Storage device performance test method and device, equipment, medium and product

Abstract

The embodiment of the invention provides a storage device performance testing method and device, equipment, a medium and a product. The method comprises the steps that feature parameters corresponding to storage device performance are obtained; determining the relevancy between the characteristic parameters and the IOPS of the storage device; performing preset dimension combination processing on the different feature parameters based on the relevancy and a preset SISSO rule to obtain a target feature parameter combination; training a preset neural network model through the target feature parameter combination to obtain a target neural network model; and predicting the performance of the storage device in the server in the current state through the target neural network model, and outputting a predicted value of the performance of the storage device. According to the method, the neural network model and the SISSO characteristic parameter screening method are utilized to obtain the key characteristic parameter factors influencing the performance of the RAID card chip, so that the performance of the RAID card chip can be further tested, the performance of the RAID card chip is optimized to the greatest extent, and the labor cost is further reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of storage devices, and in particular to a storage device performance testing method, device, equipment, medium and product. Background Art

[0002] RAID (Redundant Array of Independent Disks) cards organize multiple disks together, which makes them appear as a larger independent disk to users. Therefore, applying RAID technology to processing massive data and server storage has a good application prospect. The access speed of SSD disks (Solid State Disk) is too slow compared to that of CPU (Central Processing Unit). Through RAID technology, the overall access speed of disks can be increased exponentially by concurrently accessing multiple disks. In addition, RAID technology can also provide fault tolerance through data verification and backup, making the system more stable and secure.

[0003] Therefore, the performance of the RAID card chip directly affects the overall performance of the information system. It is necessary to perform performance testing on it and maintain abnormal disks in time to ensure the normal operation of the information system.

[0004] However, the existing RAID card chip performance testing method still has some drawbacks. The existing technology adopts setting a fixed time to regularly test the disk performance. The test personnel mainly use the FIO (Flexible I / O Tester) tool to test the performance of the RAID card chip in a specific use case scenario. It cannot cover more scenarios and requires a lot of manpower and test platform and tool resources within a fixed time, resulting in reduced accuracy and efficiency of storage device performance testing. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a storage device performance testing method, apparatus, device, medium and product. The specific technical solution is as follows: In a first aspect of the implementation of the present invention, a storage device performance test method is first provided to obtain characteristic parameters corresponding to the performance of the storage device; Determining the correlation between the characteristic parameter and the IOPS of the storage device; Based on the correlation and the preset SISSO rule, the different feature parameters are processed in a preset dimension combination to obtain a target feature parameter combination; The preset neural network model is trained by the target feature parameter combination to obtain a target neural network model; The target neural network model is used to predict the performance of the storage device in the server in the current state, and a predicted value of the storage device performance is output.

[0006] Optionally, performing preset dimension combination processing on different feature parameters based on the correlation and preset SISSO rules to obtain a target feature parameter combination includes: Filtering out descriptors related to the IOPS of the storage device from the characteristic parameters based on determining independent filtering rules; forming a feature subspace based on the descriptors; Obtaining a target descriptor from the feature subspace based on a sparse operator rule and a preset canonical algorithm; A preset dimension combination process is performed based on the target descriptor to obtain a target feature parameter combination.

[0007] Optionally, performing preset dimension combination processing on different feature parameters based on the correlation and preset SISSO rules to obtain a target feature parameter combination includes: When it is detected that the correlation is less than a first preset threshold, obtaining a second characteristic parameter set; Performing preset dimension combination processing on the second feature parameter set by a preset SISSO rule to obtain a second target feature parameter combination; Determine the correlation between the second target characteristic parameter combination and the IOPS; When it is detected that the correlation is greater than or equal to the preset threshold, the second target feature parameter combination is output.

[0008] Optionally, after the step of determining the correlation between the second target characteristic parameter combination and the IOPS, the method includes: When it is detected that the correlation is less than the first preset threshold, the second target feature parameter combination is removed.

[0009] Optionally, performing preset dimension combination processing on different feature parameters based on the correlation and preset SISSO rules to obtain a target feature parameter combination includes: When it is detected that the correlation is greater than or equal to a first preset threshold, obtaining a first feature parameter set; The first feature parameter set is processed by preset dimension combination according to a preset SISSO rule to obtain a first target feature parameter combination.

[0010] Optionally, the storage device includes a RAID card chip; and the prediction of the storage device performance in the server in the current state by the target neural network model and the output of the storage device performance prediction value include: The performance data related to the RAID card chip in the server in the current state is input into the target neural network model, and the predicted value of the storage device performance is output.

[0011] Optionally, before the step of acquiring characteristic parameters corresponding to the performance of the storage device, the method includes: Obtain the IOPS test data set corresponding to the storage device; The acquiring of characteristic parameters corresponding to the performance of the storage device comprises: A characteristic parameter corresponding to the performance of the storage device is obtained based on a pre-acquired IOPS test data set corresponding to the storage device.

