Methods, apparatus, and computer program products for managing storage systems

Abstract

Embodiments of this disclosure relate to methods, electronic devices, and computer program products for managing storage systems. The method includes determining whether the storage device failure will no longer occur based on reference values ​​of attribute parameters in a first set of the attribute parameters and current values ​​of attribute parameters in a second set of the attribute parameters, if it is determined that the storage device will fail based on current values ​​of multiple attribute parameters in the storage system. The method further includes determining the cause of the failure based on the attribute parameters in the first set if it is determined that the storage device failure will no longer occur. Embodiments of this disclosure can analyze the cause of storage device failure, thereby avoiding the erroneous identification of internally faulty storage devices as internally faulty ones.

Description

Technical Field

[0001] Embodiments of this disclosure generally relate to the field of data storage, and more specifically to methods, apparatus, and computer program products for managing storage systems. Background Technology

[0002] In storage systems, failure prediction is necessary to avoid data loss due to storage device failures. With the rapid development of technologies such as artificial intelligence and machine learning, failure prediction for storage devices has attracted widespread attention. However, conventional failure prediction methods suffer from low accuracy and a lack of effective analysis of the causes of failures, leading to wasted storage resources and increased system costs. Summary of the Invention

[0003] Embodiments of this disclosure provide methods, apparatus, and computer program products for managing storage systems.

[0004] In a first aspect of this disclosure, a method for managing a storage system is provided. The method includes determining whether the failure of the storage device will no longer occur, based on reference values ​​of attribute parameters in a first set of the plurality of attribute parameters and current values ​​of attribute parameters in a second set of the plurality of attribute parameters, if it is determined that the failure of the storage device will not occur. The method further includes determining the cause of the failure based on the attribute parameters in the first set if it is determined that the failure of the storage device will no longer occur.

[0005] In a second aspect of this disclosure, an electronic device is provided. The electronic device includes at least one processing unit and at least one memory. The at least one memory is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit. When executed by the at least one processing unit, the instructions cause the electronic device to perform an action including determining whether the failure of the storage device will no longer occur, based on reference values ​​of attribute parameters in a first set of the plurality of attribute parameters and current values ​​of attribute parameters in a second set of the plurality of attribute parameters, if it is determined that the failure of the storage device will no longer occur. The method further includes determining the cause of the failure based on the attribute parameters in the first set if it is determined that the failure of the storage device will no longer occur.

[0006] In a third aspect of this disclosure, a computer program product is provided. The computer program product is tangibly stored in a non-transitory computer storage medium and includes machine-executable instructions. When executed by a device, the machine-executable instructions cause the device to perform any step of the method described in the first aspect of this disclosure.

[0007] The summary section is provided to present the chosen concepts in a simplified form, which will be further described in the detailed description below. The summary section is not intended to identify key or essential features of this disclosure, nor is it intended to limit the scope of this disclosure. Attached Figure Description

[0008] The above and other objects, features and advantages of this disclosure will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0009] Figure 1 A schematic diagram of an example system that can be implemented therein according to some embodiments of the present disclosure is shown;

[0010] Figure 2 A flowchart is shown illustrating an example method for predicting and analyzing failures of a storage device according to some embodiments of the present disclosure;

[0011] Figure 3 A schematic diagram illustrates an example method for analyzing the causes of storage device failures according to some embodiments of the present disclosure;

[0012] Figure 4 A schematic diagram of another example method for analyzing the causes of storage device failures according to some embodiments of the present disclosure is shown;

[0013] Figure 5 More detailed schematic block diagrams illustrating some embodiments of the present disclosure for predicting and analyzing failures of storage devices are shown; and

[0014] Figure 6 A schematic block diagram of an example device that can be used to implement embodiments of the present disclosure is shown.

[0015] In the various figures, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0016] Preferred embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0017] The term "comprising" and its variations as used herein signify open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "at least partially based on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0018] Since storage devices in a storage system typically store large amounts of important data, a failure could result in the loss of a significant amount of data. Therefore, to avoid data loss, it is necessary to predict whether a storage device will fail.

[0019] In conventional approaches, machine learning-based failure prediction methods are used to predict whether storage devices will fail. When improving these methods, the focus is typically on increasing the accuracy of the trained model to enhance prediction accuracy, without considering the underlying causes of storage device failure.

