Slow disk identification method, device, electronic device and storage device

Abstract

The present disclosure provides a slow disk detection method, device, electronic device and storage medium, which belongs to the field of storage technology. The method comprises: determining the slow disk identification parameters of the hard disk according to at least one of the abnormal response frequency or abnormal response frequency deviation data; and identifying the hard disk according to the slow disk identification parameters. The present disclosure eliminates the delay influence of other software and hardware nodes on the I / O link by performing statistical analysis on the response time of the hard disk, and can timely discover the slow disk with high delay risk in the cluster, shorten the response time, and improve the storage and reading efficiency. In addition, the delayed response of the hard disk is statistically analyzed from a statistical perspective, and the hard disks screened out are hard disks with long-term performance abnormalities, which reduces the misjudgment of the hard disk due to occasional delays, improves the accuracy of detection, reduces the rate of incorrect disk replacement and the rate of secondary failure, and saves labor costs and material costs.

Description

Technical Field

[0001] The present disclosure relates to the field of storage technology, and in particular to a slow disk identification method, device, electronic device, and storage medium. Background Art

[0002] To meet business processing needs, electronic devices are often equipped with hard drives. Due to factors such as head degradation and external vibration, hard drives can experience long I / O (Input / Output) response times, making them slow. As a data storage medium, hard drive performance directly impacts the overall performance of electronic devices. Therefore, identifying slow drives in electronic devices is crucial.

[0003] Currently, related technologies mainly use detection tools to identify slow disks in electronic devices. During the identification process, I / O tracing tools such as blktrace are built to obtain the response time of the hardware devices on the I / O link. When the response time of the hardware device exceeds the threshold, the hard disk is identified as a slow disk.

[0004] However, the hardware devices on the I / O link include hardware drivers, disk arrays, expander backplanes, hard disks and other devices. The response time of this hardware device is the total response time of the above-mentioned devices, which cannot accurately reflect the response delay of the hard disk itself. In addition, considering the complexity of the operating environment, a certain response time cannot reflect the performance of the hard disk itself. Therefore, the accuracy of slow disk identification by related technologies is low. Summary of the Invention

[0005] The present disclosure provides a hard disk identification method, device, electronic device, and storage medium, which can improve the accuracy of slow disk identification. The technical solution is as follows:

[0006] In one aspect, a method for identifying a slow disk is provided, the method comprising:

[0007] Determining an abnormal response frequency of the hard disk according to the response time and power-on time of the hard disk;

[0008] determining abnormal response frequency deviation data of the hard disk according to the abnormal response frequency and the average abnormal response frequency, wherein the average abnormal response frequency is determined based on the abnormal response frequencies of multiple hard disks in the electronic device, and the abnormal response frequency deviation data is used to represent the difference between the abnormal response frequency and the average abnormal response frequency;

[0009] determining a slow disk identification parameter of the hard disk according to at least one of the abnormal response frequency or the abnormal response frequency deviation data, wherein the slow disk identification parameter is used to indicate a possibility that the hard disk is a slow disk;

[0010] Perform slow disk identification on the hard disk according to the slow disk identification parameter.

[0011] In another aspect, a slow disk identification device is provided, comprising:

[0012] A first determining module is configured to determine an abnormal response frequency of the hard disk according to a response time and a power-on time of the hard disk;

[0013] a second determining module, configured to determine abnormal response frequency deviation data of the hard disk based on the abnormal response frequency and an average abnormal response frequency, wherein the average abnormal response frequency is determined based on abnormal response frequencies of multiple hard disks in the electronic device, and the abnormal response frequency deviation data is used to represent a difference between the abnormal response frequency and the average abnormal response frequency;

[0014] a third determining module, configured to determine a slow disk identification parameter of the hard disk according to at least one of the abnormal response frequency or the abnormal response frequency deviation data, wherein the slow disk identification parameter is used to indicate a possibility that the hard disk is a slow disk;

[0015] The identification module is used to identify the hard disk as a slow disk according to the slow disk identification parameters.

[0016] On the other hand, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the slow disk identification method as described in the first aspect.

[0017] On the other hand, a computer-readable storage medium is provided, in which at least one program code is stored. The at least one program code is loaded and executed by a processor to implement the slow disk identification method as described in the first aspect.

[0018] On the other hand, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the method provided in various optional implementations of the above-mentioned aspect.

[0019] The technical solutions provided by the embodiments of the present disclosure have the following beneficial effects:

[0020] By statistically analyzing hard drive response times, we eliminate the impact of latency from other hardware and software nodes on the I / O link, enabling timely identification of slow drives with high latency risks within the cluster, shortening response times and improving storage and read efficiency. Furthermore, by applying a statistical analysis of hard drive latency responses, we identify drives with chronic performance anomalies, reducing the risk of misidentification due to occasional latency, improving detection accuracy, and lowering the rate of incorrect drive replacements and secondary failures, saving both labor and material costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 This is a schematic diagram of an instruction transfer process at a block input and output layer provided by an embodiment of the present disclosure;

[0023] Figure 2 This is a flow chart of a slow disk identification method provided by an embodiment of the present disclosure;

[0024] Figure 3 is a flow chart of another slow disk identification method provided by an embodiment of the present disclosure;

[0025] Figure 4 is a distribution diagram of a slow disk likelihood estimation parameter provided by an embodiment of the present disclosure;

[0026] Figure 5 This is a timing diagram of slow disk identification provided by an embodiment of the present disclosure;

[0027] Figure 6 This is a schematic structural diagram of a slow disk identification device provided by an embodiment of the present disclosure;

[0028] Figure 7 A structural block diagram of a terminal provided by an exemplary embodiment of the present disclosure is shown;

[0029] Figure 8 The present invention is a server for slow disk identification according to an exemplary embodiment. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0031] Before implementing the embodiments of the present disclosure, the terms involved in the embodiments of the present disclosure are first explained.

[0032] A slow disk is a hard disk with a longer I / O response time due to reasons such as head degradation, external vibration, and environmental problems.

[0033] Abnormal response frequency refers to the number of abnormal responses of the hard disk in a unit of time.

[0034] The abnormal response frequency deviation data represents a difference between the abnormal response frequency of the hard disk and an average abnormal response frequency, which is an average of the abnormal response frequencies of multiple hard disks in the electronic device.

[0035] The slow disk identification parameter indicates the possibility that the hard disk is a slow disk, and the value range of the slow disk identification parameter is [0, 1].

[0036] The slow disk likelihood estimation parameter represents the relationship between the likelihood that the hard disk is a slow disk and the likelihood that the hard disk is a non-slow disk. The slow disk likelihood estimation parameter is reflected by the estimation result of the likelihood estimation of the hard disk.

