Delay parameter allocation method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a delay parameter distribution method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the current use data of a target disk if the disk performance data of the target disk meets a preset condition; inputting the current use data and the disk performance data into a parameter prediction model to generate a target delay distribution parameter; and configuring the target disk according to the target delay allocation parameter so as to control the write-in operation of the target disk through the target delay allocation parameter. The target delay allocation parameter is dynamically determined based on the use scene, the delay allocation of the disk is optimized, and the disk processing performance is improved. Wherein the target delay distribution parameter is generated by using the parameter prediction model, so that the generation efficiency and accuracy of the target delay distribution parameter are improved.

Description

Technical Field

[0001] Embodiments of the present application relate to the technical field of file storage, and in particular, to a method, apparatus, electronic device, and storage medium for allocating delay parameters, where the storage medium includes a computer-readable storage medium. Background Art

[0002] In file storage technology, the setting of the disk's delayed allocation space (delayed allocation parameter) has a significant impact on system performance. Disk Delayed Allocation is a file system technology that postpones the actual write operation of data to better optimize disk I / O performance. Through delayed allocation, the file system can collect more information before writing data, thereby performing more effective block allocation and reducing fragmentation.

[0003] However, existing delayed allocation technologies mainly rely on static configuration or empirical values to set the disk's delayed allocation space. For example, some file systems such as Ext4 and XFS have adopted delayed allocation technologies, but the configuration parameters of these technologies are usually static, and the static configuration method cannot adapt to changing usage scenarios, resulting in insufficient optimization of disk utilization and write performance, and limited effects.

[0004] Therefore, in different usage scenarios, how to dynamically determine the disk's delayed allocation space is an urgent problem to be solved. Summary of the Invention

[0005] Embodiments of the present application provide a method, apparatus, electronic device, and computer-readable storage medium for allocating delay parameters, which can provide delay allocation parameters adapted to the scenario for different usage scenarios, optimize the disk's delayed allocation, and improve the disk processing performance.

[0006] In a first aspect, embodiments of the present application provide a method for allocating delay parameters, the method including:

[0007] If the disk performance data of the target disk meets a preset condition, obtain the current usage data of the target disk;

[0008] Input the current usage data and the disk performance data into a parameter prediction model to generate target delay allocation parameters;

[0009] Configure the target disk according to the target delay allocation parameters to control the write operation of the target disk through the target delay allocation parameters.

[0010] In a second aspect, embodiments of the present application further provide a device for allocating delay parameters, the device including:

[0011] An acquisition module, configured to acquire the current usage data of the target disk if the disk performance data of the target disk meets a preset condition;

[0012] A generation module, configured to input the current usage data and the disk performance data into a parameter prediction model to generate a target latency allocation parameter;

[0013] A configuration module, configured to configure the target disk according to the target latency allocation parameter to control the write operation of the target disk through the target latency allocation parameter.

[0014] Optionally, in some embodiments of the present application, before inputting the current usage data and the disk performance data into the parameter prediction model to generate a target latency allocation parameter, the method further includes:

[0015] Acquiring system performance data of the file system;

[0016] The inputting the current usage data and the disk performance data into the parameter prediction model to generate a target latency allocation parameter includes:

[0017] Generating a target latency allocation parameter through the parameter prediction model by using the current usage data, the disk performance data, and the system performance data.

[0018] Optionally, in some embodiments of the present application, the generating a target latency allocation parameter through the parameter prediction model by using the current usage data, the disk performance data, and the system performance data includes:

[0019] Screening first performance reference data from the disk performance data, and screening second performance reference data from the system performance data;

[0020] Generating a target latency allocation parameter through the parameter prediction model by using the current usage data, the first performance reference data, the second performance reference data, a first weight corresponding to the first performance reference data, and a second weight corresponding to the second performance reference data.