[0020] The aforementioned conventional solutions have encountered some problems in practical use. For example, in actual use, when a storage device is determined to fail due to external factors such as cable connection failure or excessive operating temperature, the storage device itself does not actually have an internal fault; that is, the storage device itself is not actually faulty. However, in this case, since the user does not know the specific cause of the failure, they will usually simply remove the storage device. Therefore, in the conventional solution, even if some storage devices do not have internal faults, users are very likely to remove them directly to eliminate the fault. This approach results in the waste of storage devices. Furthermore, when a storage device is determined to fail due to external factors, such as a cable connection failure, the storage device itself does not actually have an internal fault. In the conventional solution, the user is very likely to remove the storage device and replace it with a new one. Since the cable connection fault is still not repaired, the new storage device will still fail, thus failing to solve the storage device failure problem.

[0021] Furthermore, following this conventional approach, time and resources are required to back up the data stored on the storage device before removal. After removal, a new storage device is needed to replace it. This further consumes time and resources to store the backup data on the new device, resulting in wasted time and resources. The backup and data transfer processes also impact the performance of the storage system due to the large number of I / O operations involved.

[0022] Embodiments of this disclosure propose a scheme for managing a storage system to address one or more of the aforementioned problems and other potential issues. In this scheme, if it is determined that a storage device will fail based on the current values ​​of multiple attribute parameters of a storage device in the storage system, instead of directly providing instructions to remove the storage device, it determines whether the failure will still occur based on reference values ​​of attribute parameters in a first set and the current values ​​of attribute parameters in a second set. If it is determined that the failure will no longer occur, the cause of the failure is determined based on the attribute parameters in the first set.

[0023] The embodiments of this disclosure can analyze the causes of storage device failures, avoiding the erroneous identification of internally fault-free storage devices as those with internal faults, thereby preventing the unnecessary removal of such devices and saving costs. In this way, the I / O operations required for backing up storage devices to be removed can also be significantly reduced, thus saving time and resources and improving storage system performance.

[0024] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0025] Figure 1 A schematic diagram of an example storage system 100 that can be implemented according to some embodiments of the present disclosure is shown. The storage system 100 includes a storage device 101 for storing data. The storage system 100 stores a plurality of attribute parameters relating to the storage device 101, including external attribute parameters 111 and internal attribute parameters 112. External attribute parameters 111 are attribute parameters associated with the operating environment of the storage device 101, such as cable connection parameters or temperature parameters. Internal attribute parameters 112 are attribute parameters associated with the internal operation of the storage device 101, such as lifetime parameters, media loss parameters, or other internal parameters.

[0026] The storage system 100 also includes a computing device 102, which can determine whether the storage device 101 is about to fail based on external attribute parameters 111 and internal attribute parameters 112. If it is determined that the storage device 101 is about to fail, the computing device 102 can further determine the cause of the failure. The cause of the failure of the storage device 101 can include external causes 121 and internal causes 122. External causes 121 are, for example, causes related to the operating environment of the storage device 101. For example, external causes 121 could be cable connection failures, temperature values ​​outside the normal range, or other causes unrelated to the internal operation of the storage device. Internal causes 122 can be causes related to the internal operation of the storage device 101, such as excessively long service life, severe media degradation, or other internal causes.

[0027] It should be understood that Figure 1 The example storage system 100 is merely exemplary and not limiting. According to embodiments of this disclosure, the storage system 100 can be implemented in any suitable manner. For example, the storage system 100 may include any suitable number of storage devices 101, and these storage devices may be connected or configured in any suitable manner. Examples of storage devices 101 may include, but are not limited to, hard disk drives, tape drives, optical drives, hard disk drive (HDD), solid-state storage devices (SSD), redundant arrays of disks (RAID), or other hard disk devices.

[0028] Figure 2 A flowchart is shown of an example method 200 for managing a storage system 100 according to some embodiments of the present disclosure. Method 200 may be, for example, by... Figure 1 The method 200 is executed by the computing device 102 of the storage system 100 shown. It should be understood that method 200 can also be executed by other suitable devices or apparatus. Method 200 may include additional actions not shown and / or the actions shown may be omitted; the scope of this disclosure is not limited in this respect. The following is in conjunction with… Figure 1 Let me describe method 200 in detail.