[0037] With the development of computer, cloud storage and cloud computing technologies, the demand for storage performance is getting higher and higher. In a storage array system, data is stored in parallel on each hard disk. If a hard disk becomes slow, it will drag down the response speed of the entire system, and in serious cases, cause business interruption and affect the performance of electronic equipment. In addition, see Figure 1 In the storage array system, the I / O requests sent by users will pass through multiple hardware and software nodes such as the file system, page high buffer, request queue, virtual device, etc. These nodes may affect the acquisition of hard disk response time, thereby affecting the accuracy of slow disk identification results.

[0038] In order to improve the accuracy of slow disk identification results, increase the response speed of the system, and ensure the smooth operation of business, the embodiment of the present disclosure provides a slow disk identification method, which performs hard disk performance monitoring and slow disk screening based on the underlying hard disk I / O response time. By recording the number of I / O delays exceeding the threshold value inside the hard disk, statistical distribution and log-likelihood analysis are performed on each hard disk in the electronic device, and low-performance discrete hard disks in the electronic device are screened out, thereby monitoring and intercepting slow disks that affect business performance in advance. The embodiment of the present disclosure directly performs statistical analysis on the response time of the hard disk itself, eliminating the influence of other nodes and software in the I / O topology link, and performs horizontal comparison and statistical analysis based on the delay of each hard disk in the electronic device, so as to accurately complete the monitoring and screening of the underlying high-latency slow disk.

[0039] The technologies involved in the embodiments of the present disclosure are introduced.

[0040] Cloud technology refers to a managed technology that unifies hardware, software, and network resources within a wide or local area network (WAN) to enable data computing, storage, processing, and sharing. Cloud technology is a general term for network, information technology, integration technology, management platform technology, and application technology based on the cloud computing business model. It can form a resource pool for on-demand, flexible and convenient use. Cloud computing technology will become a crucial support. Backend services for technical network systems, such as video websites, image websites, and more portals, require extensive computing and storage resources. With the rapid development and application of the internet industry, every item will likely have its own unique identifier and will need to be transmitted to backend systems for logical processing. Different levels of data will be processed separately, and data from various industries will require a strong system backend, which can only be achieved through cloud computing.

[0041] Cloud storage is a new concept that has been extended and developed from the concept of cloud computing. A distributed cloud storage system (hereinafter referred to as storage system) refers to a storage system that uses cluster applications, grid technology, and distributed storage file systems to bring together a large number of different types of storage devices (storage devices are also called storage nodes) in the network through application software or application interfaces to work together and provide external data storage and business access functions.

[0042] Currently, storage systems utilize a method for creating logical volumes. When creating a logical volume, physical storage space is allocated for each logical volume. This physical storage space may consist of disks on a specific storage device or several storage devices. When a client stores data on a logical volume, it stores the data on a file system. The file system divides the data into multiple parts, each of which is an object. An object contains not only the data but also additional information such as the data identifier (ID) of the data entity. The file system writes each object to the physical storage space of the logical volume and records the storage location information of each object. Therefore, when a client requests access to data, the file system can provide access to the data based on the storage location information of each object.

[0043] The storage system allocates physical storage space to logical volumes by pre-dividing the physical storage space into stripes based on the estimated capacity of the objects to be stored in the logical volume (this estimate often has a large margin relative to the actual capacity of the objects to be stored) and the Redundant Array of Independent Disks (RAID) groupings. A logical volume can be understood as a stripe, thereby allocating physical storage space to the logical volume.

[0044] The disclosed embodiment provides a slow disk identification method, which can be executed by an electronic device, wherein the electronic device is provided with one or more hard disks for storing data required for business operation, and can provide computing, storage and other aspects of support for business execution. The electronic device can be a terminal or a server. When the electronic device is a terminal, the electronic device can be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited to this. When the electronic device is a server, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms.

[0045] It should be understood that the terms "each," "plurality," and "any" used in the embodiments of the present disclosure include two or more, each refers to each of the corresponding plurality, and any refers to any one of the corresponding plurality. For example, if a plurality of words includes 10 words, each word refers to each of the 10 words, and any word refers to any one of the 10 words.

[0046] The present disclosure provides a method for identifying a slow disk. Figure 2 , the method process provided by the embodiment of the present disclosure includes:

[0047] 201. Determine an abnormal response frequency of the hard disk based on the hard disk's response time and power-on time.

[0048] 202. Determine abnormal response frequency deviation data of the hard disk based on the abnormal response frequency and the average abnormal response frequency.

[0049] The average abnormal response frequency is determined according to the abnormal response frequencies of multiple hard disks in the electronic device, and the abnormal response frequency deviation data is used to represent the difference between the abnormal response frequency and the average abnormal response frequency.

[0050] 203. Determine a slow disk identification parameter of the hard disk according to at least one of the abnormal response frequency data and the abnormal response frequency deviation data.

[0051] The slow disk identification parameter is used to indicate the possibility that the hard disk is a slow disk.

[0052] 204. Perform slow disk identification on the hard disk according to the slow disk identification parameters.

[0053] The method provided by the disclosed embodiments, through statistical analysis of hard drive response times, eliminates the impact of delays from other hardware and software nodes on the I / O link, enabling timely identification of slow disks with high latency risks within the cluster, shortening response times and improving storage and read efficiency. Furthermore, statistical analysis of hard drive latency responses is performed from a statistical perspective, screening out hard drives with chronic performance anomalies. This reduces the risk of misjudging hard drives due to occasional delays, improves detection accuracy, reduces the rate of incorrect disk replacements and secondary failures, and saves both labor and material costs.

[0054] In another embodiment of the present disclosure, determining the abnormal response frequency of the hard disk according to the response time and power-on time of the hard disk includes:

[0055] Obtaining, from the hard disk response time, the number of responses in which the response time exceeds a first threshold;

[0056] The ratio of the number of responses to the power-on time is determined as the abnormal response frequency.

[0057] In another embodiment of the present disclosure, determining abnormal response frequency deviation data of a hard disk according to the abnormal response frequency and the average abnormal response frequency includes:

[0058] Obtaining a first difference between the abnormal response frequency and the average abnormal response frequency;

[0059] The ratio of the first difference to the abnormal response frequency standard deviation is determined as the abnormal response frequency deviation data of the hard disk. The abnormal response frequency standard deviation is determined according to the abnormal response frequencies of multiple hard disks in the electronic device.

[0060] In another embodiment of the present disclosure, determining a slow disk identification parameter of a hard disk according to at least one of abnormal response frequency data or abnormal response frequency deviation data includes:

[0061] In response to the abnormal response frequency being less than the average abnormal response frequency, determining that a slow disk identification parameter of the hard disk is a first value;

[0062] In response to the abnormal response frequency being greater than the average abnormal response frequency, maximum abnormal response deviation data in the electronic device is determined, and a slow disk identification parameter of the hard disk is determined according to the abnormal response frequency deviation data and the maximum abnormal response deviation data.