[0021] Optionally, in some embodiments of the present application, the parameter prediction model includes a generative adversarial network model, and the generative adversarial network model includes a generator and a discriminator, and the discriminator is configured to optimize the generator;

[0022] The inputting the current usage data and the disk performance data into the parameter prediction model to generate a target latency allocation parameter includes:

[0023] Outputting a target latency allocation parameter for the current usage data and the disk performance data through the generator.

[0024] Optionally, in some embodiments of the present application, the training steps of the generative adversarial network model are as follows:

[0025] Input the sample disk usage data and the sample disk performance data into the generator to be trained to obtain the sample delay allocation parameters;

[0026] Simulate the usage scenario of the target disk through the sample delay allocation parameters to obtain the sample disk performance data;

[0027] Input the sample disk performance data and the preset disk performance data corresponding to the preset delay allocation parameters into the discriminator to be trained to obtain the performance discrimination result;

[0028] Update the generator to be trained and the discriminator to be trained according to the performance discrimination result to obtain the generative adversarial network model.

[0029] Optionally, in some embodiments of the present application, after inputting the current usage data and the disk performance data into the parameter prediction model to generate the target delay allocation parameters, the method further includes:

[0030] Obtain the reference delay allocation parameters generated by the target policy, where the target policy includes at least one of being generated by other parameter prediction models, screened based on a mapping relationship table, or manually customized;

[0031] Perform weighted processing on the target delay allocation parameters and the reference delay allocation parameters according to a preset weighting policy to obtain the optimized delay allocation parameters;

[0032] The configuring the target disk according to the target delay allocation parameters includes:

[0033] Configure the target disk according to the optimized delay allocation parameters.

[0034] Optionally, in some embodiments of the present application, before obtaining the current usage data of the target disk if the disk performance data of the target disk meets the preset conditions, the method further includes:

[0035] Obtain the disk performance data in real time;

[0036] Alternatively, in response to detecting the target data to be written to the target disk, obtain the disk performance data of the target disk.

[0037] In a third aspect, an embodiment of the present application further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps in the above-mentioned delay parameter allocation method are implemented.

[0038] Fourthly, an embodiment of the present application further provides a storage medium, which includes a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in the above-mentioned delay parameter allocation method are implemented.

[0039] Fifthly, an embodiment of the present application further provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in various optional implementation manners of the embodiments of the present application.

[0040] In an embodiment of the present application, if the disk performance data of the target disk meets a preset condition, the current usage data of the target disk is obtained, the current usage data and the disk performance data are input into a parameter prediction model to generate a target delay allocation parameter, and the target disk is configured according to the target delay allocation parameter, so as to control the write operation of the target disk through the target delay allocation parameter.

[0041] Among them, by determining the target delay allocation parameter based on the current usage data and the disk performance data of the target disk, the target delay allocation parameter is dynamically determined based on the usage scenario, the delay allocation of the disk is optimized, and the disk processing performance is improved.

[0042] Among them, by using the parameter prediction model to generate the target delay allocation parameter, the generation efficiency and accuracy of the target delay allocation parameter are improved. Description of the Drawings

[0043] In order to more clearly illustrate the technical solutions in the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 It is a schematic diagram of the scenario where the terminal device provided by the embodiment of the present application executes the delay parameter allocation method;

[0045] Figure 2 It is a schematic flowchart of the delay parameter allocation method provided by the embodiment of the present application;

[0046] Figure 3 It is a schematic structural diagram of the delay parameter allocation device provided by the embodiment of the present application;

[0047] Figure 4It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0048] Next, the technical solutions in the present application will be clearly and completely described in conjunction with the accompanying drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the protection scope of the present invention.

[0049] An embodiment of the present application provides a method, device, electronic device, and computer-readable storage medium for allocating delay parameters. Specifically, the embodiment of the present application provides a delay parameter allocation device applicable to an electronic device, which is used to provide delay allocation parameters adapted to different usage scenarios, optimize the delay allocation of the disk, and improve the disk processing performance. Specifically, the electronic device includes a terminal device or a server. The terminal device includes, but is not limited to, devices such as desktop computers and notebook computers. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery network (CDN, Content Delivery Network), and big data and artificial intelligence platforms. The server can be directly or indirectly connected through wired or wireless communication methods.