[0029] like Figure 2 As shown, at 210, computing device 102 determines whether storage device 101 will fail based on the current values ​​of multiple attribute parameters of storage device 101. The multiple attribute parameters may include any suitable parameters indicating the state of storage device 101. In some embodiments, the multiple attribute parameters of storage device 101 may include external attribute parameters 111 associated with the usage environment of storage device 101 and internal attribute parameters 112 associated with the internal operation of storage device 101.

[0030] In some embodiments, external attribute parameter 111 may include attribute parameters as shown in Table 1. Specifically, external attribute parameter 111 may include two subcategories: cable connection parameters and temperature parameters. Cable connection parameters may include the number of cable connection resets and the number of cable connection failures, and temperature parameters may include the storage device temperature. It should be understood that the external attribute parameters 111 in Table 1 are merely exemplary and not restrictive. In some embodiments, external attribute parameter 111 may also include other parameters related to the operating environment of storage device 101, such as the humidity of the operating environment of storage device 101.

[0031] Table 1. Examples of external attribute parameters

[0032]

[0033] In some embodiments, internal attribute parameters 112 may include attribute parameters as shown in Table 2. Specifically, internal attribute parameters 112 may include subcategories such as lifetime parameters, media loss parameters, and other internal parameters. Lifetime parameters may include power-on time and number of power-on / off cycles. Media loss parameters may include the number of read / write errors, the number of sector reallocations, and the number of drive failures. Other internal parameters may also include the number of drive head failures. It should be understood that the internal attribute parameters 112 in Table 2 are merely exemplary and not restrictive. In some embodiments, internal attribute parameters 112 may also include other attribute parameters associated with the internal operation of storage device 101.

[0034] Table 2. Examples of Internal Attribute Parameters

[0035]

[0036]

[0037] Return to Figure 2 If it is determined at 210 that storage device 101 will fail, then method 200 proceeds to 220. At 220, computing device 102 determines whether the failure will no longer occur based on reference values ​​of attribute parameters in a first set of a plurality of attribute parameters and current values ​​of attribute parameters in a second set of a plurality of attribute parameters. In some embodiments, the reference value of the attribute parameter may be the average value of that attribute parameter for normal storage devices stored in the historical data of storage system 100. In other embodiments, the standard value of the attribute parameter may also be a standard value of that attribute parameter provided by the storage device manufacturer, or other attribute parameter values ​​corresponding to normal storage devices.

[0038] In some embodiments, the first set may be a set of at least one attribute parameter selected from a plurality of attribute parameters. A second set may be optionally determined from attribute parameters other than those included in the first set.

[0039] In some other embodiments, the first set may include at least one external attribute parameter 111 selected from a plurality of attribute parameters, and may also include additional attribute parameters. A second set may be determined from attribute parameters other than those included in the first set. Using this method, since the first set includes at least one external attribute parameter 111, it is easier to determine whether the cause of the failure includes an external cause.

[0040] In some embodiments, a first set of attribute parameters can be determined from a plurality of attribute parameters based on historical fault information of the storage device 101. The historical fault information includes a failure rate associated with each of the plurality of candidate attribute parameters, and the first set can include the attribute parameter with the highest failure rate. A second set can be determined from attribute parameters other than those included in the first set. Since the first set includes the attribute parameter with the highest failure rate in the historical record, the cause of the failure is more easily determined based on the first set determined in this way.

[0041] If it is determined at 220 that the failure of storage device 101 will no longer occur, method 200 proceeds to 230. At 230, computing device 102 determines the cause of the failure based on attribute parameters in a first set. In some embodiments, the cause of the failure may include an external cause 121 and an internal cause 122. (See the following references...) Figures 3-4 The description will provide a more detailed account of the process for determining the cause of the failure.

[0042] Return to Figure 2If it is determined at 220 that the failure of storage device 101 will still occur, method 200 proceeds to 240. At 240, computing device 102 updates the first set. In some embodiments, at least one attribute parameter may be moved from the second set to the first set to update the first set. In other embodiments, at least one attribute parameter in the first set may be moved to the second set to update the first set. Alternatively or additionally, the first set may also be updated by moving at least one attribute parameter from the second set to the first set and moving at least one attribute parameter in the first set to the second set. It should be understood that the process of updating the first set described above is merely exemplary and not limiting; other methods may be used to update the first set in other embodiments. In some embodiments, additionally or alternatively, after updating the first set, the cause of the failure of storage device 101 may be determined based on the updated first set.