[0063] In another embodiment of the present disclosure, determining maximum abnormal response deviation data in an electronic device includes:

[0064] Obtaining a maximum abnormal response frequency from abnormal response frequencies of multiple hard disks in the electronic device;

[0065] obtaining a second difference between the maximum abnormal response frequency and the average abnormal response frequency;

[0066] The ratio of the second difference to the abnormal response frequency standard deviation is determined as the maximum abnormal response deviation data, and the abnormal response frequency standard deviation is determined according to the abnormal response frequencies of multiple hard disks in the electronic device.

[0067] In another embodiment of the present disclosure, performing slow disk identification on a hard disk according to slow disk identification parameters includes:

[0068] In response to the slow disk identification parameter being greater than the second threshold, the hard disk is identified as a slow disk.

[0069] Perform slow disk identification on the hard disk based on the slow disk identification parameters, including:

[0070] Determine a slow disk likelihood estimation parameter of the hard disk according to the slow disk identification parameter. The slow disk likelihood estimation parameter is used to represent the relationship between the likelihood that the hard disk is a slow disk and the likelihood that the hard disk is a non-slow disk.

[0071] The hard disk is identified as slow disk based on the slow disk likelihood estimation parameters.

[0072] In another embodiment of the present disclosure, determining a slow disk likelihood estimation parameter of a hard disk according to the slow disk identification parameter includes:

[0073] In response to the slow disk identification parameter being a first value, determining that the slow disk likelihood estimation parameter is negative infinity;

[0074] In response to the slow disk identification parameter being a second value, determining the slow disk likelihood estimation parameter to be positive infinity;

[0075] In response to the slow disk identification parameter being greater than a first value and less than a second value, obtaining a non-slow disk identification parameter of the hard disk, and determining a slow disk likelihood estimation parameter of the hard disk based on the slow disk identification parameter and the non-slow disk identification parameter, wherein the non-slow disk identification parameter is determined based on the slow disk identification parameter;

[0076] The first value is smaller than the second value.

[0077] In another embodiment of the present disclosure, performing slow disk identification on a hard disk according to a slow disk likelihood estimation parameter includes:

[0078] In response to the slow disk likelihood estimation parameter being greater than a third threshold, the hard disk is identified as a slow disk.

[0079] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present disclosure, and will not be described in detail here.

[0080] The present disclosure provides a method for identifying a slow disk. Take an electronic device executing the present disclosure as an example. Figure 3 , the method process provided by the embodiment of the present disclosure includes:

[0081] 301. The electronic device obtains the response time of the hard disk.

[0082] During the operation of electronic devices, they store various data generated during operation in a hard disk log. This data includes hard disk response time, hard disk response times, operation type, power-on time, and so on. Power-on time refers to the time it takes for an electronic device to stabilize after power is turned on, and can be measured in hours. Operation types include read and write operations. Therefore, the hard disk response time includes both read and write response times.

[0083] Optionally, in order to save storage space and facilitate intuitive viewing of the response status of each hard disk, the electronic device can divide the response time into different intervals, and map each I / O response time of the hard disk to a different interval according to the divided intervals, and count the number of responses in each interval, and then store the response time of the hard disk according to the divided intervals and the number of responses.

[0084] See Table 1, which shows the hard disk log structure of a hard disk.

[0085] Table 1

[0086]

[0087]

[0088] Based on the data stored in the hard disk log, the electronic device can read the response time of each hard disk and identify each hard disk as a slow disk based on the response time of each hard disk.

[0089] 302. The electronic device determines an abnormal response frequency of the hard disk according to the response time and power-on time of the hard disk.

[0090] The abnormal response frequency refers to the number of abnormal responses of the hard disk in a unit of time. The abnormal response frequency can reflect the performance of the hard disk. The higher the abnormal response frequency, the longer the hard disk response time and the worse the hard disk performance; the lower the abnormal response frequency, the shorter the hard disk response time and the better the hard disk performance.

[0091] Based on the acquired response time, the electronic device can use the following method to determine the abnormal response frequency of the hard disk according to the hard disk response time and power-on time:

[0092] 3021. The electronic device obtains, from the response time of the hard disk, the number of responses in which the response time exceeds a first threshold.

[0093] The first threshold can be obtained based on the response time statistics of non-slow and slow disks. The first threshold can be 128ms (milliseconds), 265ms, etc. The disclosed embodiments do not impose a specific limit on the value of the first threshold. Generally, when the hard disk is functioning normally, the response time will be less than the first threshold, and when the hard disk is abnormal, the response time will be greater than the first threshold.

[0094] For any hard disk, the electronic device compares the response time of the hard disk with the first threshold each time. When any response time exceeds the first threshold, the electronic device adds 1 to the number of times the response time exceeds the first threshold. After all the response times of the hard disk are compared with the first threshold, the final number obtained is used as the number of responses when the response time exceeds the first threshold.

[0095] Optionally, if the response time of the hard disk is stored according to the divided intervals, the first threshold value may be the boundary point of the interval. When the electronic device obtains the number of responses whose response time exceeds the first threshold value, it may directly compare the first threshold value with the boundary point of the interval, and take the sum of the corresponding number of responses in the interval whose boundary point is greater than the first threshold value as the number of responses whose response time exceeds the first threshold value.

[0096] 3022. The electronic device determines the ratio of the number of responses to the power-on time as the abnormal response frequency.

[0097] Based on the acquired number of responses where the response time exceeds the first threshold and the power-on time of the electronic device, the electronic device calculates the ratio of the response number to the power-on time to obtain the abnormal response frequency of the hard disk. Assuming the number of responses where the response time exceeds the first threshold is represented by Ct, and the power-on time is represented by POH, the abnormal response frequency Ctavg = Ct / POH.

[0098] For example, referring to Table 1 above, if the first threshold is set to 256ms, the number of times the response time exceeds 256ms is 10 times, and the power-on time of the electronic device is 4879 hours, then the abnormal response frequency of the hard disk is 10 / 4879=0.002.

[0099] 303. The electronic device determines abnormal response frequency deviation data of the hard disk according to the abnormal response frequency and the average abnormal response frequency.

[0100] Among them, the abnormal response frequency deviation data is used to represent the difference between the abnormal response frequency of the hard disk and the average abnormal response frequency. The abnormal response frequency deviation data can be a Z score, etc. The average abnormal response frequency is the average value of the abnormal response frequencies of multiple hard disks in the electronic device, and is used to reflect the overall situation of the abnormal response frequencies of each hard disk in the cluster. The average abnormal response frequency can be obtained by obtaining the abnormal response frequencies of multiple hard disks in the electronic device and calculating the average value of the abnormal response frequencies of the multiple hard disks. The average value of the abnormal response frequency can be represented by μ. For example, the electronic device includes 5 hard disks, and the abnormal response frequencies of the 5 hard disks are Ctavg1, Ctavg2, Ctavg3, Ctavg4, and Ctavg5 respectively. Then the average abnormal response frequency of the electronic device = (Ctavg1+Ctavg2+Ctavg3+Ctavg4+Ctavg5) / 5.