[0050] Please refer to Figure 1 , Figure 1 It is a schematic diagram of the scenario in which the terminal device provided by the embodiment of the present application executes the delay parameter allocation method. Among them, the specific execution process of the terminal device executing the delay parameter allocation method is as follows:

[0051] If it is detected that the disk performance data of the target disk of the terminal device 10 meets the preset conditions, the current usage data of the target disk is obtained. The terminal device 10 inputs the current usage data and the disk performance data into a parameter prediction model to obtain a target delay allocation parameter, and configures the target disk according to the target delay allocation parameter to control the write operation of the target disk through the target delay allocation parameter.

[0052] For example, when the target delay allocation parameter is determined based on the parameter prediction model, the timing of writing from the cache to the disk is controlled based on the target delay allocation parameter. For example, the target delay allocation parameter includes cache size, time, etc. Correspondingly, based on the control of the target delay allocation parameter, it can be controlled to write to the disk when the cache data reaches a set value or reaches a set time.

[0053] In summary, in the embodiments of the present application, the target delay allocation parameter is determined based on the current usage data and disk performance data of the target disk, realizing the dynamic determination of the target delay allocation parameter based on the usage scenario, optimizing the delay allocation of the disk, and improving the disk processing performance.

[0054] Among them, by using the parameter prediction model to generate the target delay allocation parameter, the generation efficiency and accuracy of the target delay allocation parameter are improved.

[0055] The following will be described in detail respectively. It should be noted that the description order of the following embodiments does not limit the priority order of the embodiments. Please refer to Figure 2 , Figure 2 which is a schematic flowchart of the delay parameter allocation method provided by the embodiments of the present application. Although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order from that shown in the flowchart. Specifically, the process of the delay parameter allocation method specifically includes:

[0056] 101. If the disk performance data of the target disk meets the preset condition, obtain the current usage data of the target disk.

[0057] Among them, the disk performance data refers to the disk I / O performance data, including but not limited to the score data of dimensions such as read / write latency, number of input / output operations per second, throughput, random access performance, sequential access performance, queue depth, and cache performance.

[0058] Among them, in the embodiments of the present application, the preset condition refers to the condition corresponding to poor disk performance. For example, the disk performance data is lower than the set threshold.

[0059] Among them, the current usage data includes disk utilization rate, read / write request size (the data block size involved in a single I / O operation), the number of I / O requests queued in the concurrent processing of the disk subsystem, the number of I / O operations that the disk can process per second, the amount of read / write data successfully processed by the disk per unit time, cache hit rate, etc.

[0060] Among them, the disk performance data and the current usage data of the target disk reflect the current usage scenario of the target disk. For example, through the disk performance data and the current usage data analysis, it is found that the target disk is currently in a scenario of insufficient available capacity, high disk utilization rate, and poor I / O performance.

[0061] 102. Input the current usage data and the disk performance data into the parameter prediction model to generate the target delay allocation parameter.

[0062] It should be noted that delayed allocation is a mechanism. When the file system receives a write request, it does not immediately write the data to the target disk. Instead, the data is temporarily stored in a cache in memory. The data will not be actually written to the target disk until certain conditions are met (such as the cache reaching a certain size, after a certain period of time, or when the system is idle). Among them, this condition is the delayed allocation parameter corresponding to the target disk. The target delayed allocation parameter is a delayed allocation parameter determined based on the current usage data and disk performance data of the embodiments of the present application.

[0063] Among them, by determining the target delayed allocation parameter based on the current usage data and disk performance data, the determination of the target delayed allocation parameter matching the current usage scenario is realized, which helps to optimize the delayed allocation of the disk and improve the disk processing performance.

[0064] Among them, by generating the target delayed allocation parameter based on the parameter prediction model, the generation efficiency and accuracy of the target delayed allocation parameter are improved.

[0065] 103. Configure the target disk according to the target delayed allocation parameter to control the write operation of the target disk through the target delayed allocation parameter.