[0043] By employing the methods described above, the cause of failure in storage device 101 can be predicted, thereby preventing the erroneous identification of internally fault-free storage devices as those with internal failures, and thus avoiding the unnecessary removal of such devices. Furthermore, because the cause of failure in storage device 101 is predicted, users can make more confident decisions regarding the removal of faulty storage devices. This approach also significantly reduces I / O operations required for backing up storage devices to be removed, saving time and resources and improving system performance.

[0044] In some embodiments, such as Figures 3-4 Methods 300 and 400 shown are used to determine the cause of the failure in storage device 101. The following will combine... Figures 3-4 Several embodiments for determining the cause of failure in storage device 101 are described in more detail.

[0045] Figure 3 A schematic diagram of an example method 300 for analyzing the causes of a failure in storage device 101 according to some embodiments of the present disclosure is shown. Method 300 can be considered as an example implementation of block 230 in method 200. Figure 3 As shown, at 310, computing device 102 determines whether the cause of the storage device 101 failure includes external causes associated with the usage environment of the storage device 101. In some embodiments, this can be achieved by determining, as referenced... Figure 2The determination of whether the first set includes external attribute parameter 111 is used to determine whether the cause of the storage device 101 failure includes external causes related to the usage environment of the storage device 101. If the first set includes external attribute parameter 111, then the cause of the storage device 101 failure includes external causes. If the first set does not include external attribute parameter 111, then the cause of the storage device 101 failure does not include external causes.

[0046] If at 310 it is determined that the cause of the storage device 101 failure includes external causes, then method 300 proceeds to 320. At 320, instructions are provided regarding the environment in which the storage device 101 is used to be checked. If at 310 it is determined that the cause of the storage device 101 failure does not include external causes, then method 300 proceeds to 330. At 330, instructions are provided regarding the removal of the storage device 101.

[0047] In this way, the cause of failure of storage device 101 can be predicted. Specifically, when method 300 determines that the cause of failure of storage device 101 includes external factors, it does not provide instructions on whether storage device 101 should be removed, but instead provides instructions on whether the operating environment of storage device 101 should be checked. This allows users to easily and accurately know the cause of failure of storage device 101, enabling efficient troubleshooting. Consequently, unnecessary removal of storage device 101 that is not internally faulty can be avoided, thus saving costs.

[0048] In some embodiments, the cause of failure of storage device 101 can be determined in more detail. Figure 4 A schematic diagram of an example method 400 for analyzing the causes of a failure in storage device 101 according to some embodiments of the present disclosure is shown. Method 400 can be considered as an example implementation of blocks 310 and 320 of method 300. At 410, computing device 102 determines whether the cause of the failure includes a cable connection failure. For example, this can be determined by determining, as referenced... Figure 2 The cause of the fault is determined by whether the first set of parameters listed in Table 1 above is included. If the first set includes cable connection parameters, the cause of the fault is determined to be a cable connection fault. If the first set does not include cable connection parameters, the cause of the fault is determined to be an ineligible cable connection fault.

[0049] If the cause of the fault is determined at 410 to include a cable connection failure, then method 400 proceeds to 420. At 420, instructions are provided regarding the cable connections of storage device 101 that need to be checked.

[0050] If the cause of the fault is determined at 410 not to be a cable connection fault, then method 400 proceeds to 430. At 430, the computing device 102 determines whether the cause of the fault includes a temperature value outside the normal range. For example, this can be determined by determining whether the first set includes temperature parameters as listed in Table 1 above. If the first set includes temperature parameters, then the cause of the fault is determined to include a temperature value outside the normal range. Conversely, if the first set does not include temperature parameters, then the cause of the fault is determined not to include a temperature value outside the normal range.

[0051] If the cause of the fault is determined at 430 to include a temperature value outside the normal range, then method 400 proceeds to 440. At 440, instructions are given regarding the fans of storage device 101 that need to be checked.

[0052] The previous text combined Figures 3-4 An example implementation for determining the cause of a failure in storage device 101 is described, but this is merely illustrative. Other methods can also be used to determine the cause of a failure.

[0053] By using the above methods, the cause of failure of storage device 101 can be predicted. Specifically, if method 400 determines that the cause of failure of storage device 101 includes external factors such as cable connection failure or temperature values ​​outside the normal range, no instruction is provided to remove storage device 101. Instead, instructions are provided regarding the operating environment of storage device 101, such as cable connections or fan operation, to be checked. This avoids unnecessary removal of storage device 101 when it is not internally faulty, thus preventing waste of storage devices. Furthermore, this method provides instructions to check the cable connections or fan operation of storage device 101, allowing the user to inspect these aspects and potentially restore normal operation of storage device 101.