[0012] Optionally, the characteristic parameters corresponding to the storage device performance include at least one of RAID level, number of disks, stripe size, number of logical units, number of storage output links, disk type, RAID controller information, disk capacity, RAID group status, cache configuration, RAID rebuild time, RAID stripe depth, RAID write penalty, number of disk failures in the RAID group, and load balancing strategy of the RAID group.

[0013] Optionally, obtaining an IOPS test data set corresponding to the storage device includes: In response to a first input on the human-computer interaction interface, obtaining a test parameter and IOPS for a storage device in the server; An IOPS test data set corresponding to the storage device is generated based on the test parameters and the IOPS.

[0014] Optionally, before the step of obtaining test parameters and IOPS for a storage device in the server in response to a first input on the human-computer interaction interface, the method includes: Create a test environment corresponding to the storage device.

[0015] Optionally, the training of a preset neural network model by the target feature parameter combination to obtain a target neural network model includes: Debug the hyperparameters corresponding to the preset neural network model to obtain the target hyperparameters; The preset neural network model is iteratively trained based on the target hyperparameter, the target feature parameter combination and a preset training data set until the preset neural network module reaches a preset number of training times to obtain a target neural network model.

[0016] Optionally, after the step of predicting the performance of the storage device in the server in the current state by using the target neural network model and outputting the predicted value of the storage device performance, the method includes: Determine whether the IOPS performance of the storage device meets a preset condition based on the predicted value of the storage device performance; If so, continue to predict the performance of the storage device through the target neural network model.

[0017] Optionally, after the step of judging whether the IOPS performance of the storage device meets a preset condition based on the predicted value of the storage device performance, the method includes: If not, the initial monitoring time is adjusted based on the predicted value of the storage device performance, and the IOPS test data set corresponding to the storage device is reacquired.

[0018] Optionally, the adjusting the initial monitoring time based on the predicted value of the storage device performance includes: When it is monitored that the predicted value of the storage device performance is less than a second preset threshold, the initial monitoring time is shortened according to a preset time rule to obtain a first monitoring time, and the obtained test data is added to the test data set, the neural network model is retrained, and the new neural network model is used to predict the performance of the storage device; When it is monitored that the predicted value of the storage device performance is greater than or equal to a second preset threshold, the monitoring time is increased according to the preset time rule, and the test data set is added to the original sample, the model is continued to be trained, and the new neural network model is used to predict the performance of the storage device performance.

[0019] Optionally, after the step of predicting the performance of the storage device in the server in the current state by using the target neural network model and outputting the predicted value of the storage device performance, the method includes: Obtaining actual performance value of the storage device; Performing mean absolute error processing based on the actual value of the storage device performance and the predicted value of the storage device performance to obtain a first target difference; The target neural network model is optimized based on the first target difference.

[0020] Optionally, after the step of predicting the performance of the storage device in the server in the current state by using the target neural network model and outputting the predicted value of the storage device performance, the method includes: Obtaining actual performance value of the storage device; Performing root mean square error processing based on the actual value of the storage device performance and the predicted value of the storage device performance to obtain a second target difference value; The target neural network model is optimized based on the second target difference.

[0021] In a second aspect of the present invention, a storage device performance test device is provided, the device comprising: An acquisition module, used to acquire characteristic parameters corresponding to the performance of the storage device; A determination module, used to determine the correlation between the characteristic parameter and the IOPS of the storage device; A combination module, used for performing preset dimension combination processing on different feature parameters based on the correlation and the preset SISSO rule to obtain a target feature parameter combination; A training module, used to train a preset neural network model through the target feature parameter combination to obtain a target neural network model; The prediction module is used to predict the performance of the storage device in the server under the current state through the target neural network model and output the predicted value of the storage device performance.

[0022] In a third aspect of the implementation of the present invention, there is also provided a communication device, comprising: a transceiver, a memory, a processor, and a program stored in the memory and executable on the processor; The processor is used to read the program in the memory to implement the storage device performance testing method as described in any one of the first aspects.

[0023] In a fourth aspect of the implementation of the present invention, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, and when the computer-readable storage medium is executed on a computer, the computer implements the storage device performance testing method as described in any one of the first aspects.

[0024] In a fifth aspect of the implementation of the present invention, a computer program product is also provided, including a computer program / instruction, which, when executed by a processor, implements the storage device performance testing method as described in any one of the first aspects.

