# Patent

## Title
System and method for predicting future disk failures

## Abstract

According to one embodiment, a diagnostic parameter of a target storage disk of a storage system is received. A first weight factor is determined from a first quantile distribution representation based on a value of the diagnostic parameter, where the first quantile distribution representation represents a quantile distribution of values of the diagnostic parameter of a set of known failed disks. A second weight factor is determined from a second quantile distribution representation based on the value of the diagnostic parameter, where the second quantile distribution representation represents a quantile distribution of values of the diagnostic parameter of a set of known working disks. A third weight factor is determined for the diagnostic parameter for the target storage disk based on the first weight factor and the second weight factor and a probability of potential disk failure of the target storage disk is determined based on the third weight factor.

## Background
RELATED APPLICATIONS
This application is a continuation-in-part (CIP) of co-pending U.S. patent application Ser. No. 14/037,199, filed Sep. 25, 2013, which is incorporated by reference herein in its entirety.


FIELD OF THE INVENTION
Embodiments of the present invention relate generally to data storage systems. More particularly, embodiments of the invention relate to predicting future disk failures.
BACKGROUND
Data storage utilization is continually increasing, causing the proliferation of storage systems in data centers. Monitoring and managing these systems require increasing amounts of human resources. Information technology (IT) organizations often operate reactively, taking action only when systems reach capacity or fail, at which point performance degradation or failure has already occurred. Hard disk failures fall into one of two basic classes: predictable failures and unpredictable failures. Predictable failures result from slow processes such as mechanical wear and gradual degradation of storage surfaces. Monitoring can determine when such failures are becoming more likely. Unpredictable failures happen suddenly and without warning. They range from electronic components becoming defective to a sudden mechanical failure (perhaps due to improper handling).
Self-Monitoring, Analysis and Reporting Technology (S.M.A.R.T., or simply written as SMART) is a monitoring system for computer hard disk drives to detect and report on various indicators of reliability, in the hope of anticipating failures. When a failure is anticipated by S.M.A.R.T., the user may choose to replace the drive to avoid unexpected outage and data loss. The manufacturer may be able to use the S.M.A.R.T. data to discover where faults lie and prevent them from recurring in future drive designs. However, not all of the S.M.A.R.T. attributes can consistently provide reliable indications of possible disk failures. The S.M.A.R.T. attributes tend to vary and they may have different interpretation from one hard disk vendor or configuration to another. There has been a lack of reliable mechanism to determine which of the S.M.A.R.T. attributes to be the best disk failure indicator, as well as the efficient ways to predict single disk failure or multi-disk failures in a redundant array independent disks (RAID) environment. Further prior techniques do a poor job monitoring the RAID overall health status. The discrepancy between a working disk and impending failure is not obvious.



BRIEF DESCRIPTION OF THE DRAWINGS
Embodiments of the invention are illustrated by way of example and not limitation in the figures of the accompanying drawings in which like references indicate similar elements.
FIG. 1 is a block diagram illustrating a storage system according to one embodiment of the invention.
FIG. 2 is a flow diagram illustrating a method for predicting disk failures according to one embodiment of the invention.
FIG. 3 is a diagram illustrating certain quantile distribution representations which may be used with an embodiment of the invention.
FIG. 4 is a flow diagram illustrating a method for determining a disk failure indicator according to one embodiment of the invention.
FIG. 5 is a flow diagram illustrating a method for predicting disk failures of a single disk according to one embodiment of the invention.
FIG. 6 is a diagram illustrating a process for determining an optimal threshold for predicting disk failures according to one embodiment of the invention.
FIG. 7 is a flow diagram illustrating a method for determining an optimal threshold for predicting disk failures according to one embodiment of the invention.
FIGS. 8A and 8B are diagrams illustrating a process for predicting multi-disk failures according to one embodiment of the invention.
FIG. 9 is a flow diagram illustrating a method for predicting multiple disk failures according to one embodiment of the invention.
FIG. 10 is a block diagram illustrating a deduplicated storage system according to one embodiment of the invention.
FIG. 11 is a diagram illustrating a process for predicting disk failures using quantile distribution techniques according to one embodiment of the invention.
FIG. 12 is a flow diagram illustrating a method for predicting disk failures according to one embodiment of the invention.
FIG. 13 is a flow diagram illustrating a method for predicting disk failures according to another embodiment of the invention.