[0054] Figure 5 A more detailed schematic block diagram is shown illustrating some embodiments of the present disclosure for predicting and analyzing failures of storage device 101. Figure 5 The prediction module 501, cause determination module 502, and decision-making module 503 can be used in the process. Figure 1 The prediction module 501, cause determination module 502, and decision module 503 are implemented in the computing device 102 shown. It should be understood that the prediction module 501, cause determination module 502, and decision module 503 can also be executed by other suitable devices or apparatuses.

[0055] like Figure 5As shown, the prediction module 501 can be used to predict whether the storage device 101 will fail based on external attribute parameters 111 and internal attribute parameters 112. In some embodiments, external attribute parameters 111 and internal attribute parameters 112 may include attribute parameters listed in Tables 1 and 2 above. In other embodiments, external attribute parameters 111 and internal attribute parameters 112 may also include other attribute parameters.

[0056] like Figure 5 As shown, the prediction module 501 can predict whether the storage device 101 will fail based on the machine learning model 511 and obtain a prediction result 512. In some embodiments, the machine learning model 511 can be a trained machine learning model, such as a trained neural network model. It should be understood that the machine learning model 511 can be other machine learning models.

[0057] If prediction result 512 indicates that the storage device will not fail, no further action is required. However, if prediction result 512 indicates that the storage device will fail, the cause determination module 502 is used to find and prioritize the causes of the failure.

[0058] like Figure 5 As shown, the cause determination module 502 includes a cause lookup module 521 and a cause sorting module 522. The cause lookup module 521 can be used to find the possible causes of the storage device 101 failure. For example, the possible causes of the storage device 101 failure may include single cause (SRC) and multiple cause (MRC).

[0059] First, the cause determination module 502 can use the following equations (1)-(2) to find possible single causes (SRCs).

[0060]

[0061] SRC={F1,F2…F n} (2)

[0062] In equations (1) and (2), FC represents the set of subcategories of attributes. For example, referring to Tables 1 and 2 above, FC = {service life parameter, dielectric loss parameter, other internal parameters, cable connection parameter, temperature parameter}. F represents any subcategory in the set of subcategories of attributes FC. For example, F represents the cable connection parameter subcategory. F = {F1, F2…F2} n} indicates that F can include multiple attribute parameters F1, F2...F belonging to that subcategory. nFor example, when F represents a cable connection parameter subcategory, F can include the number of cable connection resets and the number of cable connection faults as shown in Table 1, i.e., F = {F1, F2}, where F1 represents the number of cable connection resets and F2 represents the number of cable connection faults. In some embodiments, FC can also be other subcategory attribute sets.

[0063] Returning to equation (1), I represents the set of current values ​​of multiple attribute parameters of storage device 101, each of which belongs to a subcategory of FC. The attribute parameters belonging to F in I form the first set, and the other attribute parameters form the second set. MF(I,F) represents the set of reference values ​​of the attribute parameters in the first set and the set of current values ​​of the attribute parameters in the second set. For example, the reference value may be the average value of this attribute parameter of normal storage devices stored in the historical data of storage system 100.

[0064] In equation (1), P(MF(I,F)) = 0 and P(I) = 1 represent that the prediction results of the prediction module 501 based on MF(I,F) and I are 0 and 1, respectively. Result 0 indicates that the prediction result is that the storage device 101 will not fail, while result 1 indicates that the prediction result is that the storage device 101 will fail. In other embodiments, the output result of the prediction module 501 can also be any other value between 0 and 1, and an upper threshold and a lower threshold can be set (for example, the upper threshold can be 0.9 and the lower threshold can be 0.1). When the output result P(·) of the prediction module 501 is higher than the upper threshold, it indicates that the storage device 101 will fail, and when the output result P(·) of the prediction module 501 is lower than the lower threshold, it indicates that the storage device 101 will not fail. If there is a subcategory F in FC that satisfies equation (1), then the cause lookup module 521 finds a single cause SRC = F = {F1, F2…F}. n}

[0065] In some embodiments, if a single cause SRC is found using the cause lookup module 521, the cause sorting module 522 will not be used for sorting. Instead, the cause determination module 502 will directly output the single cause SRC to the decision module 503.