[0025] The storage device performance test method provided in the embodiment of the present invention includes: obtaining characteristic parameters corresponding to the performance of the storage device; determining the correlation between the characteristic parameters and the IOPS of the storage device; performing preset dimension combination processing on different characteristic parameters based on the correlation and the preset SISSO rule to obtain a target characteristic parameter combination; training a preset neural network model through the target characteristic parameter combination to obtain a target neural network model; predicting the performance of the storage device in the server in the current state through the target neural network model, and outputting the predicted value of the storage device performance. In the embodiment of the present invention, by using the neural network model and the SISSO characteristic parameter screening method, the key characteristic parameter factors affecting the performance of the RAID card chip are obtained, and then the performance of the RAID card chip can be further tested, and the performance of the RAID card chip can be optimized to the greatest extent, and the labor cost can be further reduced. In addition, by predicting the performance of the RAID card chip based on the characteristic parameters with high correlation, the maximum performance of the RAID card chip under a certain capacity, a certain RAID scenario, and the surrounding environment can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0027] Figure 1 A flowchart of a method for testing the performance of a storage device provided by an embodiment of the present invention; Figure 2 is a device block diagram of a storage device performance test device provided by an embodiment of the present invention; Figure 3 is a schematic diagram of a communication device provided by an embodiment of the present invention; Figure 4 This is an exemplary IO path diagram for testing a RAID card chip from a host provided by an embodiment of the present invention; Figure 5 is a schematic diagram of an exemplary storage device structure provided by an embodiment of the present invention; Figure 6 is a schematic diagram of an exemplary SISSO operation workflow provided by an embodiment of the present invention; Figure 7 is a schematic diagram of an exemplary neural network model provided by an embodiment of the present invention; Figure 8 is another exemplary neural network model schematic diagram provided by an embodiment of the present invention; Fig. 9 The present invention provides an exemplary storage device performance test process diagram. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. However, it can be understood by those skilled in the art that in the embodiments of the present invention, many technical details are proposed in order to enable readers to better understand the present invention. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present invention can also be implemented. The division of the following embodiments is for the convenience of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined and referenced with each other without contradiction.

[0029] Reference Figure 1 , shows a flowchart of the steps of a storage device performance testing method provided by an embodiment of the present invention, the method may include: Step 101, obtaining characteristic parameters corresponding to the performance of the storage device; Step 102, determining the correlation between the characteristic parameter and the IOPS of the storage device; Optionally, before the step of acquiring characteristic parameters corresponding to the performance of the storage device, the method includes: Obtain the IOPS test data set corresponding to the storage device; The acquiring of characteristic parameters corresponding to the performance of the storage device comprises: A characteristic parameter corresponding to the performance of the storage device is obtained based on a pre-acquired IOPS test data set corresponding to the storage device.

[0030] Optionally, the characteristic parameters corresponding to the storage device performance include at least one of RAID level, number of disks, stripe size, number of logical units, number of storage output links, disk type, RAID controller information, disk capacity, RAID group status, cache configuration, RAID rebuild time, RAID stripe depth, RAID write penalty, number of disk failures in the RAID group, and load balancing strategy of the RAID group.

[0031] It should be noted that in the embodiment of the present invention, a RAID card chip performance test model is constructed based on different RAID level information, disk size and link information through a neural network model. Through this invention, the performance of the RAID card chip can be understood in real time and the hard disk stocking cost can be effectively reduced, the risk of data loss can be greatly reduced, and a reliable data basis for hard disk maintenance can be provided. The specific implementation process can be referred to Fig. 9As shown, specifically, the IOPS test data set of the RAID card chip is collected, and multiple characteristic parameters related to the performance (such as RAID level, disk size and link information, etc.) are obtained. By judging the correlation between these characteristic parameters and the IOPS test data set, a parameter set with high correlation and a parameter set with low correlation are obtained respectively. For different parameter sets, the characteristic parameters are combined in different dimensions through SISSO, and their correlation with the IOPS test data set is judged again. The neural network model is trained using the filtered characteristic parameters (the characteristic parameters combined in the parameter set with high correlation and the characteristic parameters with high correlation combined in the parameter set with low correlation) to construct a prediction model that can closely reflect the performance of the RAID card chip. After obtaining the prediction model, the model is used to quickly predict the performance of the RAID card chip in the current state to judge whether the IOPS performance is good; if the performance is good, it is further judged whether it is close to the set monitoring time. If it is close to the set monitoring time, the model is continued to be used for prediction, otherwise the process ends; if the performance is not good, the monitoring time is shortened, and a new IOPS test data set is obtained.