DETAILED DESCRIPTION
Various embodiments and aspects of the inventions will be described with reference to details discussed below, and the accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative of the invention and are not to be construed as limiting the invention. Numerous specific details are described to provide a thorough understanding of various embodiments of the present invention. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments of the present inventions.
Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in conjunction with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification do not necessarily all refer to the same embodiment.
According to one aspect of the invention, a method using quantile distribution techniques is provided to select one or more of diagnostic parameters, such as S.M.A.R.T. attributes and/or small computer system interface (SCSI) disk return codes, that are collected from the disks to be the most reliable disk failure indicator or indicators for a given certain storage configuration or environment (e.g., target storage system or disks). According to one embodiment, diagnostic parameters are collected from a set of historically known failed disks and known working disks of a particular storage system or configuration (e.g., a storage system or systems deployed at a particular enterprise or organization). The diagnostic parameters of the known failed disks and known working disks are analyzed or trained in view each other to determine which of the those diagnostic parameters can be used to most efficiently or consistently distinguish or identify a failed disk or working disk from the set of disks.
According to one embodiment, for each of the diagnostic parameters, a first quantile distribution representation and a second quantile distribution representation (e.g., quantile distribution graphs or curves) are generated for the known failed disks and the known work disks, respectively. For each of the diagnostic parameters, the first and second quantile distribution representations are analyzed to determine its maximum difference value between the first and second quantile distribution representations. The maximum difference values of all the diagnostic parameters are then compared to each other to select one or more of the diagnostic parameters that are largest amongst all or above certain predetermined threshold as one or more disk failure indicators, which can consistently identify a potential failed disk from a group of disks.
According to another aspect of the invention, the selected disk failure indicator may be used to predict the disk failure probability of a particular disk. According to one embodiment, values of a predetermined diagnostic parameter (representing the selected disk failure indicator, such as the reallocated sector count as part of S.M.A.R.T. attributes or the medium error as part of SCSI disk return codes) are collected from a set of known failed disks and known working disks associated with a target storage system. A first and second quantile distribution graphs of the values of the predetermined diagnostic parameter for the known failed disks and known working disks are generated, respectively, in view of a range of values of the known failed disks. An optimal threshold of the values of the predetermined diagnostic parameter is determined by comparing the first and second quantile distribution graphs. In one embodiment, the optimal threshold is identified at a position where the difference between the first and second quantile distribution graphs reaches the maximum. The optimal threshold is then utilized to indicate or predict whether a particular target disk may have a higher probability of disk failure, for example, by comparing the value of the predetermined diagnostic obtained from the target disk against the optimal threshold.
According to another aspect of the invention, the selected disk failure indicator may be used to predict the disk failure probability of multiple disks in a RAID configuration. According to one embodiment, values of a predetermined diagnostic parameter (representing the selected disk failure indicator, such as the reallocated sector count as part of S.M.A.R.T. attributes) are collected from a set of known failed disks associated with a target storage system. A quantile distribution graph is generated for the values in view of percentiles (e.g., 10%, 20%, . . . , 100%) of a number of known failed disks involved in the quantile distribution. Subsequently, when values of the predetermined diagnostic parameter are collected from a set of target storage disks, the collected values are applied to the quantile distribution graph to determine their respective percentiles of the target disks. Each percentile represents a probability of the corresponding disk failure. The individual disk failure probabilities are then used to calculate the probability of disk failures of two or more of the target disks.
According to another aspect of the invention, in addition to the straightforward approach that uses the values of certain diagnostic parameters, a weight vector algorithm is utilized that takes into account all errors to recognize soon-to-fail disks. The weight vector algorithm evaluates the error degree of every disk fault metric, assigns weights accordingly, and consolidates all weights with Euclidean length to present the failure probability. In particular, a disk is provided with a weight or score indicating different error levels instead of only predicting whether the disk will fail. The weight or score enables the system to adjust a failure threshold by monitoring every disk's score in the same RAID group. For example, if there are multiple disks with comparatively high scores indicating that they may be simultaneously approaching their end of lifespan, the threshold used to categorize whether a particular disk may fail may be lowered to fail the disks more aggressively, which may reduce the probability of potential multiple disk failures and improve the overall system reliability.