[0066] Conversely, if the cause lookup module 521 cannot find a single cause (SRC), then the cause lookup module 521 continues to use the following equations (3)-(5) to find all multiple causes (MRCs).

[0067]

[0068]

[0069] E = {MRC1, MRC2, MRC3…MRC m} (5)

[0070] In equations (3)-(5) above, MRC represents a subset of set FC, I represents the set of current values ​​of multiple attribute parameters of storage device 101, the attribute parameters belonging to MRC in I form the first set, and the other attribute parameters form the second set. MS(I,MRC) represents the set of reference values ​​of attribute parameters in the first set and the set of current values ​​of attribute parameters in the second set, and P(MS(I,MRC)) represents the result predicted by prediction module 501 based on MS(I,MRC) that storage device 101 will not fail. Equation (3) indicates that MRC is a multi-cause MRC that satisfies the condition. In equation (4) above, S is a subset of MRC, and equation (4) indicates that there is no subset S in MRC that can make P(MS(I,S)) = 0. Equation (5) represents all multi-cause MRC1, MRC2, MRC3...MRC that satisfy equations (3) and (4) above. m This constitutes a set E of multiple causes. Using the above method, the cause-finding module 521 can identify all possible multiple causes MRC1, MRC2, MRC3…MRC. m The set E is used to avoid missing the cause of the fault.

[0071] In some embodiments, after the cause finding module 521 finds the set E of all multiple causes, the cause determination module 502 can output it to the decision module 503. Alternatively or additionally, after finding the set E of all multiple causes, the cause ranking module 522 can also be used to rank each of the multiple causes in the set E of all multiple causes to determine the most important causes. For example, the cause ranking module 522 can be used to determine the three most important causes. The cause ranking module 522 ranks each of the multiple causes in the set E of all multiple causes using the following equations (6)-(7).

[0072]

[0073]

[0074] In equation (7), sub-class represents a subclass, such as subclass F1. If subclass F1 belongs to MRC, the function f(MRC,F1) has a value of 1; otherwise, f(MRC,F1) has a value of 0. Next, equation (6) is used to calculate the importance of subclass F1. When there are m multi-cause MRC1-MRC... m When subcategory F1 is included, for each MRC that includes subcategory F1 i ,i=1,…,m, calculate MRCi The number of all subcategories Num(MRC) i ), then the MRC i f(MRC) is calculated using equation (7). i The value of F1 will be used for each MRC i f(MRC) i F1) divided by the number Num(MRC) i The values ​​obtained are added together to obtain the importance of subcategory F1. It should be understood that in other embodiments, other methods can also be used to calculate the importance of subcategories. After calculating the importance of each subcategory, the subcategories are sorted from high to low according to their importance values, and at least one subcategory with the highest importance value is selected. For example, the three subcategories with the highest importance values ​​can be selected and output to the decision module 503.

[0075] In this way, the cause ranking module 522 sorts the possible causes of failure according to their importance and identifies one or more of the most important causes, thereby avoiding interference from less important causes to the decision module 503 and improving the accuracy and efficiency of the decision module 503.

[0076] In some embodiments, a decision module 503 may also be included. The decision module 503 may receive a single cause or multiple most important causes output by the cause determination module 502, analyze the received cause, and provide a decision. If the decision module 503 receives only a single cause, it may determine whether the cause is associated with an internal attribute parameter. When the cause is associated with an internal attribute parameter, such as a lifespan parameter, the decision module 503 outputs an instruction 531 that the storage device should be removed. The decision module 503 may also determine whether the cause is associated with a cable fault. When the cause is associated with a cable fault, the decision module 503 outputs an instruction 532 that the cable connections of the storage device should be checked. Furthermore, when the decision module 503 determines that the cause is associated with temperature, it outputs an instruction 533 that the fan of the storage device should be checked.

[0077] If the cause determination module 502 outputs multiple causes to the decision module, the decision module 503 will determine the category of the multiple causes. When the multiple causes do not include external causes related to external attribute parameters, the decision module 503 will output an instruction 531 to remove the storage device. When the multiple causes include causes related to cable connection failures, the decision module 503 will output an instruction 532 to check the cable connections of the storage device. Furthermore, when the multiple causes received by the decision module 503 include causes related to temperature, the decision module 503 will output an instruction 533 to check the fan of the storage device. It should be understood that the decision module 503 can also perform additional determinations and output additional instructions.