[0066] For example, use the generated target delayed allocation parameter class to update the original delayed allocation parameter of the target disk.

[0067] In summary, the embodiments of the present application determine the target delayed allocation parameter based on the current usage data and disk performance data of the target disk, realize the dynamic determination of the target delayed allocation parameter based on the usage scenario, optimize the delayed allocation of the disk, and improve the disk processing performance.

[0068] Among them, by using the parameter prediction model to generate the target delayed allocation parameter, the generation efficiency and accuracy of the target delayed allocation parameter are improved.

[0069] It can be understood that the file system provides a management interface for files and directories. It processes the logical organization of files, access control, metadata management, etc. Therefore, the performance of the file system can also be used as a reference factor for determining the target delayed allocation parameter, thereby improving the accuracy of the target delayed allocation parameter. That is, optionally, in some embodiments of the present application, before the step of "inputting the current usage data and the disk performance data into a parameter prediction model to generate a target delayed allocation parameter", the method further includes:

[0070] Obtain the system performance data of the file system;

[0071] The step of "inputting the current usage data and the disk performance data into a parameter prediction model to generate a target delayed allocation parameter" includes:

[0072] Generate a target latency allocation parameter by means of the parameter prediction model, using the current usage data, the disk performance data, and the system performance data.

[0073] Among them, the file system performance data includes score data in dimensions such as read / write latency, throughput, file creation / deletion time, metadata operation performance, cache, and buffer performance.

[0074] Improve the accuracy of the target latency allocation parameter by combining the file system performance data.

[0075] Among them, since the disk performance data and the system performance data involve a large number of data dimensions and there is some data with low relevance to latency allocation, in the embodiments of this application, some data can be extracted from the disk performance data and the system performance data to generate the target latency allocation parameter. That is, optionally, in some embodiments of this application, the step of "generating a target latency allocation parameter by means of the parameter prediction model, using the current usage data, the disk performance data, and the system performance data" includes:

[0076] Screen the first performance reference data from the disk performance data, and screen the second performance reference data from the system performance data;

[0077] Generate a target latency allocation parameter by means of the parameter prediction model, using the current usage data, the first performance reference data, the second performance reference data, the first weight corresponding to the first performance reference data, and the second weight corresponding to the second performance reference data.

[0078] Among them, by screening some disk performance data and some system performance data and generating the target latency allocation parameter based on the set weights, the accuracy and effectiveness of the target latency allocation parameter are improved.

[0079] Among them, in the embodiments of this application, the first performance reference data and the second performance reference data can be screened according to strategies such as the data type, priority, and correlation degree of each dimension data in the disk performance data and the system performance data.

[0080] Optionally, in the embodiments of the present application, the parameter prediction model includes, but is not limited to, a generative adversarial network model, where the generative adversarial network model is trained based on a generative adversarial network (GAN, Generative Adversarial Network). Correspondingly, the parameter prediction model includes a trained generator and discriminator, where the discriminator is used to optimize the generator so as to output target latency allocation parameters through the optimized generator. That is, optionally, in some embodiments of the present application, the step of "inputting the current usage data and the disk performance data into a parameter prediction model to generate target latency allocation parameters" includes:

[0081] Outputting, by the generator, target latency allocation parameters for the current usage data and the disk performance data.

[0082] Among them, in the embodiments of the present application, the generator includes a first input layer, a first hidden layer, a second hidden layer, and a first output layer. Among them, the first input layer is used to receive the input current usage data and disk performance data. The first hidden layer and the second hidden layer contain several neurons, where the activation function is ReLU. The first output layer is used to generate target latency allocation parameters, where the activation function is a linear activation function.

[0083] In addition, the discriminator includes a second input layer, a third hidden layer, a fourth hidden layer, and a second output layer. Among them, the first input layer is used to receive the target latency allocation parameters or latency allocation parameters output by the first output layer of the generator. The third hidden layer and the fourth hidden layer contain several neurons, where the activation function is ReLU. The second output layer is used to generate a discrimination result for the target latency allocation parameters or latency allocation parameters, where the activation function is Sigmoid.