[0032] Based on the theoretical deduction and test analysis of RAID card chip performance, select several characteristic parameters with the highest correlation with RAID card chip performance. For example, the disk size characteristic parameter mainly refers to the storage capacity, which determines the amount of data that can be stored. RAID (Redundant Array of Independent Disks) information (such as manufacturer, model, SN, status, etc.), Name Space (logical space accessible to the host) information, SSD (Solid State Disk) information (such as manufacturer, model, SN, status, capacity, etc.), different levels of RAID information: RAID-0, RAID-1, RAID-2, RAID-3, RAID-4, RAID-5, RAID-10, RAID-50, RAID-TP, etc. RAID stripe is the basic unit of RAID data processing, and stripes are also numbered starting from 0. The number of disks contained in RAID, that is, the size of the disk, the number of disks, and the data of the hard disk usage environment of each solid-state hard disk manufacturer are used to obtain other characteristic parameter values. Storage output link quantity information: refers to the number of external connections or output channels that a storage device (such as a hard disk, storage array, etc.) can support. The number of LUNs (Logical Unit Number, a logical unit on a physical storage device) created by each RAID: In fact, it depends on multiple factors, including the RAID configuration, the capacity of the storage device, the performance requirements of the storage system, and the data management strategy. In a RAID environment, LUN is usually a virtual storage unit created on a RAID group or storage pool to provide storage space to the host. Each LUN can be regarded as an independent storage volume that can be recognized and managed by the operating system or application. The configuration of RAID (such as RAID 0, RAID 1, RAID 5, etc.) will affect the creation and performance of LUN. For example, RAID 0 provides higher performance but no data redundancy, while RAID 5 may sacrifice some performance while providing data redundancy. Therefore, it is necessary to consider the performance, reliability, and data protection requirements when selecting a RAID level.

[0033] The structure of a solid state drive SSD is as follows Figure 5 As shown, it is demonstrated that the solid-state drive can be used for garbage collection, bad block handling, address translation and wear leveling, as well as flash memory interface and multiple flash memory chips.

[0034] Optionally, obtaining an IOPS test data set corresponding to the storage device includes: In response to a first input on the human-computer interaction interface, obtaining a test parameter and IOPS for a storage device in the server; An IOPS test data set corresponding to the storage device is generated based on the test parameters and the IOPS.

[0035] It should be noted that in the embodiment of the present invention, the test parameters and IOPS (Input / Output Operations Per Second) of the RAID card chip to be tested in the server input on the human-computer interaction interface are collected. Figure 4 As shown, it is an IO (Input / Output) path diagram for testing RAID card chips from a host. The host accesses multiple logical spaces through memory and input / output requests. The logical spaces then organize data through independent disk redundant array 5 and independent disk redundant array 0 technology, and multiple solid-state hard disks are used as storage media. For different RAID levels, random write, random read, sequential write, sequential read and mixed read and write operations can be performed on disks with block sizes of 4K, 8K, 16K, 32K, 64K, 128K, 256K, 1M, 4M, 16M, 1G, 10G, etc. In the present invention, the collected RAID card chip IOPS is mainly based on a series of test data sets obtained by the FIO test tool.

[0036] Optionally, before the step of obtaining test parameters and IOPS for a storage device in the server in response to a first input on the human-computer interaction interface, the method includes: Create a test environment corresponding to the storage device.

[0037] Step 103, performing preset dimension combination processing on different feature parameters based on the correlation and the preset SISSO rule to obtain a target feature parameter combination; Optionally, performing preset dimension combination processing on different feature parameters based on the correlation and preset SISSO rules to obtain a target feature parameter combination includes: Filtering out descriptors related to the IOPS of the storage device from the characteristic parameters based on determining independent filtering rules; forming a feature subspace based on the descriptors; Obtaining a target descriptor from the feature subspace based on a sparse operator rule and a preset canonical algorithm; A preset dimension combination process is performed based on the target descriptor to obtain a target feature parameter combination.

[0038] Optionally, performing preset dimension combination processing on different feature parameters based on the correlation and preset SISSO rules to obtain a target feature parameter combination includes: When it is detected that the correlation is less than a first preset threshold, obtaining a second characteristic parameter set; Performing preset dimension combination processing on the second feature parameter set by a preset SISSO rule to obtain a second target feature parameter combination; Determine the correlation between the second target characteristic parameter combination and the IOPS; When it is detected that the correlation is greater than or equal to the preset threshold, the second target feature parameter combination is output.

[0039] Optionally, after the step of determining the correlation between the second target characteristic parameter combination and the IOPS, the method includes: When it is detected that the correlation is less than the first preset threshold, the second target feature parameter combination is removed.

[0040] Optionally, performing preset dimension combination processing on different feature parameters based on the correlation and preset SISSO rules to obtain a target feature parameter combination includes: When it is detected that the correlation is greater than or equal to a first preset threshold, obtaining a first feature parameter set; The first feature parameter set is processed by preset dimension combination according to a preset SISSO rule to obtain a first target feature parameter combination.

[0041] It should be noted that, in the embodiment of the present invention, in order to obtain a plurality of characteristic parameters related to the performance of the RAID card chip, the characteristic parameters are combined using the SISSO method based on the compressed sensing theory.

[0042] Specifically, on the first aspect, after screening out several characteristic parameters that are highly correlated with the performance of the RAID card chip, the SISSO method based on compressed sensing theory is used to , and perform random nonlinear combinations, so that a large number of descriptors can be obtained, and the set they constitute is called feature space. After combining the initial feature parameters, as the number of iterations increases, the feature space will show recursive growth, generally reaching the order of 1010.