Specifically, according to one embodiment of the invention, when a first and a second quantile distribution representations are created representing a set of known failed disks and a set of known working disks, respectively, a default weight factor or score is assigned to each of the deciles (or intervals) evenly distributed over the range represented by the quantile distribution representations. A default weight or score of a particular decile represents a degree of potential disk failure of a particular disk when the value of the diagnostic parameter of that particular disk falls within the corresponding decile. Subsequently, when a diagnostic parameter (e.g., reallocated sector count) of a target storage disk is received for the purpose of predicting disk failures of the target storage disk, a lookup operation is performed on both of the first quantile distribution representation (e.g., failed disk quantile distribution) and the second quantile distribution representation (e.g., working disk quantile distribution) based on the value of the diagnostic parameter to determine the diagnostic parameter having that particular value is represented by both the first and second quantile distribution representations (e.g., whether a quantile distribution contains the value of the diagnostic parameter).
In one embodiment, if the value of the diagnostic parameter is represented by both the first and second quantile distribution representations, a first weight factor is determined from the first quantile distribution representation based on the value of the diagnostic parameter and a second weight factor is determined from second quantile distribution representation based on the value of the diagnostic parameter. A third weight factor is then determined based on the first and second weight factors and a disk failure probability is then determined based on the third weight factor. If the value of the diagnostic parameter is only represented by the first quantile distribution representation (and not by the second quantile distribution representation), the first weight factor is utilized as the third weight factor.
FIG. 1 is a block diagram illustrating a storage system according to one embodiment of the invention. Referring to FIG. 1, system 100 includes, but is not limited to, one or more client systems 101-102 communicatively coupled to one or more storage systems 104 over network 103. Clients 101-102 may be any type of clients such as a server, a personal computer (e.g., desktops, laptops, and tablets), a “thin” client, a personal digital assistant (PDA), a Web enabled appliance, a gaming device, a media player, or a mobile phone (e.g., Smartphone), etc. Network 103 may be any type of networks such as a local area network (LAN), a wide area network (WAN) such as Internet, or a combination thereof.
Storage system 104 may include any type of server or cluster of servers. For example, storage system 104 may be a storage server used for any of various different purposes, such as to provide multiple users with access to shared data and/or to back up mission critical data. In one embodiment, storage system 104 includes, but is not limited to, backup engine 106, optional deduplication storage engine 107, and one or more storage units 108-109 communicatively coupled to each other. Storage units 108-109 may be implemented locally (e.g., single node operating environment) or remotely (e.g., multi-node operating environment) via interconnect 120, which may be a bus and/or a network.
In response to a data file to be stored in storage units 108-109, deduplication storage engine 107 is configured to segment the data file into multiple chunks (also referred to as segments) according to a variety of segmentation policies or rules. Deduplication storage engine 107 may choose not to store a chunk in a storage unit if the chunk has been previously stored in the storage unit. In the event that deduplication storage engine 107 chooses not to store the chunk in the storage unit, it stores metadata enabling the reconstruction of the file using the previously stored chunk. As a result, chunks of data files are stored in a deduplicated manner, either within each of storage units 108-109 or across at least some of storage units 108-109. The metadata, such as metadata 110-111, may be stored in at least some of storage units 108-109, such that files can be accessed independent of another storage unit. Metadata of each storage unit includes enough information to provide access to the files it contains.
According to one embodiment, storage system 104 further includes an operation manager 105 to manage and monitor operations performed by storage system 104, including periodically collecting and transmitting operating diagnostic data to a remote device such as management system 150 over network 103. In this example as shown in FIG. 1, storage system 104 may be located at a client site and utilized by a client such as an enterprise or corporation, where the storage system 104 may be provided by a storage provider or vendor such as EMC® Corporation. In one embodiment, operation manager 105 periodically collects operating statistics concerning operations of storage units 108-109 and transmits diagnostic data representing at least some of the operating statistics to management system 150, where management system 150 is associated with a storage provider or vendor that provides storage system 104 to a client. For example, management system 150 may be operated or owned by the storage provider or alternatively, it may be operated by a third-party vendor on behalf of the storage provider. In one embodiment, the diagnostic data may include diagnostic parameters such as those defined by S.M.A.R.T. specification and/or those defined as part of the SCSI disk return codes, which may be collected from the storage system 104. For example, operation manager 105 may include or communicate with an S.M.A.R.T. tool or software configured to monitor operations of storage units 108-109. Each of the storage units 108-109 may be implemented one or more individual disks or alternatively, a RAID array of disks.
Note that storage system 104 may represent a group or cluster of individual storage systems, where operation manager 105 of each storage system may be equipped with a “phone-home” functionality that may periodically transmit operating status of the respective storage system, including the diagnostic parameters (e.g., S.M.A.R.T. attributes and SCSI return codes) of the associated storage disks, to a centralized or distributed entity, such as management server 150 or dedicated data collection entity 160 (e.g., a third-party data collection agent).