[0084] Among them, the training steps of the generative adversarial network model are as follows:

[0085] Inputting sample disk usage data and sample disk performance data into the generator to be trained to obtain sample latency allocation parameters;

[0086] Simulating the usage scenario of the target disk through the sample latency allocation parameters to obtain sample disk performance data;

[0087] Inputting the sample disk performance data and the preset disk performance data corresponding to the preset latency allocation parameters into the discriminator to be trained to obtain a performance discrimination result;

[0088] Updating the generator to be trained and the discriminator to be trained according to the performance discrimination result to obtain a generative adversarial network model.

[0089] Among them, the preset delay allocation parameter includes the delay allocation parameter set for training, such as the delay allocation parameter determined by the user according to empirical values. The preset disk performance data is the disk performance data detected when controlling the write operation by simulating the preset delay allocation parameter.

[0090] Correspondingly, the discriminator obtains a discrimination result by comparing the sample delay allocation parameter generated by the generator with the preset disk performance data. Among them, the discrimination result can present the comparison result in the form of scoring.

[0091] Correspondingly, through multiple generations, discriminations, and feedback of discrimination results by the generator and the discriminator, the training of the generative adversarial network is realized, and a parameter prediction model is obtained.

[0092] Among them, in the embodiment of the present application, when it is necessary to predict and generate the target delay allocation parameter in combination with the system performance data, it is only necessary to train the generative adversarial network in combination with the system performance data.

[0093] Correspondingly, in the embodiment of the present application, the parameter prediction model may also be other machine learning models (such as linear regression, decision tree, neural network, etc.) other than the generative adversarial network model. These machine learning models can predict the target delay allocation parameter according to the current usage data and disk performance data through the training of sample data.

[0094] Correspondingly, in the embodiment of the present application, the target delay allocation parameters can also be output respectively according to multiple parameter prediction models of different types or different training results, and the final target delay allocation parameter corresponding to each target delay allocation parameter is determined by a weighted method. That is, optionally, in some embodiments of the present application, after the step of "inputting the current usage data and the disk performance data into the parameter prediction model to generate the target delay allocation parameter", the method further includes:

[0095] Obtaining a reference delay allocation parameter generated by a target policy, where the target policy includes at least one of being generated by other parameter prediction models, screened based on a mapping relationship table, or manually defined;

[0096] Performing weighted processing on the target delay allocation parameter and the reference delay allocation parameter according to a preset weighted policy to obtain an optimized delay allocation parameter;

[0097] The configuring the target disk according to the target delay allocation parameter includes:

[0098] Configuring the target disk according to the optimized delay allocation parameter.

[0099] Among them, by performing weighted processing on the reference delay allocation parameter and the target delay allocation parameter, the accuracy of the finally obtained optimized delay allocation parameter is improved.

[0100] It can be understood that in the embodiments of the present application, when it is detected that the disk performance data meets the preset conditions, the task of obtaining the current usage data and triggering the generation of the target delay allocation parameter is triggered. In the embodiments of the present application, the disk performance data can be detected in real time or the disk performance data can be obtained after detecting the target data to be written to the disk. That is, optionally, in some embodiments of the present application, before the step "if the disk performance data of the target disk meets the preset conditions, obtain the current usage data of the target disk", the method includes:

[0101] Obtain the disk performance data in real time;

[0102] Or, in response to detecting the target data to be written to the target disk, obtain the disk performance data of the target disk.

[0103] Among them, by obtaining the disk performance data in real time, it helps to update the target delay allocation parameter of the target disk in real time, ensuring that various sudden input and write situations can be handled. And by triggering the acquisition of the disk performance data and then triggering the calculation of the target delay allocation parameter after detecting the target data to be written to the target disk, it helps to control the utilization of the computing resources of the device and reduce resource consumption. It can be understood that either of the above two methods can be selected based on different requirements for the allocation control of the delay parameter.