[0043] On the other hand, after screening out several feature parameters with low correlation with the performance of the RAID card chip, the SISSO method based on compressed sensing theory is used to perform random nonlinear combinations to obtain a large number of descriptors, and screen out several feature parameters that are highly correlated with the performance of the RAID card chip, and discard the descriptors with low correlation. In order to reduce the dimension of the feature space, SISSO uses the Sure Independence Screening (SIS) method to screen out descriptors related to the IOPS performance of the RAID card chip, and then form a feature subspace. Subsequently, SISSO uses the Sparsifying Operator (SO) to select Minimize or The norm minimization algorithm further searches for the optimal Dimension descriptor.

[0044] in, Figure 6 The workflow of SISSO (Sure Independence Screening and Sparsifying Operator, which finds the most important few features that describe the properties or functions of the target from a huge feature space) is given. It can process huge feature spaces, and the optimal descriptor determined by SISSO is an explicit function of the input feature parameters. Specifically, by screening key subspaces (such as S 1D , S 2D , S nD ), and generates one-dimensional, two-dimensional to n-dimensional descriptors. SISSO combines Sure Independence Screening (SIS, a statistical method for high-dimensional data feature selection) and Sparsifying Operator (SO, a sparsifying operator, used to introduce sparsity in the model) to find the most important few features that describe the properties or functions of the target from a huge feature space. It can be widely used in data-driven problems such as regression, classification, and multi-task learning.

[0045] Step 104, training a preset neural network model by using the target feature parameter combination to obtain a target neural network model; Optionally, the training of a preset neural network model by the target feature parameter combination to obtain a target neural network model includes: Debug the hyperparameters corresponding to the preset neural network model to obtain the target hyperparameters; The preset neural network model is iteratively trained based on the target hyperparameter, the target feature parameter combination and a preset training data set until the preset neural network module reaches a preset number of training times to obtain a target neural network model.

[0046] A neural network model for testing the performance of RAID card chips was constructed. In order to obtain the optimal neural network model, the hyperparameters (including the number of hidden layers and neurons, the size of batchsize, and the selection of optimizer, etc.) were properly debugged, and the training data set was used to iteratively train the neural network model until the number of training times reached the optimal neural network model. The neural network model includes a series of interconnected neurons, and a single neuron mainly has the following five components (input, weight and threshold, summation, activation function, and output), such as Figure 7 As shown, the input data is received, weighted by weights, the sum of weighted inputs is calculated by summing, nonlinear transformation is introduced by activation function, and output data is finally generated. First, the input, assuming that the input of the neuron is: , it can be recorded as an n-dimensional column vector X: , , the neural network mainly consists of two parts.

[0047] The first part is the forward propagation of the signal. Specifically, for the input layer, the input of the jth node is (j = 1,2, …, M); the connection weights between the hidden layer and the output layer are available express, is the threshold in the hidden layer, is the transfer function; for the output layer, (i = 1, 2, …, q) represents the connection weights between the output layer and the hidden layer, (k =1, 2, …, L) is the threshold of the kth node, is the transfer function, is the output of the kth node. The input of the i-th node in the hidden layer can be expressed as neti: , the signal output of the i-th node in the hidden layer is available express: , the signal input of the kth node in the output layer is available express: , the signal output of the kth node in the output layer is available express: .

[0048] The second part is the back propagation of the error, specifically, Figure 8 As shown, the input layer, the input of the jth node is , the signal output of the kth node in the output layer is , the target value of the kth node in the output layer can be represented by Tk, then the error function of the pth sample is: For the output layer, the weight correction can be used Indicates that the threshold correction amount is available For the hidden layer, represents the weight correction amount, represents the threshold correction amount, is the learning rate. First, the calculation error of the neural network is fed back to each hidden layer by the output layer, and the weights and thresholds in each layer are adjusted using the gradient descent algorithm.

[0049] Step 105: predict the performance of the storage device in the server in the current state through the target neural network model, and output the predicted value of the storage device performance.

[0050] Furthermore, the storage device includes a RAID card chip; the target neural network model is used to predict the performance of the storage device in the server in the current state, and the output storage device performance prediction value includes: The performance data related to the RAID card chip in the server in the current state is input into the target neural network model, and the predicted value of the storage device performance is output.

[0051] The performance data related to the RAID card chip in the server in the current state is input into a neural network model for predicting the performance of the RAID card chip, so as to quickly predict the performance of the RAID card chip in the server in the current state.

[0052] Optionally, after the step of predicting the performance of the storage device in the server in the current state by using the target neural network model and outputting the predicted value of the storage device performance, the method includes: Determine whether the IOPS performance of the storage device meets a preset condition based on the predicted value of the storage device performance; If so, continue to predict the performance of the storage device through the target neural network model.