According to one embodiment, management system 150 includes a data collector 151, disk failure predictor 152, and analysis module 153. Data collector 151 is employed to communicate with operation manager 105 of storage system(s) 104 to collect diagnostic data concerning operating statuses of storage units 108-109, as well as storage system 104 in general. Note that although one storage system is shown in FIG. 1, data collector 151 may communicate with multiple operation managers of multiple storage systems to collect diagnostic data concerning the respective storage systems, which may be located at the same or different geographical locations (e.g., same or different client sites). For example, management system 150 may be a centralized management server or cluster of servers (e.g., in the cloud) for single or multiple clients or customers.
The collected diagnostic data is stored in a storage device as part of diagnostic logs 154. In one embodiment, diagnostic data 154 includes diagnostic parameters collected from various storage systems such as storage system 104. The diagnostic parameters may be those attributes (e.g., reallocated sector, pending sector, uncorrectable sector, etc.) defined by S.M.A.R.T. Alternatively, diagnostic parameters may be those from the SCSI return codes (e.g., medium error, timeout, connection error, data error, etc.). In one embodiment, analysis module 153 is to perform an analysis on the diagnostic data 154 such as determining which of the diagnostic parameters can be used as the best disk failure indicator(s). Disk failure predictor 152 is configured to predict, using the disk failure indicator(s), which one or more of the disks of storage units 108-109 of storage system 104 have a higher probability of disk failures.
According to one embodiment of the invention, analysis module 153 utilizes quantile distribution techniques to select one or more of diagnostic parameters, such as S.M.A.R.T. attributes, SCSI return codes, that are collected from the disks of storage units 108-109 to be the most reliable disk failure indicator or indicators for a given certain storage configuration or environment (e.g., target storage system or disks). According to one embodiment, diagnostic parameters are collected, for example, by data collector 151 or server 160, from a set of historically known failed disks and known working disks of a particular storage system or configuration (e.g., a storage system or systems deployed at a particular enterprise or organization). The diagnostic parameters of the known failed disks and known working disks are analyzed or trained by analysis module 153 in view each other to determine which of the those diagnostic parameters can be used to most efficiently or consistently distinguish or identify a failed disk or working disk from the set of disks.
According to one embodiment, for each of the diagnostic parameters, a first quantile distribution representation and a second quantile distribution representation (e.g., quantile distribution graphs or curves) are generated for the known failed disks and the known work disks, respectively. For each of the diagnostic parameters, the first and second quantile distribution representations are analyzed to determine its maximum difference value between the first and second quantile distribution representations. The maximum difference values of all the diagnostic parameters are then compared to each other to select one or more of the diagnostic parameters that are largest amongst all or above certain predetermined threshold as one or more disk failure indicators, which can consistently identify a potential failed disk from a group of disks.
According to another embodiment of the invention, the selected disk failure indicator may be used by disk failure predictor 152 to predict the disk failure probability of a particular disk. According to one embodiment, values of a predetermined diagnostic parameter (representing the selected disk failure indicator, such as the reallocated sector count as part of S.M.A.R.T. attributes) are collected (e.g., by collector 151 or server 160) from a set of known failed disks and known working disks associated with a target storage system. A first and second quantile distribution graphs of the values of the predetermined diagnostic parameter for the known failed disks and known working disks are generated, respectively, in view of a range of values of the known failed disks. An optimal threshold of the value of the predetermined diagnostic parameter is determined by comparing the first and second quantile distribution graphs. In one embodiment, the optimal threshold is identified at a position where the difference between the first and second quantile distribution graphs reaches the maximum. The optimal threshold is then utilized by disk failure predictor 152 to indicate or predict whether a particular target disk may have a higher probability of disk failure, for example, by comparing the value of the predetermined diagnostic obtained from the target disk against the optimal threshold.
According to a further embodiment of the invention, the selected disk failure indicator may be used to predict the disk failure probability of multiple disks in a RAID configuration. According to one embodiment, values of a predetermined diagnostic parameter (representing the selected disk failure indicator, such as the reallocated sector count as part of S.M.A.R.T. attributes) are collected (e.g., by data collector 151 or server 160) from a set of known failed disks associated with a target storage system. A quantile distribution graph is generated (e.g., by analysis module 153 or disk failure predictor 152) for the values in view of percentiles (e.g., 10%, 20%, . . . , 100%) of a number of known failed disks involved in the quantile distribution. Subsequently, when values of the predetermined diagnostic parameter are collected from a set of target storage disks, the collected values are applied (e.g., by disk failure predictor 152) to the quantile distribution graph to determine their respective percentiles of the target disks. Each percentile represents a probability of the corresponding disk failure. The individual disk failure probabilities are then used (e.g., by disk failure predictor 152) to calculate the probability of disk failures of two or more of the target disks.