[0104] In summary, the embodiments of the present application determine the target delay allocation parameter based on the current usage data and disk performance data of the target disk, realize the dynamic determination of the target delay allocation parameter based on the usage scenario, optimize the delay allocation of the disk, and improve the disk processing performance. Among them, by using the parameter prediction model to generate the target delay allocation parameter, the generation efficiency and accuracy of the target delay allocation parameter are improved.

[0105] Among them, by combining the system performance data of the file system to generate the target delay allocation parameter, the accuracy of the target delay allocation parameter is improved.

[0106] To facilitate the better implementation of the delay parameter allocation method of the present application, the present application also provides a delay parameter allocation device based on the above delay parameter allocation method. The meanings of the nouns are the same as those in the above delay parameter allocation method, and the specific implementation details can refer to the description in the method embodiments.

[0107] Please refer to Figure 3 , Figure 3 which is the structural schematic diagram of the delay parameter allocation device provided by the embodiments of the present application. The delay parameter allocation device can be specifically as follows:

[0108] An acquisition module 201, configured to acquire current usage data of the target disk if disk performance data of the target disk meets a preset condition;

[0109] A generation module 202, configured to input the current usage data and the disk performance data into a parameter prediction model to generate a target delay allocation parameter;

[0110] A configuration module 203, configured to configure the target disk according to the target delay allocation parameter, so as to control a write operation of the target disk by the target delay allocation parameter.

[0111] Optionally, in some embodiments of the present application, before inputting the current usage data and the disk performance data into the parameter prediction model to generate a target delay allocation parameter, the method further includes:

[0112] Acquiring system performance data of a file system;

[0113] The inputting the current usage data and the disk performance data into the parameter prediction model to generate a target delay allocation parameter includes:

[0114] Generating a target delay allocation parameter by using the current usage data, the disk performance data, and the system performance data through the parameter prediction model.

[0115] Optionally, in some embodiments of the present application, the generating a target delay allocation parameter by using the current usage data, the disk performance data, and the system performance data through the parameter prediction model includes:

[0116] Screening first performance reference data from the disk performance data, and screening second performance reference data from the system performance data;

[0117] Generating a target delay allocation parameter by using the current usage data, the first performance reference data, the second performance reference data, a first weight corresponding to the first performance reference data, and a second weight corresponding to the second performance reference data through the parameter prediction model.

[0118] Optionally, in some embodiments of the present application, the parameter prediction model includes a generative adversarial network model, and the generative adversarial network model includes a generator and a discriminator, and the discriminator is configured to optimize the generator;

[0119] The inputting the current usage data and the disk performance data into the parameter prediction model to generate a target delay allocation parameter includes:

[0120] Output, through the generator, target latency allocation parameters for the current usage data and the disk performance data.

[0121] Optionally, in some embodiments of the present application, the training steps of the generative adversarial network model are as follows:

[0122] Input sample disk usage data and sample disk performance data into the generator to be trained to obtain sample latency allocation parameters;

[0123] Simulate the usage scenario of the target disk through the sample latency allocation parameters to obtain sample disk performance data;

[0124] Input the sample disk performance data and the preset disk performance data corresponding to the preset latency allocation parameters into the discriminator to be trained to obtain a performance discrimination result;

[0125] Update the generator to be trained and the discriminator to be trained according to the performance discrimination result to obtain a generative adversarial network model.

[0126] Optionally, in some embodiments of the present application, after inputting the current usage data and the disk performance data into the parameter prediction model to generate target latency allocation parameters, the method further includes:

[0127] Obtain reference latency allocation parameters generated by a target policy, where the target policy includes at least one of being generated by other parameter prediction models, screened based on a mapping relationship table, or manually customized;

[0128] Perform weighted processing on the target latency allocation parameters and the reference latency allocation parameters according to a preset weighting policy to obtain optimized latency allocation parameters;

[0129] The configuring the target disk according to the target latency allocation parameters includes:

[0130] Configure the target disk according to the optimized latency allocation parameters.