[0053] Optionally, after the step of judging whether the IOPS performance of the storage device meets a preset condition based on the predicted value of the storage device performance, the method includes: If not, the initial monitoring time is adjusted based on the predicted value of the storage device performance, and the IOPS test data set corresponding to the storage device is reacquired.

[0054] Optionally, the adjusting the initial monitoring time based on the predicted value of the storage device performance includes: When it is monitored that the predicted value of the storage device performance is less than a second preset threshold, the initial monitoring time is shortened according to a preset time rule to obtain a first monitoring time, and the obtained test data is added to the test data set, the neural network model is retrained, and the new neural network model is used to predict the performance of the storage device; When it is monitored that the predicted value of the storage device performance is greater than or equal to a second preset threshold, the monitoring time is increased according to the preset time rule, and the test data set is added to the original sample, the model is continued to be trained, and the new neural network model is used to predict the performance of the storage device performance.

[0055] It should be noted that in an embodiment of the present invention, when the device system performs a performance test on the RAID card chip, the monitoring time is dynamically adjusted according to the test results (i.e., the size of IOPS). For example, when it is detected that the IOPS is small, the monitoring time is shortened, and the obtained test data is added to the test data set, the neural network model is retrained, and the performance of the RAID card chip is predicted using the new neural network model. When the IOPS is large, the monitoring time is increased, and the test data set is also added to the original sample, the model is continued to be trained, and the performance of the RAID card chip is predicted using the new neural network model. In this way, the performance of the RAID card chip can be understood in real time and the hard disk stocking cost can be effectively reduced, the risk of data loss is greatly reduced, and a reliable data basis for hard disk maintenance is provided.

[0056] Optionally, after the step of predicting the performance of the storage device in the server in the current state by using the target neural network model and outputting the predicted value of the storage device performance, the method includes: Obtaining actual performance value of the storage device; Performing mean absolute error processing based on the actual value of the storage device performance and the predicted value of the storage device performance to obtain a first target difference; The target neural network model is optimized based on the first target difference.

[0057] Optionally, after the step of predicting the performance of the storage device in the server in the current state by using the target neural network model and outputting the predicted value of the storage device performance, the method includes: Obtaining actual performance value of the storage device; Performing root mean square error processing based on the actual value of the storage device performance and the predicted value of the storage device performance to obtain a second target difference value; The target neural network model is optimized based on the second target difference.

[0058] It should be noted that, in the embodiment of the present invention, the mean absolute error (MAE) is a statistic that can be used to measure the distance between the predicted value and the actual value. Generally, the smaller the MAE value, the higher the accuracy of the ANN model (Artificial Neural Network) in predicting the actual RAID card chip performance. The root mean square error (RMSE) is the square root of the ratio of the square of the deviation between the predicted value and the true value to the number of observations n. RMSE measures the deviation between the predicted value and the true value, and is more sensitive to abnormal values ​​in the data. When performing performance testing, the test device for the performance of the RAID card chip provided above is only illustrated by the division of the above-mentioned program modules. In actual applications, the above-mentioned processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the above-mentioned processing.

[0059] The storage device performance test method provided in the embodiment of the present invention includes: obtaining characteristic parameters corresponding to the performance of the storage device; determining the correlation between the characteristic parameters and the IOPS of the storage device; performing preset dimension combination processing on different characteristic parameters based on the correlation and the preset SISSO rule to obtain a target characteristic parameter combination; training a preset neural network model through the target characteristic parameter combination to obtain a target neural network model; predicting the performance of the storage device in the server in the current state through the target neural network model, and outputting the predicted value of the storage device performance. In the embodiment of the present invention, by using the neural network model and the SISSO characteristic parameter screening method, the key characteristic parameter factors affecting the performance of the RAID card chip are obtained, and then the performance of the RAID card chip can be further tested, and the performance of the RAID card chip can be optimized to the greatest extent, and the labor cost can be further reduced. In addition, by predicting the performance of the RAID card chip based on the characteristic parameters with high correlation, the maximum performance of the RAID card chip under a certain capacity, a certain RAID scenario, and the surrounding environment can be obtained.

[0060] Reference Figure 2 , shows a schematic diagram of the structure of a backup power device provided by an embodiment of the present invention, which is applied to a baseboard management controller in an interconnection switching board in a memory resource integrated machine, wherein the memory resource integrated machine includes the interconnection switching board, a power supply module and a backup power module, and the device includes: An acquisition module 201 is used to acquire characteristic parameters corresponding to the performance of the storage device; A determination module 202, configured to determine a correlation between the characteristic parameter and the IOPS of the storage device; A combination module 203 is used to perform preset dimension combination processing on different feature parameters based on the correlation and the preset SISSO rule to obtain a target feature parameter combination; A training module 204 is used to train a preset neural network model by using the target feature parameter combination to obtain a target neural network model; The prediction module 205 is used to predict the performance of the storage device in the server in the current state through the target neural network model and output the predicted value of the storage device performance.