According to another embodiment of the invention, in addition to the straightforward approach that uses the values of certain diagnostic parameters, a weight vector algorithm is utilized that takes into account all errors to recognize soon-to-fail disks. The weight vector algorithm evaluates the error degree of every disk fault metric, assigns weights accordingly, and consolidates all weights, for example, using Euclidean length, to present the failure probability. In particular, a disk is provided with a weight or score indicating different error levels instead of only predicting whether the disk will fail. The weight or score enables the system to adjust a failure threshold by monitoring every disk's score in the same RAID group. For example, if there are multiple disks with comparatively high scores indicating that they may be simultaneously approaching their end of lifespan, the threshold used to categorize whether a particular disk may fail may be lowered to fail the disks more aggressively, which may reduce the probability of potential multiple disk failures and improve the overall system reliability.
Specifically, according to one embodiment of the invention, when a first and a second quantile distribution representations are created, by analysis module 153, representing a set of known failed disks and a set of known working disks, respectively, a default weight factor or score is assigned to each of the deciles (or intervals such as percentiles) evenly distributed over the range represented by the quantile distribution representations. A default weight or score of a particular decile represents a degree of potential disk failure of a particular disk when the value of the diagnostic parameter of that particular disk falls within the corresponding decile. Subsequently, when a diagnostic parameter (e.g., reallocated sector count) of a target storage disk is received for the purpose of predicting disk failures of the target storage disk, disk failure predictor 152 performs a lookup operation on both of the first quantile distribution representation (e.g., failed disk quartile distribution) and the second quantile distribution representation (e.g., working disk quantile distribution) corresponding to the diagnostic parameter based on the value of the diagnostic parameter to determine the diagnostic parameter having that particular value is represented by both the first and second quantile distribution representations (e.g., whether a quantile distribution contains the value of the diagnostic parameter).
In one embodiment, if the value of the diagnostic parameter is represented by both the first and second quantile distribution representations (e.g., the particular value of the diagnostic parameter in question is found in both quantile distribution representations), disk failure predictor 152 determines a first weight factor from the first quantile distribution representation based on the value of the diagnostic parameter and determines a second weight factor from second quantile distribution representation based on the value of the diagnostic parameter. A third weight factor is then determined based on the first and second weight factors and a disk failure probability is then determined based on the third weight factor. If the value of the diagnostic parameter is only represented by the first quantile distribution representation (and not by the second quantile distribution representation), the first weight factor is utilized as the third weight factor.
FIG. 2 is a flow diagram illustrating a method for predicting disk failures according to one embodiment of the invention. Method 200 may be performed by processing logic which may include software, hardware, or a combination thereof. For example, method 200 may be performed by management system 150 of FIG. 1. Referring to FIG. 2, at block 201, processing logic collects diagnostic parameters (e.g., at least a portion of S.M.A.R.T. attributes) from a set of historically known failed disks and known working disks of one or more storage systems (e.g., deduplicated backup storage systems). At block 202, processing logic performs an analysis on the collected diagnostic parameters to identify one or more of the diagnostic parameters as one or more disk failure indicators that can be used to consistently or reliably indicate future disk failures. In one embodiment, processing logic utilizes quantile distribution techniques to select one or more of the diagnostic parameters as one or more disk failure indicators. At block 203, processing logic monitors and collects diagnostic parameters from one or more target disks of a target storage system. At block 204, processing logic predicts, using the selected disk failure indicator(s), the probability of disk failures of the one or more disks of the target storage system based on the collected diagnostic parameters. The disk failure indicator(s) can be utilized to predict the probability of single-disk configuration or multi-disk configuration such as a RAID configuration.
As described above, a disk failure indicator can be identified and selected from a set of diagnostic parameters, such as the S.M.A.R.T. attributes or SCISI return codes collected from a set of known failed disks and working disks, using quantile distribution analysis. Quantiles are points taken at regular intervals from the cumulative distribution function of a random variable. Dividing ordered data into q essentially equal-sized data subsets is the motivation for q-quantiles; the quantiles are the data values marking the boundaries between consecutive subsets. Put another way, the kth q-quantile for a random variable is the value x such that the probability that the random variable will be less than x is at most k/q and the probability that the random variable will be more than x is at most (q−k)/q=1−(k/q). There are q−1 of the q-quantiles, one for each integer k satisfying 0<k<q.
As described above, in order to predict future disk failures, a disk failure indicator that can consistently or reliably indicate future disk failures must be identified. According to one embodiment, certain diagnostic parameters, which may be selected from at least some of the S.M.A.R.T. attributes, are collected from a set of previously known failed disks (e.g., q failed disks) and a set of known working disks (e.g., q working disks). For each of the collected diagnostic parameters, a quantile distribution representation (also referred to as a quantile distribution graph or curve) is generated.
For example, for a set of known working disks (e.g., a predetermined number of working disks), values of a particular diagnostic parameter are collected from the set of working disks (for example, via a diagnostic or monitoring tool such as S.M.A.R.T. tool):