[0078] In this way, the cause determination module 502 can predict the cause of failure of storage device 101. If the cause of failure includes external factors, the decision module 503 will not output an instruction to remove storage device 101, but instead output an instruction to check the operating environment (e.g., an instruction 532 to check the cable connections of the storage device, and an instruction 533 to check the fan of the storage device). This avoids unnecessary removal of the internally functioning storage device 101, thus saving costs. Furthermore, this method improves the accuracy of machine learning-based prediction methods for fault prediction of storage device 101, and allows for better application of machine learning-based fault prediction methods in the storage system 100.

[0079] In other embodiments, when the cause of a failure includes both external and internal causes, the decision module 503 outputs an instruction regarding the usage environment of the storage device 101 (e.g., cable connections, fans) that needs to be checked and subsequently reconfirmed. In this way, the user first checks the usage environment of the storage device 101 to ensure that cable connections are normal and fans are functioning properly. After confirming that the usage environment is normal, the prediction module 501 can be used to re-predict whether a failure has occurred in the storage device 101, ensuring that no internal failure has occurred in the storage device 101, thereby improving the accuracy of failure prediction.

[0080] Figure 6 A schematic block diagram of an example device 600 that can be used to implement embodiments of the present disclosure is shown. For example, such as Figure 1 The storage system 100 shown can be implemented by device 600. For example... Figure 6As shown, device 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) 602 or loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. CPU 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0081] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0082] The various processes and handling described above, such as methods 200, 300, and / or 400, can be executed by processing unit 601. For example, in some embodiments, methods 200, 300, and / or 400 can be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by CPU 601, one or more actions of methods 200, 300, and / or 400 described above can be performed.

[0083] This disclosure can be a method, apparatus, system, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of this disclosure.

[0084] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0085] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0086] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0087] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0088] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0089] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0090] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0091] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method of managing a storage system by a processing unit, comprising: identifying values of a plurality of attribute parameters of a storage device in the storage system; upon determining that a failure of the storage device will occur based on the values of the plurality of attribute parameters, determining, by the processing unit, whether the failure will no longer occur based on reference values of attribute parameters in a first set of the plurality of attribute parameters and current values of attribute parameters in a second set of the plurality of attribute parameters; upon determining that the failure will occur again, updating the first set and determining a cause of the failure based on the updated first set; upon determining that the failure will no longer occur, determining the cause of the failure based on the attribute parameters in the first set; and outputting an indication associated with the cause of the failure.

2. The method of claim 1, further comprising: obtaining the values of the plurality of attribute parameters of the storage device, the plurality of attribute parameters including external attribute parameters associated with a usage environment of the storage device and internal attribute parameters associated with internal operations of the storage device.

3. The method of claim 2, wherein determining a cause of the failure comprises: if the external attribute parameters are included in the first set, determining a cause of the failure includes an external cause associated with a usage environment of the storage device; and if the internal attribute parameters are included in the first set, determining a cause of the failure includes an internal cause associated with internal operations of the storage device.

4. The method of claim 2, further comprising: determining the first set from the plurality of attribute parameters, the first set including at least one of the external attribute parameters; and determining the second set from attribute parameters in the plurality of attribute parameters other than the attribute parameters included in the first set.

5. The method of claim 2, wherein the external attribute parameters include at least one of: a temperature of the storage device, a number of connection resets of a cable of the storage device, and a number of connection failures of the cable; and wherein the internal attribute parameters include at least one of: a power-on time of the storage device, a number of power cycles of the storage device, a number of read / write errors of the storage device, a number of sector reallocations of the storage device, and a number of drive failures of the storage device.

6. The method of claim 1, further comprising: if it is determined that the failure will occur again, updating the first set by moving at least one attribute parameter from the second set to the first set; and determining a cause of the failure based on at least the updated first set.

7. The method of claim 1, further comprising: if it is determined that the failure will occur again, updating the first set by moving at least one attribute parameter in the first set to the second set; and determining a cause of the failure based on at least the updated first set.

8. The method of claim 1, further comprising: ​ ​ ​ ​ ​ if it is determined that the cause includes an external cause associated with a usage environment of the storage device, providing an indication that the usage environment of the storage device is to be checked; and if it is determined that the cause includes only an internal cause associated with an internal operation of the storage device, providing an indication that the storage device is to be removed.