[0101] When the electronic device determines the abnormal response frequency deviation data of the hard disk based on the abnormal response frequency and the average abnormal response frequency, the following method can be used:

[0102] 3031. The electronic device obtains a first difference between the abnormal response frequency and the average abnormal response frequency.

[0103] 3032. The electronic device determines the ratio of the first difference to the abnormal response frequency standard deviation as the abnormal response frequency deviation data of the hard disk.

[0104] The standard deviation of the abnormal response frequency is used to reflect the degree of dispersion of the abnormal response frequencies of each hard disk in the electronic device. The standard deviation of the abnormal response frequency can be obtained by calculating the square of the difference between the abnormal response frequency of each hard disk and the average abnormal response frequency to obtain the square of the mean deviation of multiple hard disks, and then calculating the arithmetic square root of the square of the mean deviation of multiple hard disks to obtain the standard deviation of the abnormal response frequency. The standard deviation of the abnormal response frequency can be expressed as σ, Wherein, N is the number of hard disks in the electronic device, i represents any hard disk, and ctavg(i) represents the abnormal response frequency of any hard disk.

[0105] The electronic device can calculate the abnormal response frequency deviation data Z(i) of the hard disk using the following formula based on the abnormal response frequency ctavg(i), the average abnormal response frequency μ, and the abnormal response frequency standard deviation σ:

[0106]

[0107] 304. The electronic device determines a slow disk identification parameter of the hard disk according to at least one of the abnormal response frequency data and the abnormal response frequency deviation data.

[0108] The slow disk identification parameter is used to indicate the possibility that the hard disk is a slow disk. The slow disk identification parameter can be represented by ρ, and the value range of the slow disk identification parameter is [0, 1].

[0109] When the electronic device determines the slow disk identification parameter of the hard disk based on at least one of the abnormal response frequency or the abnormal response frequency deviation data, it can be determined based on the abnormal response frequency, or based on the abnormal response frequency deviation data, or based on the abnormal response frequency and the abnormal response frequency deviation data.

[0110] Before determining the slow disk identification parameter of the hard disk based on at least one of the abnormal response frequency or abnormal response frequency deviation data, the electronic device may first compare the abnormal response frequency of the hard disk with the average abnormal response frequency, and perform a subsequent determination process based on the comparison result.

[0111] In one possible implementation, if the abnormal response frequency is less than the average abnormal response frequency, the electronic device determines that the slow disk identification parameter of the hard disk is a first value in response to the abnormal response frequency being less than the average abnormal response frequency. The first value is a value greater than or equal to 0 and less than or equal to 1. In the embodiment of the present disclosure, the first value is 0.

[0112] In another possible implementation, if the abnormal response frequency is greater than the average abnormal response frequency, in response to the abnormal response frequency being greater than the average abnormal response frequency, the electronic device determines maximum abnormal response deviation data in the electronic device, and determines a slow disk identification parameter for the hard disk based on the abnormal response frequency deviation data and the maximum abnormal response deviation data. Specifically, when determining the maximum abnormal response deviation data in the electronic device, the electronic device may obtain the maximum abnormal response frequency from the abnormal response frequencies of multiple hard disks in the electronic device, obtain a second difference between the maximum abnormal response frequency and the average abnormal response frequency, and then determine the maximum abnormal response deviation data as the ratio of the second difference to the standard deviation of the abnormal response frequency.

[0113] The electronic device can determine the maximum abnormal response deviation data Z(max) by applying the following formula based on the maximum abnormal response frequency ctavg(max), the average abnormal response frequency μ, and the standard deviation σ of the abnormal response frequency:

[0114]

[0115] When determining the hard drive's slow disk identification parameter based on the abnormal response frequency deviation data and the maximum abnormal response deviation data, the electronic device can obtain a third difference between the abnormal response frequency deviation data and the average abnormal response frequency, and a fourth difference between the maximum abnormal response deviation data and the average abnormal response frequency, and determine the hard drive's slow disk identification parameter as the ratio of the third difference to the fourth difference. This calculation process can be expressed as:

[0116] Combining the above two situations, the slow disk identification parameters are:

[0117]

[0118] The slow disk identification parameter in the embodiment of the present disclosure is a soft decision value for identifying a slow disk. The closer the slow disk identification parameter is to 1, the greater the possibility that the hard disk is a slow disk. The closer the slow disk identification parameter is to 0, the greater the possibility that the hard disk is not a slow disk.

[0119] 305. The electronic device performs slow disk identification on the hard disk according to the slow disk identification parameters.

[0120] In one embodiment of the present disclosure, based on the acquired slow disk identification parameter, the electronic device compares the slow disk identification parameter with a second threshold value and, based on the comparison result, identifies the hard disk as a slow disk. If the slow disk identification parameter is greater than the second threshold value, the electronic device determines the hard disk is a slow disk; if the slow disk identification parameter is less than the second threshold value, the electronic device determines the hard disk is not a slow disk. The second threshold value can be obtained by statistically analyzing data from a large number of hard disks; the first threshold value can be 0.7, 0.8, or the like.

[0121] In another embodiment of the present disclosure, when the electronic device performs slow disk identification on the hard disk according to the slow disk identification parameter, the following method may be used:

[0122] 3051. The electronic device determines a slow disk likelihood estimation parameter of the hard disk based on the slow disk identification parameter.

[0123] The slow disk likelihood estimation parameter represents the relationship between the likelihood of a hard disk being slow and the likelihood of a hard disk being non-slow. This relationship can be reflected by the likelihood estimation result of the hard disk. The slow disk likelihood estimation parameter can be represented by an LLR (Log Likely Ration).

[0124] When the electronic device determines the slow disk likelihood estimation parameters of the hard disk based on the slow disk identification parameters, the following situations may occur, but are not limited to:

[0125] In the first case, in response to the slow disk identification parameter being a first value, the electronic device determines that the slow disk likelihood estimation parameter is negative infinity.

[0126] In the embodiment of the present disclosure, the first value may be 0. When the slow disk identification parameter is 0, the probability that the hard disk is a slow disk is 0, and the probability that the hard disk is not a slow disk is 1. At this time, the electronic device may determine that the slow disk likelihood estimation parameter of the hard disk is negative infinity.

[0127] In the second case, in response to the slow disk identification parameter being a second value, the electronic device determines that the slow disk likelihood estimation parameter is positive infinity.