    
    
        Working Set=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

In this example, it is assumed there are 10 disks in each set. The values of the particular diagnostic parameter are stored in an array and sorted according to a predetermined order, in this example, an ascending order, from small to large.

    
    


Similarly, values of the same diagnostic parameter are collected from the set of known failed disks:

    
    
        Failed Set=[10, 50, 100, 150, 200, 250, 300, 350, 400, 450]

Similar to the working set, the values of the same diagnostic parameter for the failed disks are stored in an array and sorted according to the same order as the working set (e.g., ascending order).

    
    


These two arrays are then plotted against the percentiles of number of the disks involved (e.g., 10 disks here) to generate the quantile distribution representations or graphs for both the known failed disks and known working disks, as shown in FIG. 3. In the example as shown in FIG. 3, a first quantile distribution representation 301 is generated representing the known working disks while a second quantile distribution representation 302 is generated representing the known failed disks. Referring to FIG. 3, two quantile distribution representations are then analyzed to determine the maximum or most significant difference value between the two quantile distribution representations 301-302. In the example as shown in FIG. 3, the maximum difference value 303 can be identified at the 100% location, where the maximum difference value is (450−100)=350.
Similar processes are performed for all of the diagnostic parameters that have been selected as disk failure indicator candidates. The maximum difference values associated with the diagnostic parameters are compared with each other. In one embodiment, one of the diagnostic parameter associated with the largest difference value amongst all may be selected as a disk failure indicator. In one embodiment, a distance between two quantile curves may be evaluated by calculating the area size between the two curves. This takes into account all the points on the curves rather than one specific point. The parameter that has the biggest area size between two curve is the best disk failure indicator.
FIG. 4 is a flow diagram illustrating a method for determining a disk failure indicator according to one embodiment of the invention. Method 400 may be performed by processing logic which may include software, hardware, or a combination thereof. For example, method 400 may be performed by analysis module 153 of FIG. 1. Referring to FIG. 4, at block 401, processing logic receives diagnostic parameters (e.g., certain selected S.M.A.R.T. attributes) of a set of known or predetermined failed disks and working disks associated with one or more storage systems (e.g., backup storage systems). At block 402, for each of the diagnostic parameters, processing logic generates a first quantile distribution representation or graph for the set of working disks. At block 403, processing logic generates a second quantile distribution representation for the set of failed disks. Once all of the quantile distribution representations for all of the diagnostic parameters have been generated, at block 404, the first and second quantile representations of each diagnostic parameter are compared to select one or more of the diagnostic parameters as one or more disk failure indicators. Specifically, for each diagnostic parameter, a maximum or most significant difference value is determined, as described above, between the two quantile distribution representations that represent a set of known working disks and a set of known failed disks, respectively. All the maximum difference values of all diagnostic parameters are compared to select the largest difference value amongst all to be a disk failure indicator. Alternatively, multiple diagnostic parameters whose maximum difference values greater than a predetermined threshold may be selected as multiple disk failure indicators.
According to one embodiment of the invention, once a disk failure indicator has been selected, the disk failure indicator may be used to predict the disk failure probability of a particular disk, referred to as a target disk of a target storage system. A value of a particular diagnostic parameter (e.g., predetermined diagnostic parameter representing the determined disk failure indicator) is obtained from a target disk, for example, using a diagnostic tool such as an S.M.A.R.T. compatible monitoring tool. The value of the predetermined diagnostic parameter is then compared to a predetermined threshold that is associated the predetermined diagnostic parameter to determine a probability of disk failures for the target disk. For example, if the value of the predetermined diagnostic parameter is greater than the predetermined threshold, the target disk is considered having a higher risk of future disk failures; otherwise, the target disk is considered as a working disk with a lower risk of disk failures. The value that is used to compare the predetermined threshold may be an averaged value or a mathematical representation or function of many values collected from the target disk over a predetermined period of time. In one embodiment, the predetermined threshold may be an optimal threshold that is determined based on the balances between the accuracy of detecting future disk failures and the risk of false positive detection of the target disk.
FIG. 5 is a flow diagram illustrating a method for predicting disk failures of a single disk according to one embodiment of the invention. Method 500 may be performed by processing logic which may include software, hardware, or a combination thereof. For example, method 400 may be performed by disk failure predictor 152 of FIG. 1. Referring to FIG. 5, at block 501, processing logic selects a diagnostic parameter as a disk failure indicator based on operating status of a set of known working disks and known failed disks. The disk failure indicator may be selected using the techniques as described above. At block 502, processing logic determines an optimal threshold for the diagnostic parameter to categorize whether a particular disk (e.g., a target disk) has a higher risk of disk failures. At block 503, processing logic monitors or receives values of the selected diagnostic parameter from one or more disks of a target storage system. For each of the disks, at block 504, the values of diagnostic parameter of the disks are compared with the optimal threshold. At block 505, processing logic categorizes a disk having a higher risk of disk failure if the value of the parameter of that disk is greater than the optimal threshold.
As illustrated above, the optimal threshold may determine the accuracy of the prediction. A too-high or too-low threshold may lead to missing certain potential failed disks and/or false positive working disks. In one embodiment, values of a predetermined diagnostic parameter (representing the selected disk failure indicator, such as the reallocated sector count as part of S.M.A.R.T. attributes) are collected from a set of known failed disks and known working disks associated with a target storage system. A first and second quantile distribution graphs of the values of the predetermined diagnostic parameter for the known failed disks and known working disks are generated, respectively, in view of a range of values of the known failed disks. An optimal threshold of the values of the predetermined diagnostic parameter is determined by comparing the first and second quantile distribution graphs. In one embodiment, the optimal threshold is identified at a location where the difference between the first and second quantile distribution graphs reaches the maximum. The optimal threshold is then utilized to indicate or predict whether a particular target disk may have a higher probability of disk failure, for example, by comparing the value of the predetermined diagnostic obtained from the target disk against the optimal threshold.