[0131] Optionally, in some embodiments of the present application, before obtaining the current usage data of the target disk if the disk performance data of the target disk meets a preset condition, the method further includes:

[0132] Obtain the disk performance data in real time;

[0133] Alternatively, in response to detecting target data to be written to the target disk, obtain the disk performance data of the target disk.

[0134] In an embodiment of the present application, if the disk performance data of the target disk meets a preset condition, the acquisition module 201 acquires the current usage data of the target disk. The generation module 202 inputs the current usage data and the disk performance data into a parameter prediction model to generate a target delay allocation parameter. The configuration module 203 configures the target disk according to the target delay allocation parameter to control the write operation of the target disk through the target delay allocation parameter.

[0135] Specifically, in an embodiment of the present application, by determining the target delay allocation parameter based on the current usage data and disk performance data of the target disk, the target delay allocation parameter is dynamically determined based on the usage scenario, the delay allocation of the disk is optimized, and the disk processing performance is improved.

[0136] Moreover, by using the parameter prediction model to generate the target delay allocation parameter, the generation efficiency and accuracy of the target delay allocation parameter are improved.

[0137] In addition, the present application also provides an electronic device, as Figure 4 shown, which shows a schematic structural diagram of the electronic device involved in the present application. Specifically:

[0138] The electronic device may include a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, an input unit 304, and other components. Those skilled in the art can understand that Figure 4 the structural diagram of the electronic device shown does not limit the electronic device, and it may include more or fewer components than shown, or combine certain components, or have different component arrangements. Among them:

[0139] The processor 301 is the control center of the electronic device, connecting various parts of the entire electronic device through various interfaces and lines. By running or executing software programs and / or modules stored in the memory 302, and calling data stored in the memory 302, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above modem processor may not be integrated into the processor 301.

[0140] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and data processing by running the software programs and modules stored in the memory 302. The memory 302 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the electronic device. In addition, the memory 302 can include high-speed random access memory and can also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices. Correspondingly, the memory 302 can also include a memory controller to provide the processor 301 with access to the memory 302.

[0141] The electronic device further includes a power supply 303 for powering each component. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 303 can also include any components such as one or more DC or AC power supplies, a recharge system, a power device debugging circuit, a power converter or inverter, and a power status indicator.

[0142] The electronic device may further include an input unit 304, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0143] Although not shown, the electronic device may further include a display unit, etc., which will not be elaborated here. Specifically, in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302, so as to implement the steps in any of the delay parameter allocation methods provided in the embodiments of the present application.

[0144] In the embodiment of the present application, if the disk performance data of the target disk meets the preset conditions, the current usage data of the target disk is obtained, the current usage data and the disk performance data are input into a parameter prediction model to generate a target delay allocation parameter, and the target disk is configured according to the target delay allocation parameter to control the write operation of the target disk through the target delay allocation parameter.

[0145] Among them, by determining the target delay allocation parameter based on the current usage data and disk performance data of the target disk, the target delay allocation parameter is dynamically determined based on the usage scenario, the delay allocation of the disk is optimized, and the disk processing performance is improved.

[0146] Among them, by using a parameter prediction model to generate target delay allocation parameters, the generation efficiency and accuracy of the target delay allocation parameters are improved.

[0147] For the specific implementation of each of the above operations, reference may be made to the previous embodiments and will not be elaborated here.

[0148] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions or by controlling relevant hardware through instructions. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0149] For this reason, the present application provides a storage medium, including a computer-readable storage medium, on which a computer program is stored. The computer program can be loaded by a processor to execute the steps in any of the delay parameter allocation methods provided by the present application.

[0150] For the specific implementation of each of the above operations, reference may be made to the previous embodiments and will not be elaborated here.

[0151] Among them, the computer-readable storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0152] Since the instructions stored in the computer-readable storage medium can execute the steps in any of the delay parameter allocation methods provided by the present application, the beneficial effects achievable by any of the delay parameter allocation methods provided by the present application can be realized. For details, reference may be made to the previous embodiments and will not be elaborated here.