[0128] In the embodiment of the present disclosure, the second value may be 1. When the slow disk identification parameter is 1, the probability that the hard disk is a slow disk is 1, and the probability that the hard disk is not a slow disk is 0. At this time, the electronic device may determine that the slow disk likelihood estimation parameter of the hard disk is positive infinity.

[0129] In the third case, in response to the slow disk identification parameter being greater than the first value and less than the second value, the electronic device obtains the non-slow disk identification parameter of the hard disk, and determines the slow disk likelihood estimation parameter of the hard disk based on the slow disk identification parameter and the non-slow disk identification parameter.

[0130] Since the events of the hard disk being a slow disk or a non-slow disk are mutually exclusive events, when the hardware is a slow disk, the probability of the hard disk being a non-slow disk is 0; when the hard disk is a non-slow disk, the probability of the hard disk being a slow disk is 0. Therefore, when the above steps are used to determine the slow disk identification parameter of the hard disk, based on the mutual exclusivity of the events, the electronic device can determine that the non-slow disk identification parameter of the hard disk is 1-slow disk identification parameter, that is, the non-slow disk identification parameter is 1-ρ.

[0131] Based on the acquired non-slow disk identification parameters, the electronic device can calculate the ratio of the slow disk identification parameters to the non-slow disk identification parameters, and calculate the logarithm of the ratio to obtain a slow disk likelihood estimation parameter. The calculation process of the slow disk likelihood estimation parameter is as follows:

[0132]

[0133] The embodiment of the present disclosure performs a log-likelihood ratio (LLR) on ρ to achieve a quantitative guess of the soft decision ρ that the sampled hard disk is a slow disk, thereby obtaining an inverse mapping of the hard decision, and further accurately identifying the slow disk.

[0134] 3052. The electronic device identifies the hard disk as a slow disk based on the slow disk likelihood estimation parameter.

[0135] In response to the value of the slow disk likelihood estimation parameter being greater than a third threshold, the electronic device identifies the hard disk as a slow disk. The third threshold can be obtained by statistically analyzing data from a large number of hard disks, and can be 2, 3, 5, etc.

[0136] Furthermore, when the hard disk is identified as a slow disk, the electronic device will also send an alarm prompt message to the user to warn the user so that the user can replace the hard disk in time to ensure that the business can proceed smoothly.

[0137] Figure 4Figure 1 shows the distribution of the likelihood estimation parameter for slow disks in electronic devices. A positive likelihood estimation parameter represents the slow disk interval, while a negative likelihood estimation parameter represents the non-slow disk interval. The amplitude of the likelihood estimation parameter represents the confidence level. A higher amplitude indicates greater confidence, while a smaller amplitude indicates lower confidence. For example, an amplitude of infinity indicates that the hard drive is either slow or non-slow. An amplitude of 0 indicates a probability of 0.5 for both, making it difficult to accurately determine whether the hard drive is slow or non-slow. Based on the distribution characteristics of hardware in electronic devices, a third threshold is defined to perform a hard decision inverse mapping on the likelihood estimation parameter. This means that any hard drive whose likelihood estimation parameter exceeds the third threshold is identified as a slow drive, enabling early identification and screening.

[0138] It should be noted that the above example uses the identification of a single hard drive. The identification process for other hard drives can be found in the previous section and will not be further elaborated here. Furthermore, operations personnel can categorize the second and third hard drive thresholds according to the business's performance sensitivity. For businesses less sensitive to performance, the warning thresholds can be appropriately lowered to reduce spare parts inventory and manpower costs, thus saving costs. For businesses more sensitive to performance, the second and third thresholds can be appropriately raised to allow for the early replacement of slower drives, improving overall system performance and service quality.

[0139] For the above slow disk identification process, the following will be Figure 5 Take this as an example to illustrate.

[0140] See also Figure 5 For any hard drive, the number of responses in which the hard drive's response time exceeds a first threshold is obtained, and based on the number of responses, the ratio of the number of responses to the power-on time is determined as the hard drive's abnormal response frequency. Based on the abnormal response frequency, the hard drive's abnormal response deviation data can be obtained by calculating the average abnormal response frequency and the standard deviation of the abnormal response frequency within the calculation cluster. The slow disk identification parameter is then determined based on the abnormal response deviation data, and the slow disk likelihood estimation parameter LLR is then determined based on the slow disk identification parameter. If the LLR is greater than a third threshold, the hard drive is identified as a slow disk, and a slow disk alarm is issued for the hard disk. If the LLR is less than the third threshold, the next hard disk is polled and tested.

[0141] The method provided by the disclosed embodiments, through statistical analysis of hard drive response times, eliminates the impact of delays from other hardware and software nodes on the I / O link, enabling timely identification of slow disks with high latency risks within the cluster, shortening response times and improving storage and read efficiency. Furthermore, statistical analysis of hard drive latency responses is performed from a statistical perspective, screening out hard drives with chronic performance anomalies. This reduces the risk of misjudging hard drives due to occasional delays, improves detection accuracy, reduces the rate of incorrect disk replacements and secondary failures, and saves both labor and material costs.

[0142] See also Figure 6 The present disclosure provides a slow disk identification device, which includes:

[0143] The first determination module 601 is used to determine the abnormal response frequency of the hard disk according to the response time and power-on time of the hard disk;

[0144] A second determining module 602 determines abnormal response frequency deviation data of the hard disk based on the abnormal response frequency and the average abnormal response frequency, wherein the average abnormal response frequency is determined based on the abnormal response frequencies of multiple hard disks in the electronic device, and the abnormal response frequency deviation data is used to represent the difference between the abnormal response frequency and the average abnormal response frequency;

[0145] A third determining module 603 is configured to determine a slow disk identification parameter of the hard disk based on at least one of the abnormal response frequency data and the abnormal response frequency deviation data, where the slow disk identification parameter indicates the possibility that the hard disk is a slow disk;

[0146] The identification module 604 is configured to identify the hard disk as a slow disk according to the slow disk identification parameters.

[0147] In another embodiment of the present disclosure, the first determination module 601 is used to obtain the number of responses whose response time exceeds a first threshold value in the response time of the hard disk; and determine the ratio of the number of responses to the power-on time as the abnormal response frequency.

[0148] In another embodiment of the present disclosure, the second determination module 602 is used to obtain a first difference between the abnormal response frequency and the average abnormal response frequency; and determine the ratio of the first difference to the standard deviation of the abnormal response frequency as the abnormal response frequency deviation data of the hard disk, where the standard deviation of the abnormal response frequency is determined based on the abnormal response frequencies of multiple hard disks in the electronic device.

