodiment, the storage performance of the cloud storage system can be relatively accurately measured through the IOPS parameters, and the accuracy and reliability of storage performance parameter statistics can be relatively ensured.
On the basis of the above series of embodiments, as a preferred implementation, the acquiring storage resource parameters of the cloud storage system includes:
and acquiring a storage resource parameter of the OpenStack cloud storage system.
It should be noted that OpenStack is an open-source cloud computing management platform project, which is a project combined by several main components to complete some specific work, and OpenStack is an open-source project aiming at providing software for construction and management of public and private clouds, and providing extensible and flexible cloud computing services for the private clouds and the public clouds. The important point of the embodiment is to analyze the storage resource parameters of the OpenStack cloud storage system to generate corresponding storage performance parameters, so that the reliability of counting the storage performance parameters of the OpenStack cloud storage system can be relatively ensured.
Referring to fig. 2, an embodiment of the present application provides a storage performance parameter statistic apparatus, including:
the resource parameter acquiring module 10 is configured to acquire a storage resource parameter of the cloud storage system;
the first performance parameter obtaining module 11 is configured to input the storage resource parameter into a preset statistical model to obtain a first performance parameter; the XGboost model is trained by the preset statistical model based on sample storage resource parameters and sample performance parameters corresponding to the sample storage resource parameters;
the second performance parameter obtaining module 12 is configured to perform an operation on the storage resource parameter based on the test tool to generate a second performance parameter;
and the parameter result generating module 13 is configured to calculate a performance parameter statistical result by using the first performance parameter and the second performance parameter.
Further, as a preferred embodiment, the apparatus further comprises:
the system comprises a sample parameter acquisition module, a data storage module and a data processing module, wherein the sample parameter acquisition module is used for acquiring sample storage resource parameters of the cloud storage system and sample performance parameters corresponding to the sample storage resource parameters;
and the model generation module is used for inputting the sample storage resource parameters and the sample performance parameters into the XGboost model and generating a preset statistical model.
The storage performance parameter counting device provided by the application firstly obtains storage resource parameters of a cloud storage system, then inputs the storage resource parameters to a preset counting model obtained by training an XGboost model based on sample storage resource parameters and corresponding sample performance parameters together, further obtains first performance parameters, utilizes a testing tool to calculate the storage resource parameters to generate second performance parameters, and finally obtains performance parameter counting results through calculation of the first performance parameters and the second performance parameters. The device obtains the preset statistical model based on the XGboost model training to count the storage resource parameters to obtain the first performance parameters, obtains the performance parameter statistical result based on the second performance parameter common operation generated by the test tool operating the storage resource parameters, can realize the correction of the performance parameter statistical result through the preset statistical model and the test tool, and further can relatively ensure the accuracy of the counted storage performance parameters.
In addition, the present application also provides a storage performance parameter statistics apparatus, including:
a memory for storing a computer program;
a processor for implementing the steps of the statistical method of storage performance parameters as described above when executing the computer program.
According to the storage performance parameter statistical equipment, firstly, storage resource parameters of a cloud storage system are obtained, then the storage resource parameters are input into a preset statistical model obtained by training an XGboost model based on sample storage resource parameters and corresponding sample performance parameters, further, first performance parameters are obtained, a testing tool is used for calculating the storage resource parameters to generate second performance parameters, and finally, performance parameter statistical results are obtained through calculation of the first performance parameters and the second performance parameters. The device obtains the preset statistical model based on the XGboost model training to count the storage resource parameters to obtain the first performance parameters, and obtains the performance parameter statistical result based on the second performance parameter common operation generated by the test tool operating the storage resource parameters, so that the performance parameter statistical result can be corrected through the preset statistical model and the test tool, and the accuracy of the counted storage performance parameters can be relatively ensured.
In addition, the present application also provides a computer readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the storage performance parameter statistical method as described above.
The computer-readable storage medium provided by the application firstly obtains storage resource parameters of a cloud storage system, then inputs the storage resource parameters to a preset statistical model obtained by training an XGboost model based on sample storage resource parameters and corresponding sample performance parameters together, further obtains first performance parameters, utilizes a test tool to calculate the storage resource parameters to generate second performance parameters, and finally obtains performance parameter statistical results through calculation of the first performance parameters and the second performance parameters. The computer readable storage medium obtains the preset statistical model based on XGboost model training to count the storage resource parameters to obtain the first performance parameters, and obtains the performance parameter statistical result based on the second performance parameter common operation generated by the test tool operating the storage resource parameters, so that the performance parameter statistical result can be corrected through the preset statistical model and the test tool, and the accuracy of the counted storage performance parameters can be relatively ensured.