[0153] The above has introduced in detail a delay parameter allocation method, device, electronic device, and computer-readable storage medium provided by the present application. Specific examples are used herein to elaborate on the principles and implementation manners of the present invention. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present invention; 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 manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for allocating delay parameters, characterized in that, The method includes: If the disk performance data of the target disk meets a preset condition, obtain the current usage data of the target disk; Input the current usage data and the disk performance data into a parameter prediction model to generate a target delay allocation parameter; Configure the target disk according to the target delay allocation parameter to control the write operation of the target disk through the target delay allocation parameter.

2. The delay parameter allocation method according to claim 1, wherein Before inputting the current usage data and the disk performance data into the parameter prediction model to generate a target delay allocation parameter, the method further includes: Obtain the system performance data of the file system; The step of inputting the current usage data and the disk performance data into the parameter prediction model to generate a target delay allocation parameter includes: Generate a target delay allocation parameter through the parameter prediction model using the current usage data, the disk performance data, and the system performance data.

3. The delay parameter allocation method according to claim 2, wherein The step of generating a target delay allocation parameter through the parameter prediction model using the current usage data, the disk performance data, and the system performance data includes: Screen first performance reference data from the disk performance data, and screen second performance reference data from the system performance data; Through the parameter prediction model, use the current usage data, the first performance reference data, the second performance reference data, the first weight corresponding to the first performance reference data, and the second weight corresponding to the second performance reference data to generate a target delay allocation parameter.

4. The delay parameter allocation method according to claim 1, wherein The parameter prediction model includes a generative adversarial network model, and the generative adversarial network model includes a generator and a discriminator, and the discriminator is used to optimize the generator; The step of inputting the current usage data and the disk performance data into the parameter prediction model to generate a target delay allocation parameter includes: Output a target delay allocation parameter for the current usage data and the disk performance data through the generator.

5. The delay parameter allocation method according to claim 4, wherein The training steps of the generative adversarial network model are as follows: Input sample disk usage data and sample disk performance data into the generator to be trained to obtain sample delay allocation parameters; Simulate the usage scenario of the target disk through the sample delay allocation parameters to obtain sample disk performance data; Input the sample disk performance data and the preset disk performance data corresponding to the preset delay allocation parameter into the discriminator to be trained to obtain a performance discrimination result; Update the generator to be trained and the discriminator to be trained according to the performance discrimination result to obtain a generative adversarial network model.

6. The delay parameter allocation method according to claim 1, wherein After inputting the current usage data and the disk performance data into the parameter prediction model to generate a target delay allocation parameter, the method further includes: Obtain a reference delay allocation parameter generated by a target strategy, where the target strategy includes at least one of being generated by other parameter prediction models, screened based on a mapping relationship table, or manually defined; Perform weighted processing on the target delay allocation parameter and the reference delay allocation parameter according to a preset weighted strategy to obtain an optimized delay allocation parameter; The step of configuring the target disk according to the target delay allocation parameter includes: Configure the target disk according to the optimized delay allocation parameter.

7. The delay parameter allocation method according to claim 1, wherein Before obtaining the current usage data of the target disk if the disk performance data of the target disk meets a preset condition, the method further includes: Obtain the disk performance data in real time; Alternatively, in response to detecting target data to be written to the target disk, obtain the disk performance data of the target disk.

8. A delay parameter allocation device, characterized in that, The device includes: An obtaining module, configured to obtain the current usage data of the target disk if the disk performance data of the target disk meets a preset condition; A generating module, configured to input the current usage data and the disk performance data into a parameter prediction model to generate a target delay allocation parameter; A configuration module, configured to configure the target disk according to the target delay allocation parameter to control the write operation of the target disk through the target delay allocation parameter.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the delay parameter allocation method according to any one of claims 1-7.

10. A storage medium, characterized in that, A computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps in the delay parameter allocation method according to any one of claims 1-7.

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