[0149] In another embodiment of the present disclosure, the third determining module 603 is configured to determine that the slow disk identification parameter of the hard disk is a first value in response to the abnormal response frequency being less than the average abnormal response frequency;

[0150] In response to the abnormal response frequency being greater than the average abnormal response frequency, maximum abnormal response deviation data in the electronic device is determined, and a slow disk identification parameter of the hard disk is determined according to the abnormal response frequency deviation data and the maximum abnormal response deviation data.

[0151] In another embodiment of the present disclosure, the third determination module 603 is used to obtain the maximum abnormal response frequency from the abnormal response frequencies of multiple hard disks in the electronic device; obtain a second difference between the maximum abnormal response frequency and the average abnormal response frequency; and determine the ratio of the second difference to the standard deviation of the abnormal response frequency as the maximum abnormal response deviation data, where the standard deviation of the abnormal response frequency is determined based on the abnormal response frequencies of multiple hard disks in the electronic device.

[0152] In another embodiment of the present disclosure, the identification module 604 is configured to identify the hard disk as a slow disk in response to the slow disk identification parameter being greater than a second threshold.

[0153] In another embodiment of the present disclosure, the identification module 604 is used to determine the slow disk likelihood estimation parameter of the hard disk based on the slow disk identification parameter, and the slow disk likelihood estimation parameter is used to represent the relationship between the possibility that the hard disk is a slow disk and the possibility that the hard disk is a non-slow disk; and the hard disk is identified as a slow disk based on the slow disk likelihood estimation parameter.

[0154] In another embodiment of the present disclosure, the identification module 604 is used to determine that the slow disk likelihood estimation parameter is negative infinity in response to the slow disk identification parameter being a first value; determine that the slow disk likelihood estimation parameter is positive infinity in response to the slow disk identification parameter being a second value; obtain a non-slow disk identification parameter of the hard disk in response to the slow disk identification parameter being greater than the first value and less than the second value, determine the slow disk likelihood estimation parameter of the hard disk based on the slow disk identification parameter and the non-slow disk identification parameter, and the non-slow disk identification parameter is determined based on the slow disk identification parameter; wherein the first value is less than the second value.

[0155] In another embodiment of the present disclosure, the identification module 604 is configured to identify the hard disk as a slow disk in response to a slow disk likelihood estimation parameter being greater than a third threshold.

[0156] In summary, the device provided by the disclosed embodiments, through statistical analysis of hard drive response times, eliminates the impact of delays from other hardware and software nodes on the I / O link, enabling timely detection of slow disks with high latency risks in the cluster, shortening response times and improving storage and read efficiency. Furthermore, by using a statistical perspective to analyze hard drive latency responses, it screens out hard drives with long-term performance anomalies, reducing the risk of misjudgment of hard drives due to occasional delays, improving detection accuracy, reducing the rate of incorrect disk replacement and secondary failures, and saving labor and material costs.

[0157] When the electronic device implementing the embodiment of the present disclosure is a terminal, see Figure 7, Figure 7 The following is a block diagram of a terminal 700 according to an exemplary embodiment of the present disclosure. Terminal 700 may be a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. Terminal 700 may also be referred to as user equipment, portable terminal, laptop terminal, desktop terminal, or other similar names.

[0158] Typically, the terminal 700 includes a processor 701 and a memory 702 .

[0159] The processor 701 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 701 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 701 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 701 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 701 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0160] The memory 702 may include one or more computer-readable storage media, which may be non-transitory. The memory 702 may also include high-speed random access memory and non-volatile memory, such as one or more magnetic disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 702 is used to store at least one instruction, which is executed by the processor 701 to implement the slow disk identification method provided in the method embodiment of the present application.

[0161] In some embodiments, terminal 700 may optionally include a peripheral device interface 703 and at least one peripheral device. Processor 701, memory 702, and peripheral device interface 703 may be connected via a bus or signal lines. Each peripheral device may be connected to peripheral device interface 703 via a bus, signal lines, or circuit boards. Specifically, the peripheral device may include at least one of a radio frequency circuit 704, a display screen 705, a camera assembly 706, an audio circuit 707, and a power supply 709.

[0162] The peripheral device interface 703 can be used to connect at least one I / O (Input / Output)-related peripheral device to the processor 701 and the memory 702. In some embodiments, the processor 701, the memory 702, and the peripheral device interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 701, the memory 702, and the peripheral device interface 703 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0163] The RF circuit 704 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 704 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 704 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the RF circuit 704 includes an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The RF circuit 704 can communicate with other terminals via at least one wireless communication protocol. Such wireless communication protocols include, but are not limited to, metropolitan area networks, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 704 may also include circuits related to Near Field Communication (NFC), which is not limited in this application.

[0164] Display screen 705 is used to display a user interface (UI). This UI can include graphics, text, icons, videos, or any combination thereof. When display screen 705 is a touchscreen display, it can also capture touch signals on or above the surface of display screen 705. These touch signals can be input as control signals to processor 701 for processing. Display screen 705 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there can be a single display screen 705, located on the front panel of terminal 700. In other embodiments, there can be at least two display screens 705, located on different surfaces of terminal 700 or in a foldable design. In still other embodiments, display screen 705 can be a flexible display screen, located on a curved or foldable surface of terminal 700. Furthermore, display screen 705 can be configured as a non-rectangular, irregular shape, also known as a special-shaped screen. Display screen 705 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0165] The camera assembly 706 is used to capture images or videos. Optionally, the camera assembly 706 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal, and the rear camera is arranged on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 706 may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0166] The audio circuit 707 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals to be input into the processor 701 for processing, or input into the radio frequency circuit 704 to achieve voice communication. For the purpose of stereo sound collection or noise reduction, there can be multiple microphones, which are respectively arranged in different parts of the terminal 700. The microphone can also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert the electrical signals from the processor 701 or the radio frequency circuit 704 into sound waves. The speaker can be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signals into sound waves audible to humans, but also convert the electrical signals into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 707 may also include a headphone jack.

[0167] Power supply 709 is used to power various components in terminal 700. Power supply 709 can be AC power, DC power, a disposable battery, or a rechargeable battery. When power supply 709 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0168] In some embodiments, the terminal 700 further includes one or more sensors 710 , including but not limited to: an acceleration sensor 711 , a gyroscope sensor 712 , a pressure sensor 713 , an optical sensor 715 , and a proximity sensor 716 .

[0169] The accelerometer 711 can detect the magnitude of acceleration along the three coordinate axes of the coordinate system established by the terminal 700. For example, the accelerometer 711 can be used to detect the components of gravity acceleration along the three coordinate axes. The processor 701 can control the display screen 705 to display the user interface in a landscape or portrait view based on the gravity acceleration signal collected by the accelerometer 711. The accelerometer 711 can also be used to collect game or user motion data.