[0061] The storage device performance testing device provided in the embodiment of the present invention obtains characteristic parameters corresponding to the performance of the storage device; determines the correlation between the characteristic parameters and the IOPS of the storage device; performs preset dimension combination processing on different characteristic parameters based on the correlation and the preset SISSO rule to obtain a target characteristic parameter combination; trains a preset neural network model through the target characteristic parameter combination to obtain a target neural network model; predicts the performance of the storage device in the server in the current state through the target neural network model, and outputs a predicted value of the storage device performance. In the embodiment of the present invention, by using the neural network model and the SISSO characteristic parameter screening method, the key characteristic parameter factors affecting the performance of the RAID card chip are obtained, and then the performance of the RAID card chip can be further tested, so that the performance of the RAID card chip can be optimized to the greatest extent, and the labor cost can be further reduced. In addition, by predicting the performance of the RAID card chip based on the characteristic parameters with high correlation, the maximum performance of the RAID card chip under a certain capacity, a certain RAID scenario, and the surrounding environment can be obtained.

[0062] The embodiment of the present invention also provides a communication device, such as Figure 3 As shown, it includes a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304. Memory 303, used for storing computer programs; The processor 301, when used to execute the program stored in the memory 303, can implement the following steps: Acquire characteristic parameters corresponding to the performance of the storage device; Determining the correlation between the characteristic parameter and the IOPS of the storage device; Based on the correlation and the preset SISSO rule, the different feature parameters are processed in a preset dimension combination to obtain a target feature parameter combination; The preset neural network model is trained by the target feature parameter combination to obtain a target neural network model; The target neural network model is used to predict the performance of the storage device in the server in the current state, and the storage device performance test value is output.

[0063] Among them, the memory and the processor are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits together, which are all well known in the art, so this article will not further describe them. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor can be transmitted through a wired medium or transmitted on a wireless medium through an antenna. Further, the antenna also receives data and transmits the data to the processor. The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. The memory can be used to store data used by the processor when performing operations.

[0064] The communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0065] The communication interface is used for communication between the above terminal and other devices.

[0066] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0067] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0068] In another embodiment of the present invention, a computer-readable storage medium is provided, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the storage device performance testing method described in any of the above embodiments.

[0069] In another embodiment of the present invention, a computer program product including instructions is provided. When the computer program product is run on a computer, the computer executes the storage device performance testing method described in any one of the above embodiments.

[0070] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0071] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0072] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A storage device performance testing method, characterized in that: The method comprises: Acquire characteristic parameters corresponding to the performance of the storage device; Determining the correlation between the characteristic parameter and the IOPS of the storage device; Based on the correlation and the preset SISSO rule, the different feature parameters are processed in a preset dimension combination to obtain a target feature parameter combination; The preset neural network model is trained by the target feature parameter combination to obtain a target neural network model; The target neural network model is used to predict the performance of the storage device in the server in the current state, and a predicted value of the storage device performance is output.

2. The method according to claim 1, characterized in that The preset dimension combination processing is performed on different feature parameters based on the correlation and the preset SISSO rule to obtain the target feature parameter combination, which includes: Filtering out descriptors related to the IOPS of the storage device from the characteristic parameters based on determining independent filtering rules; forming a feature subspace based on the descriptors; Obtaining a target descriptor from the feature subspace based on a sparse operator rule and a preset canonical algorithm; A preset dimension combination process is performed based on the target descriptor to obtain a target feature parameter combination.

3. The method according to claim 1, characterized in that: The preset dimension combination processing is performed on different feature parameters based on the correlation and the preset SISSO rule to obtain the target feature parameter combination, which includes: When it is detected that the correlation is less than a first preset threshold, obtaining a second characteristic parameter set; Performing preset dimension combination processing on the second feature parameter set by a preset SISSO rule to obtain a second target feature parameter combination; Determine the correlation between the second target characteristic parameter combination and the IOPS; When it is detected that the correlation is greater than or equal to the preset threshold, the second target feature parameter combination is output.

4. The method according to claim 3, characterized in that After the step of determining the correlation between the second target characteristic parameter combination and the IOPS, the method includes: When it is detected that the correlation is less than the first preset threshold, the second target feature parameter combination is removed.

5. The method according to claim 1, characterized in that The preset dimension combination processing is performed on different feature parameters based on the correlation and the preset SISSO rule to obtain the target feature parameter combination, which includes: When it is detected that the correlation is greater than or equal to a first preset threshold, obtaining a first feature parameter set; The first feature parameter set is processed by preset dimension combination according to a preset SISSO rule to obtain a first target feature parameter combination.

6. The method according to claim 1, characterized in that The storage device includes a RAID card chip; the target neural network model is used to predict the performance of the storage device in the server in the current state, and the output storage device performance prediction value includes: The performance data related to the RAID card chip in the server in the current state is input into the target neural network model, and the predicted value of the storage device performance is output.