The storage performance parameter statistical method, device, equipment and storage medium provided by the present application are introduced in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method for statistics of storage performance parameters, comprising:
acquiring storage resource parameters of a cloud storage system;
inputting the storage resource parameters into a preset statistical model to obtain first performance parameters; the XGboost model is trained by the preset statistical model based on sample storage resource parameters and sample performance parameters corresponding to the sample storage resource parameters;
calculating the storage resource parameters based on a test tool to generate second performance parameters;
and calculating to obtain a performance parameter statistical result by using the first performance parameter and the second performance parameter.
2. The statistical method of storage performance parameters of claim 1, wherein the generation process of the preset statistical model comprises:
acquiring the sample storage resource parameters of the cloud storage system and sample performance parameters corresponding to the sample storage resource parameters;
and inputting the sample storage resource parameters and the sample performance parameters into the XGboost model to generate the preset statistical model.
3. The method according to claim 2, wherein the obtaining the sample storage resource parameters of the cloud storage system and the sample performance parameters corresponding to the sample storage resource parameters comprises:
obtaining the sample storage resource parameters of the cloud storage system;
and inputting the sample storage resource parameters to a vbench testing tool to generate the sample performance parameters.
4. The statistical method for storage performance parameters according to claim 2, wherein the inputting the sample storage resource parameters and the sample performance parameters into the XGBoost model to generate the preset statistical model comprises:
inputting the sample storage resource parameters and the sample performance parameters to the XGboost model;
and adjusting the training parameters in the XGboost model until the average absolute error of the XGboost model reaches a preset threshold value, and setting the XGboost model as the preset statistical model.
5. The storage performance parameter statistics method of claim 1, wherein the first performance parameter comprises a first IOPS parameter, the second performance parameter comprises a second IOPS parameter, and the performance parameter statistics comprise IOPS statistics.
6. The storage performance parameter statistical method according to any one of claims 1 to 5, wherein the obtaining storage resource parameters of a cloud storage system includes:
and acquiring the storage resource parameters of the OpenStack cloud storage system.
7. A stored performance parameter statistic apparatus, comprising:
the resource parameter acquisition module is used for acquiring storage resource parameters of the cloud storage system;
the first performance parameter acquisition module is used for inputting the storage resource parameters into a preset statistical model to obtain first performance parameters; the XGboost model is trained by the preset statistical model based on sample storage resource parameters and sample performance parameters corresponding to the sample storage resource parameters;
the second performance parameter acquisition module is used for calculating the storage resource parameters based on a test tool to generate second performance parameters;
and the parameter result generation module is used for calculating by utilizing the first performance parameter and the second performance parameter to obtain a performance parameter statistical result.
8. The device of claim 7, wherein the device further comprises:
the sample parameter acquisition module is used for acquiring the sample storage resource parameters of the cloud storage system and sample performance parameters corresponding to the sample storage resource parameters;
and the model generation module is used for inputting the sample storage resource parameters and the sample performance parameters into the XGboost model to generate the preset statistical model.
9. A device for storing performance parameter statistics, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the storage performance parameter statistics method of any of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the storage performance parameter statistical method according to any one of claims 1 to 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110674009A (en) * | 2019-09-10 | 2020-01-10 | 平安普惠企业管理有限公司 | Application server performance monitoring method and device, storage medium and electronic equipment |
| CN111352812A (en) * | 2020-02-22 | 2020-06-30 | 苏州浪潮智能科技有限公司 | Method and system for predicting performance of storage device based on naive Bayes machine learning model |
| CN111881010A (en) * | 2020-07-29 | 2020-11-03 | 北京浪潮数据技术有限公司 | Method and device for predicting performance of storage equipment and computer readable storage medium |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110674009A (en) * | 2019-09-10 | 2020-01-10 | 平安普惠企业管理有限公司 | Application server performance monitoring method and device, storage medium and electronic equipment |
| CN111352812A (en) * | 2020-02-22 | 2020-06-30 | 苏州浪潮智能科技有限公司 | Method and system for predicting performance of storage device based on naive Bayes machine learning model |
| CN111881010A (en) * | 2020-07-29 | 2020-11-03 | 北京浪潮数据技术有限公司 | Method and device for predicting performance of storage equipment and computer readable storage medium |
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