[0170] The gyroscope sensor 712 can detect the orientation and rotation angle of the terminal 700. It can work with the accelerometer 711 to collect the user's 3D movements on the terminal 700. Based on the data collected by the gyroscope sensor 712, the processor 701 can implement the following functions: motion sensing (for example, changing the UI based on the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0171] The pressure sensor 713 can be set on the side frame of the terminal 700 and / or the lower layer of the display screen 705. When the pressure sensor 713 is set on the side frame of the terminal 700, it can detect the user's grip signal of the terminal 700, and the processor 701 performs left and right hand recognition or shortcut operations based on the grip signal collected by the pressure sensor 713. When the pressure sensor 713 is set on the lower layer of the display screen 705, the processor 701 controls the operable controls on the UI interface based on the user's pressure operation on the display screen 705. The operable controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0172] The optical sensor 715 is used to detect ambient light intensity. In one embodiment, the processor 701 can control the display brightness of the display screen 705 based on the ambient light intensity detected by the optical sensor 715. Specifically, when the ambient light intensity is high, the display brightness of the display screen 705 is increased; when the ambient light intensity is low, the display brightness of the display screen 705 is decreased. In another embodiment, the processor 701 can also dynamically adjust the shooting parameters of the camera assembly 706 based on the ambient light intensity detected by the optical sensor 715.

[0173] Proximity sensor 716, also known as a distance sensor, is typically located on the front panel of terminal 700. Proximity sensor 716 is used to detect the distance between the user and the front of terminal 700. In one embodiment, when proximity sensor 716 detects that the distance between the user and the front of terminal 700 is gradually decreasing, processor 701 controls display screen 705 to switch from the screen-on state to the screen-off state. When proximity sensor 716 detects that the distance between the user and the front of terminal 700 is gradually increasing, processor 701 controls display screen 705 to switch from the screen-off state to the screen-on state.

[0174] Those skilled in the art will understand that Figure 7 The structure shown in the figure does not constitute a limitation on the terminal 700, and the terminal 700 may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0175] The terminal provided by the disclosed embodiment performs statistical analysis on the hard drive's response time, eliminating the impact of delays from other hardware and software nodes on the I / O link. This allows for the timely identification of slow disks in the cluster with high latency risks, shortening response times and improving storage and read efficiency. Furthermore, statistical analysis of the hard drive's delayed response is performed from a statistical perspective, screening out hard drives with long-term performance anomalies. This reduces the risk of misjudging hard drives due to occasional delays, improves detection accuracy, reduces the rate of incorrect disk replacement and secondary failures, and saves both labor and material costs.

[0176] When the electronic device implementing the embodiment of the present disclosure is a server, see Figure 8 , Figure 8 A server for slow disk identification is shown according to an exemplary embodiment. Server 800 includes a processing component 822, which further includes one or more processors, and memory resources represented by memory 832 for storing instructions executable by processing component 822, such as applications. The applications stored in memory 832 may include one or more modules, each corresponding to a set of instructions. Furthermore, processing component 822 is configured to execute the instructions to perform the functions performed by the server in the above-described slow disk detection method.

[0177] The server 800 may also include a power supply component 826 configured to perform power management of the server 800, a wired or wireless network interface 850 configured to connect the server 800 to a network, and an input / output (I / O) interface 858. The server 800 may operate based on an operating system stored in the memory 832, such as Windows Server 200. TM , Mac OSX TM , Unix TM , Linux TM , FreeBSD TM or similar.

[0178] The server provided by the disclosed embodiments performs statistical analysis on hard drive response times, eliminating the impact of delays from other hardware and software nodes on the I / O link. This allows for the timely identification of slow disks in the cluster with high latency risks, shortening response times and improving storage and read efficiency. Furthermore, statistical analysis of hard drive latency responses is performed from a statistical perspective, screening out hard drives with long-term performance anomalies. This reduces the risk of misjudging hard drives due to occasional delays, improves detection accuracy, reduces the rate of incorrect disk replacement and secondary failures, and saves both labor and material costs.

[0179] The present disclosure provides a computer-readable storage medium, wherein the storage medium stores at least one program code, and the at least one program code is loaded and executed by a processor to implement Figure 2 or Figure 3 The computer-readable storage medium may be non-transitory. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, or an optical data storage device.

[0180] The computer-readable storage medium provided by the disclosed embodiments performs statistical analysis on hard drive response times, eliminating the impact of delays from other hardware and software nodes on the I / O link. This allows for the timely identification of slow disks with high latency risks within the cluster, shortening response times and improving storage and read efficiency. Furthermore, statistical analysis of hard drive latency responses is performed from a statistical perspective, screening out hard drives with chronic performance anomalies. This reduces the risk of misjudging hard drives due to occasional delays, improves detection accuracy, reduces the rate of incorrect disk replacement and secondary failures, and saves both labor and material costs.

[0181] The present disclosure provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, so that the computer device performs the above-mentioned Figure 2 or Figure 3 The method provided in various optional implementations of slow disk identification is shown.

[0182] The computer program product or computer program provided by the embodiments of the present disclosure, by statistically analyzing the response time of hard disks, eliminates the delay effects of other hardware and software nodes on the I / O link, and can promptly identify slow disks with high delay risks in the cluster, shortening response time and improving storage and reading efficiency. Furthermore, by using a statistical perspective to perform statistical analysis of hard disk delay responses, hard disks with long-term performance anomalies are screened out, reducing the risk of misjudgment of hard disks due to occasional delays, improving detection accuracy, reducing the rate of incorrect disk replacement and secondary failure rates, and saving labor and material costs.

[0183] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0184] The above description is merely an optional embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A slow disk identification method, characterized in that: The method comprises: Determining an abnormal response frequency of the hard disk according to the response time and power-on time of the hard disk; determining abnormal response frequency deviation data of the hard disk according to the abnormal response frequency and the average abnormal response frequency, wherein the average abnormal response frequency is determined based on the abnormal response frequencies of multiple hard disks in the electronic device, and the abnormal response frequency deviation data is used to represent the difference between the abnormal response frequency and the average abnormal response frequency; determining a slow disk identification parameter of the hard disk according to at least one of the abnormal response frequency or the abnormal response frequency deviation data, wherein the slow disk identification parameter is used to indicate a possibility that the hard disk is a slow disk; determining a slow disk likelihood estimation parameter of the hard disk according to the slow disk identification parameter, wherein the slow disk likelihood estimation parameter is used to represent a relationship between a likelihood that the hard disk is a slow disk and a likelihood that the hard disk is a non-slow disk; The hard disk is identified as a slow disk according to the slow disk likelihood estimation parameter.

2. The method according to claim 1, characterized in that The determining the abnormal response frequency of the hard disk according to the response time and power-on time of the hard disk includes: Obtaining, from the response time of the hard disk, the number of responses in which the response time exceeds a first threshold; The ratio of the number of responses to the power-on time is determined as the abnormal response frequency.