7. The method according to claim 6, characterized in that Before the step of obtaining characteristic parameters corresponding to the performance of the storage device, the method includes: Obtain the IOPS test data set corresponding to the storage device; The acquiring of characteristic parameters corresponding to the performance of the storage device comprises: A characteristic parameter corresponding to the performance of the storage device is obtained based on a pre-acquired IOPS test data set corresponding to the storage device.

8. The method according to claim 7, characterized in that The characteristic parameters corresponding to the storage device performance include at least one of RAID level, number of disks, stripe size, number of logical units, number of storage output links, disk type, RAID controller information, disk capacity, RAID group status, cache configuration, RAID rebuild time, RAID stripe depth, RAID write penalty, number of disk failures in the RAID group, and load balancing strategy of the RAID group.

9. The method according to claim 7, characterized in that: The step of obtaining an IOPS test data set corresponding to the storage device includes: In response to a first input on the human-computer interaction interface, obtaining a test parameter and IOPS for a storage device in the server; An IOPS test data set corresponding to the storage device is generated based on the test parameters and the IOPS.

10. The method according to claim 9, characterized in that Before the step of obtaining test parameters and IOPS for the storage device in the server in response to the first input on the human-computer interaction interface, the method includes: Create a test environment corresponding to the storage device.

11. The method according to claim 1, characterized in that: The training of the preset neural network model by the target feature parameter combination to obtain the target neural network model comprises: Debug the hyperparameters corresponding to the preset neural network model to obtain the target hyperparameters; The preset neural network model is iteratively trained based on the target hyperparameter, the target feature parameter combination and a preset training data set until the preset neural network module reaches a preset number of training times to obtain a target neural network model.

12. The method according to claim 1, characterized in that After the step of predicting the performance of the storage device in the server in the current state by the target neural network model and outputting the predicted value of the storage device performance, the method includes: Determine whether the IOPS performance of the storage device meets a preset condition based on the predicted value of the storage device performance; If so, continue to predict the performance of the storage device through the target neural network model.

13. The method according to claim 10, characterized in that After the step of judging whether the IOPS performance of the storage device meets a preset condition based on the predicted value of the storage device performance, the method includes: If not, the initial monitoring time is adjusted based on the predicted value of the storage device performance, and the IOPS test data set corresponding to the storage device is reacquired.

14. The method according to claim 13, characterized in that The adjusting process of the initial monitoring time based on the predicted value of the storage device performance includes: When it is monitored that the predicted value of the storage device performance is less than a second preset threshold, the initial monitoring time is shortened according to a preset time rule to obtain a first monitoring time, and the obtained test data is added to the test data set, the neural network model is retrained, and the new neural network model is used to predict the performance of the storage device; When it is monitored that the predicted value of the storage device performance is greater than or equal to a second preset threshold, the initial monitoring time is increased according to the preset time rule, and the test data set is added to the original sample, the model is continued to be trained, and the new neural network model is used to predict the performance of the storage device performance.

15. The method according to claim 1, characterized in that After the step of predicting the performance of the storage device in the server in the current state by the target neural network model and outputting the predicted value of the storage device performance, the method includes: Obtaining actual performance value of the storage device; Performing mean absolute error processing based on the actual value of the storage device performance and the predicted value of the storage device performance to obtain a first target difference; The target neural network model is optimized based on the first target difference.

16. The method according to claim 1, characterized in that After the step of predicting the performance of the storage device in the server in the current state by the target neural network model and outputting the predicted value of the storage device performance, the method includes: Obtaining actual performance value of the storage device; Performing root mean square error processing based on the actual value of the storage device performance and the predicted value of the storage device performance to obtain a second target difference value; The target neural network model is optimized based on the second target difference.

17. A storage device performance testing device, characterized in that: The device comprises: An acquisition module, used to acquire characteristic parameters corresponding to the performance of the storage device; A determination module, used to determine the correlation between the characteristic parameter and the IOPS of the storage device; A combination module, used for performing preset dimension combination processing on different feature parameters based on the correlation and the preset SISSO rule to obtain a target feature parameter combination; A training module, used to train a preset neural network model through the target feature parameter combination to obtain a target neural network model; The prediction module is used to predict the performance of the storage device in the server under the current state through the target neural network model and output the predicted value of the storage device performance.

18. A communication device, characterized in that: include: A transceiver, a memory, a processor, and a program stored on the memory and executable on the processor; The processor is used to read the program in the memory to implement the storage device performance testing method as described in any one of claims 13-16.

19. A readable storage medium for storing a program, characterized in that: When the program is executed by a processor, the storage device performance testing method as described in any one of claims 1 to 16 is implemented.

20. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, a storage device performance testing method as described in any one of claims 1-16 is implemented.

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