9. The method of claim 1, further comprising: determining the first set from the plurality of attribute parameters based on historical failure information of the storage device, the historical failure information including a failure rate associated with each of the plurality of attribute parameters, the first set including attribute parameters having the highest failure rates; and and determining the second set from attribute parameters of the plurality of attribute parameters other than the attribute parameters included in the first set.

10. An electronic device, comprising: at least one processor; and at least one memory coupled to the at least one processor and storing instructions which, when executed by the at least one processor, cause the device to perform acts comprising: identifying values of a plurality of attribute parameters of a storage device in a storage system; upon determining that a failure of the storage device will occur based on the values of the plurality of attribute parameters, determining, by the at least one processor, whether the failure will no longer occur based on reference values of attribute parameters in a first set of the plurality of attribute parameters and current values of attribute parameters in a second set of the plurality of attribute parameters; upon determining that the failure will occur again, updating the first set and determining a cause of the failure based on the updated first set; upon determining that the failure will no longer occur, determining the cause of the failure based on the attribute parameters in the first set; and outputting an indication associated with the cause of the failure.

11. The electronic device of claim 10, wherein the acts further comprise: obtaining the values of the plurality of attribute parameters of the storage device, the plurality of attribute parameters including external attribute parameters associated with a usage environment of the storage device and internal attribute parameters associated with an internal operation of the storage device.

12. The electronic device of claim 11, wherein determining a cause of the failure comprises: if the external attribute parameters are included in the first set, determining that the cause of the failure includes an external cause associated with a usage environment of the storage device; and if the internal attribute parameters are included in the first set, determining that the cause of the failure includes an internal cause associated with an internal operation of the storage device.

13. The electronic device of claim 11, wherein the acts further comprise: determining the first set from the plurality of attribute parameters, the first set including at least one of the external attribute parameters; and determining the second set from attribute parameters of the plurality of attribute parameters other than the attribute parameters included in the first set.

14. The electronic device of claim 11, wherein the external attribute parameters include at least one of: a temperature of the storage device, a number of connection resets of a cable of the storage device, and a number of connection failures of the cable; and wherein the internal attribute parameters include at least one of: a power-on time of the storage device, a number of power cycles of the storage device, a number of read / write errors of the storage device, a number of sector reallocations of the storage device, and a number of drive failures of the storage device.

15. The electronic device of claim 10, wherein the actions further comprise: if it is determined that the failure will occur again, updating the first set by moving at least one attribute parameter from the second set to the first set; and determining a cause of the failure based at least on the updated first set.

16. The electronic device of claim 10, wherein the actions further comprise: if it is determined that the failure will occur again, updating the first set by moving at least one attribute parameter in the first set to the second set; and determining a cause of the failure based at least on the updated first set.

17. The electronic device of claim 10, wherein the actions further comprise: if it is determined that the cause includes an external cause associated with a usage environment of the storage device, providing an indication that the usage environment of the storage device is to be inspected; and if it is determined that the cause includes only an internal cause associated with internal operations of the storage device, providing an indication that the storage device is to be removed.

18. The electronic device of claim 10, wherein the actions further comprise: determining the first set from the plurality of attribute parameters based on historical failure information of the storage device, the historical failure information including a failure rate associated with each of the plurality of attribute parameters, the first set including attribute parameters having the highest failure rates; and determining the second set from attribute parameters of the plurality of attribute parameters other than the attribute parameters included in the first set.

19. A non-transitory computer readable medium and comprising machine executable instructions that, when executed by a processor, cause the processor to perform actions comprising: identifying values of a plurality of attribute parameters of a storage device in a storage system; upon determining, based on the values of the plurality of attribute parameters, that the storage device will experience a failure, determining, by the processor, whether the failure will not occur again based on reference values of attribute parameters in a first set of the plurality of attribute parameters and current values of attribute parameters in a second set of the plurality of attribute parameters; upon determining that the failure will occur again, updating the first set and determining a cause of the failure based on the updated first set; upon determining that the failure will not occur again, determining the cause of the failure based on the attribute parameters in the first set; and outputting an indication associated with the cause of the failure.

20. The computer-readable medium of claim 19, wherein the acts further comprise: obtaining the values of the plurality of attribute parameters of the storage device, the plurality of attribute parameters including external attribute parameters associated with a usage environment of the storage device and internal attribute parameters associated with internal operations of the storage device.

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