3. The method according to claim 1, characterized in that The determining, based on the abnormal response frequency and the average abnormal response frequency, abnormal response frequency deviation data of the hard disk includes: Obtaining a first difference between the abnormal response frequency and the average abnormal response frequency; The ratio of the first difference to the abnormal response frequency standard deviation is determined as the abnormal response frequency deviation data of the hard disk, and the abnormal response frequency standard deviation is determined according to the abnormal response frequencies of multiple hard disks in the electronic device.

4. The method according to claim 1, wherein The determining, based on at least one of the abnormal response frequency or the abnormal response frequency deviation data, a slow disk identification parameter of the hard disk includes: In response to the abnormal response frequency being less than the average abnormal response frequency, determining that a slow disk identification parameter of the hard disk is a first value; In response to the abnormal response frequency being greater than the average abnormal response frequency, maximum abnormal response deviation data in the electronic device is determined, and a slow disk identification parameter of the hard disk is determined based on the abnormal response frequency deviation data and the maximum abnormal response deviation data.

5. The method according to claim 4, characterized in that The determining of the maximum abnormal response deviation data in the electronic device includes: Obtaining a maximum abnormal response frequency from the abnormal response frequencies of the multiple hard disks in the electronic device; Obtaining a second difference between the maximum abnormal response frequency and the average abnormal response frequency; The ratio of the second difference to the abnormal response frequency standard deviation is determined as the maximum abnormal response deviation data, where the abnormal response frequency standard deviation is determined according to the abnormal response frequencies of multiple hard disks in the electronic device.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: In response to the slow disk identification parameter being greater than a second threshold, the hard disk is identified as a slow disk.

7. The method according to claim 1, characterized in that The determining, based on the slow disk identification parameter, a slow disk likelihood estimation parameter of the hard disk includes: In response to the slow disk identification parameter being a first value, determining that the slow disk likelihood estimation parameter is negative infinity; In response to the slow disk identification parameter being a second value, determining the slow disk likelihood estimation parameter to be positive infinity; In response to the slow disk identification parameter being greater than the first value and less than the second value, a non-slow disk identification parameter of the hard disk is obtained, and a slow disk likelihood estimation parameter of the hard disk is determined based on the slow disk identification parameter and the non-slow disk identification parameter, and the non-slow disk identification parameter is determined based on the slow disk identification parameter; wherein the first value is less than the second value.

8. The method according to claim 1, characterized in that The step of identifying the hard disk as a slow disk according to the slow disk likelihood estimation parameter includes: In response to the slow disk likelihood estimation parameter being greater than a third threshold, the hard disk is identified as a slow disk.

9. A slow disk identification device, characterized in that: The device comprises: A first determining module is configured to determine an abnormal response frequency of the hard disk according to a response time and a power-on time of the hard disk; a second determining module, configured to determine abnormal response frequency deviation data of the hard disk based on the abnormal response frequency and an average abnormal response frequency, wherein the average abnormal response frequency is determined based on abnormal response frequencies of multiple hard disks in the electronic device, and the abnormal response frequency deviation data is used to represent a difference between the abnormal response frequency and the average abnormal response frequency; a third determining module, configured to determine a slow disk identification parameter of the hard disk according to at least one of the abnormal response frequency or the abnormal response frequency deviation data, wherein the slow disk identification parameter is used to indicate a possibility that the hard disk is a slow disk; An identification module is used to determine the slow disk likelihood estimation parameters of the hard disk based on the slow disk identification parameters, and the slow disk likelihood estimation parameters are used to express the relationship between the possibility that the hard disk is a slow disk and the possibility that the hard disk is not a slow disk; and perform slow disk identification on the hard disk based on the slow disk likelihood estimation parameters.

10. The device according to claim 9, characterized in that The first determining module is configured to: Obtaining, from the response time of the hard disk, the number of responses in which the response time exceeds a first threshold; The ratio of the number of responses to the power-on time is determined as the abnormal response frequency.

11. The device according to claim 9, characterized in that The second determination module is used to obtain a first difference between the abnormal response frequency and the average abnormal response frequency; and determine the ratio of the first difference to the standard deviation of the abnormal response frequency as the abnormal response frequency deviation data of the hard disk, where the standard deviation of the abnormal response frequency is determined based on the abnormal response frequencies of multiple hard disks in the electronic device.

12. The device according to claim 9, characterized in that The third determination module is used to determine that the slow disk identification parameter of the hard disk is a first value in response to the abnormal response frequency being less than the average abnormal response frequency; in response to the abnormal response frequency being greater than the average abnormal response frequency, determine the maximum abnormal response deviation data in the electronic device, and determine the slow disk identification parameter of the hard disk based on the abnormal response frequency deviation data and the maximum abnormal response deviation data.

13. The device according to claim 12, characterized in that The third determining module is configured to: Obtaining a maximum abnormal response frequency from the abnormal response frequencies of the multiple hard disks in the electronic device; Obtaining a second difference between the maximum abnormal response frequency and the average abnormal response frequency; The ratio of the second difference to the abnormal response frequency standard deviation is determined as the maximum abnormal response deviation data, where the abnormal response frequency standard deviation is determined according to the abnormal response frequencies of multiple hard disks in the electronic device.

14. The device according to any one of claims 9 to 13, characterized in that The identification module is further used to: In response to the slow disk identification parameter being greater than a second threshold, the hard disk is identified as a slow disk.

15. The device according to claim 9, characterized in that The identification module is used to: In response to the slow disk identification parameter being a first value, determining that the slow disk likelihood estimation parameter is negative infinity; In response to the slow disk identification parameter being a second value, determining the slow disk likelihood estimation parameter to be positive infinity; In response to the slow disk identification parameter being greater than the first value and less than the second value, a non-slow disk identification parameter of the hard disk is obtained, and a slow disk likelihood estimation parameter of the hard disk is determined based on the slow disk identification parameter and the non-slow disk identification parameter, and the non-slow disk identification parameter is determined based on the slow disk identification parameter; wherein the first value is less than the second value.

16. The device according to claim 9, characterized in that The identification module is used to: In response to the slow disk likelihood estimation parameter being greater than a third threshold, the hard disk is identified as a slow disk.

17. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the slow disk identification method according to any one of claims 1 to 8.

18. A computer-readable storage medium, characterized in that At least one program code is stored in the storage medium, and the at least one program code is loaded and executed by the processor to implement the slow disk identification method according to any one of claims 1 to 8.

19. A computer program product, comprising computer instructions, wherein the computer instructions are stored in a computer-readable storage medium, a processor of a 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 slow disk identification method as described in any one of claims 1 to 8.

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