In one embodiment, all historical failed disks and working disks are analyzed. The prediction system calculates how many failed disks would be captured by a certain threshold (referred to as an accuracy prediction) and how many working disks would be captured by this threshold (referred to as false positive). A first curve or first set of data points of the accuracy prediction and a second curve or second set of data points of the false positive as a function of different thresholds (e.g., threshold candidates) are generated, respectively. The threshold which has the biggest difference between the first and second curves or data points may be selected as the optimal threshold.
Assuming there are five failed disks with a selected diagnostic parameter (e.g., selected disk failure indicator), in this example RAS, having values of [30, 50, 50, 50, 60] (which may be sorted in a predetermined order such as ascending order). Five working disks with RAS=[10, 20, 30, 40, 40]. In one embodiment, the range of values obtained from the known failed disks may be utilized as a set of threshold candidates. For example, if the RAS threshold is set to be 30, the prediction algorithm will capture all the disks having RAS>=30 and regard them as impending failures. So it can capture 5 failed disks and 3 working disks. So prediction accuracy=5, false positive=3. Likewise, if the threshold is set to be 40, prediction accuracy=4 and false positive=2. If the threshold is set to be 50, accuracy=4 and false positive=0. If the threshold is set to be 60, accuracy=1 and false positive=0, as shown in FIG. 6. From the curves as shown in FIG. 6, the largest difference in values between the prediction accuracy curve 601 and the false positive curve 602 appears at the threshold of 50. As a result, the threshold of 50 may be selected as the optimal threshold for predicting disk failures of a single target disk.
FIG. 7 is a flow diagram illustrating a method for determining an optimal threshold for predicting disk failures according to one embodiment of the invention. Method 700 may be performed by processing logic which may include software, hardware, or a combination thereof. For example, method 700 may be performed by disk failure predictor 152 of FIG. 1. Referring to FIG. 7, at block 701, processing logic collects or receives values of a diagnostic parameter associated with a set of known failed disks and a set of working disks. The diagnostic parameter may have been selected as a disk failure indicator amongst many diagnostic parameters such as those listed as part of S.M.A.R.T. attributes or SCISI return codes. The set of failed disks and working disks may be associated with a target storage system of which the future disk failures are to be predicted. At block 702, processing logic generates a first set of data points representing a number of known failed disks that would have been captured or identified against different thresholds or threshold candidates. The range of the thresholds may be determined by the range of the values of the diagnostic parameters of the set of failed disks. At block 703, a second set of data points is generated, representing a number of the known working disks that would have been captured against the different thresholds or threshold candidates. At block 704, the first set and second set of data points are compared in view of the different thresholds. At block 705, one of the thresholds or threshold candidates may be selected as an optimal threshold that has the maximum difference between the corresponding values of the first and second sets of data points.
According to another aspect of the invention, a selected disk failure indicator may also be used to predict the disk failure probability of multiple disks in a RAID configuration. According to one embodiment, values of a predetermined diagnostic parameter (representing the selected disk failure indicator, such as the reallocated sector count as part of S.M.A.R.T. attributes) are collected from a set of known failed disks associated with a target storage system. A quantile distribution graph is generated for the values in view of percentiles (e.g., 10%, 20%, . . . , 100%) of a number of known failed disks involved in the quantile distribution. Subsequently, when values of the predetermined diagnostic parameter are collected from a set of target storage disks, the collected values are applied to the quantile distribution graph to determine their respective percentiles of the known failed disks. Each percentile represents a probability of the corresponding disk failure. The individual disk failure probabilities are then used to calculate the probability of disk failures of two or more of the target disks.
In one embodiment, values of a predetermined parameter (e.g., selected disk failure indicator such as a reallocated sector count) are collected from a set of historically known failed disks. Assuming the number of RAS value is N, the N values of the predetermined parameter are stored in an array and sorted according to a predetermined order such as ascending order from smallest to biggest. A quantile distribution is generated based on the sorted numbers. For the purpose of illustrating only, it is assumed the selected disk failure indicator is RAS parameter. In one embodiment, the value of predetermined percentile interval, in this example, 10 percentile=the RAS value at the [N*0.1] offset of the sorted array. Similarly, the value of 0.X=the RAS value at the [N*0.x] offset of the sorted array.
For example, it is assumed there are 1000 RAS values corresponding to 1000 known failed disks. After sorting of the 1000 RAS values, it is assumed the sorted array contains: 100, 105, 160 . . . (the array length=1000). If the value of 105 positioned in the sorted array happens to be 100 (array length*0.1=1000*0.1) the quantile graph or curve at 0.1 should be 105. Similarly, if value of 230 positioned in the sorted array happens to be 200 (array length*0.2=1000*0.2), the quintile graph or curve at 0.2 should be 230. It is assumed that for a particular set of known failed disks, its quantile distribution graph is shown in FIG. 8A.
Once the quantile distribution graph has been established, according to one embodiment, it can be used to predict multiple-disk failure scenario, for example, by calculating the probability of individual disk failures and then determining the probability of two or more disks. For example, as shown in FIG. 8A, if a target disk's corresponding parameter value is around 230, its individual probability of disk failures is about 50%. Similarly, if a target disk's corresponding parameter value is around 450, its individual probability of disk failures is about 60%, etc.
For the purpose of illustration, it is assumed there are four disks having the predetermined parameter of [11, 30, 110, 240]. By applying these numbers into the quantile distribution graph as shown in FIG. 8A, for example, by looking up these numbers in the Y axis of the graph, we can obtain their respective failure probability numbers from the corresponding X axis of the graph. In this example, value 11 of the disk 1 falls between [0.2, 0.3]; value 30 of disk 2 falls between [0.3, 0.4]; value 110 of disk 3 falls between [0.4, 0.5]; and value 240 of disk 4 falls between [0.5, 0.6]. According to one embodiment, the probability will be selected as an upper bound of the range (although a lower bound can also be utilized). As a result, the individual failure probabilities of these four disks are [0.3, 0.4, 0.5, 0.6], respectively as shown in FIG. 8B.
From the individual failure probabilities of individual disks, their corresponding probabilities of working disks can be derived as P(work)=1=P(fail), as shown in FIG. 8B. In a RAID configuration, the RAID group failure can be defined as two or more disk failures in this example:

P(RAID group failure)=P(disk failure number>=2)=1−P(disk_failure_num=0)−P(disk_failure_num=1).

P(disk_failure_num=0)=P(disk1—w)*P(disk2—w)*P(disk3—w)*P(disk4—w).

P(disk_failure_num=1)=P(disk1_failure)*P(disk2—w)*P(disk3—w)*P(disk4—w)+P(disk1—w)*P(disk2_failure)*P(disk3—w)*P(disk4—w)+P(disk1—w)*P(disk2—w)*P(disk3_failure)*P(disk4—w)+P(disk1—w)*P(disk2—w)*P(disk3—w)*P(disk4_failure).

P(disk failure number=0) refers to the probability of no disk failure. P(disk failure number=1) refers to the probability of one disk failure. P(disk1_w), P(disk2_w), P(disk3_w), and P(disk4_w) refer to the probabilities of working disk for disk 1 to disk 4, respectively. P(disk1_failure), P(disk2_failure), P(disk3_failure), and P(disk4_failure) refer to the probabilities of disk failure for disk 1 to disk 4, respectively. Similarly, the probability of more than any number of disks can also be calculated. According to one embodiment, there are two tunable parameters: 1) the number of fail disks to be prevented, where the default number here is >=2 and 2) the number of disks in the RAID group (in this example, the number of disks is 3). Both numbers are adjustable based on different requirements and system settings.
FIG. 9 is a flow diagram illustrating a method for predicting multiple disk failures according to one embodiment of the invention. Method 900 can be performed by processing logic which may include software, hardware, or a combination thereof. For example, method 900 may be performed by disk failure predictor 152 of FIG. 1. Referring to FIG. 9, at block 901, processing logic builds a quantile distribution graph of a diagnostic parameter (e.g., selected disk failure indicator) from a set of known failed disks. At block 902, processing logic determines disk failure probability of disks by applying values of the diagnostic parameter collected from the target disks to the quantile distribution graph. At block 903, the disk failure probability of multiple disks is then calculated from the individual disk failure probability of the individual target disks.
As described above according to another embodiment of the invention, in addition to the straightforward approach that uses the values of certain diagnostic parameters, a weight vector algorithm is utilized that takes into account all errors to recognize soon-to-fail disks. The weight vector algorithm evaluates the error degree of every disk fault metric, assigns weights accordingly, and consolidates all weights, for example, using Euclidean length, to present the failure probability. In particular, a disk is provided with a weight or score indicating different error levels instead of only predicting whether the disk will fail. The weight or score enables the system to adjust a failure threshold by monitoring every disk's score in the same RAID group. For example, if there are multiple disks with comparatively high scores indicating that they may be simultaneously approaching their end of lifespan, the threshold used to categorize whether a particular disk may fail may be lowered to fail the disks more aggressively, which may reduce the probability of potential multiple disk failures and improve the overall system reliability.
Specifically, according to one embodiment of the invention, when a first and a second quantile distribution representations are created, for example, by analysis logic such as analysis module 153 of FIG. 1, representing a set of known failed disks and a set of known working disks, respectively, a default weight factor or score is assigned to each of the deciles (or intervals such as percentiles) evenly distributed over the range represented by the quantile distribution representations. A default weight or score of a particular interval or percentile represents a degree of potential disk failure of a particular disk when the value of the diagnostic parameter of that particular disk falls within the corresponding interval or percentile. Subsequently, when a diagnostic parameter (e.g., reallocated sector count) of a target storage disk is received for the purpose of predicting disk failures of the target storage disk, disk failure prediction logic, such as disk failure predictor 152 of FIG. 1, performs a lookup operation on both of the first quantile distribution representation (e.g., failed disk quantile distribution) and the second quantile distribution representation (e.g., working disk quantile distribution) corresponding to the diagnostic parameter based on the value of the diagnostic parameter to determine the diagnostic parameter having that particular value is represented by both the first and second quantile distribution representations (e.g., whether a quantile distribution contains the value of the diagnostic parameter).
In one embodiment, if the value of the diagnostic parameter is represented by both the first and second quantile distribution representations (e.g., the particular value of the diagnostic parameter in question is found in both quantile distribution representations), disk failure prediction logic, such as disk failure predictor 152 of FIG. 1, determines a first weight factor from the first quantile distribution representation based on the value of the diagnostic parameter and determines a second weight factor from second quantile distribution representation based on the value of the diagnostic parameter. A third weight factor is then determined based on the first and second weight factors and a disk failure probability is then determined based on the third weight factor. If the value of the diagnostic parameter is only represented by the first quantile distribution representation (and not by the second quantile distribution representation), the first weight factor is utilized as the third weight factor.
FIG. 11 is a diagram illustrating a process for predicting disk failures using quantile distribution techniques according to one embodiment of the invention. The process as shown in FIG. 11 may be performed by processing logic which may be disk predictor 152 and/or analysis module 153 of FIG. 1. Referring to FIG. 11, it is assumed a quantile distribution graph for a set of known failed disks 1101 and a quantile distribution graph for a set of known working disks 1102 for a particular diagnostic parameter (e.g., RAS) have been created using certain techniques described above. In this example, the Y axis represents the values of the corresponding diagnostic parameter and the X axis represents the distribution of the values of the diagnostic parameter. The values of the diagnostic parameter may be sorted and evenly distributed in a predetermined number of intervals, which may be in percentiles or deciles. In this example, the values are evenly distributed in 10 deciles. A decile represents any of the nine values that divide the sorted data into ten equal parts, so that each part represents 1/10 of the sample or population).
According to one embodiment, each interval or decile is assigned with a weight, a default weight factor, representing a degree or probability of disk failures. In this example, it is assumed the weight assigned to each decile is 1, 2, . . . , 10 corresponding to deciles 1, 2, . . . , 10, respectively, for the purpose of simplicity; however, other values may be utilized as weights. In this example, the maximum weight is then 10 while the minimum weight is 1. The distribution of the weights as presented in FIG. 11 is also referred to as a weight vector. A higher level of error on deciles indicates a higher failure probability. The discrimination level between working and failed deciles represents the correlation degree between values of the diagnostic parameter and disk failures.
In one embodiment, for a given value of a diagnostic parameter collected from a target storage disk for the purpose of predicting potential disk failures, a lookup operation is performed on both the failed disk quantile distribution graph 1101 and the working disk quantile distribution graph 1102 corresponding to that diagnostic parameter to determine whether that particular value of the diagnostic parameter is represented by both of the quantile distribution graphs 1101 and 1102. If the value falls in an overlapped part of the working and failed deciles (assuming in the range of Pth and Qth deciles of the failed disks, and Mth and Nth deciles of working disks), its weight is (Q−(10−N)). In this example, it is conservatively select an upper bound of the range to account for the possibility of the worst case situation. (10−N) is the remaining distance towards the end of working deciles, representing the survival probability given that particular value of the diagnostic parameter. In this example, the value of 10 represents the maximum default weight given there are 10 deciles in total. The difference between Q and (10−N) determines whether the disk is more likely to fail or survive. It also takes into account the discrimination level between working and failed deciles.
Note that in some cases (Q−(10−N)) may result in a negative number, which indicates the disk with that particular value of the diagnostic parameter is more probable to survive than to fail. In such case, the weight is assigned with a predetermined minimum weight such as zero, indicating that such a value has no contribution to the final prediction. According to one embodiment, if the value of the diagnostic parameter is only represented by the failed disk quantile distribution graph (e.g., the working quantile distribution graph does not contain that particular value of the diagnostic parameter), the default weight corresponding to the decile that the value falls within is used as the weight assigned to that particular diagnostic parameter of the target storage disk for the purpose of determining the probability of disk failures. Such scenario means there is no working disk with this value.
Referring back to FIG. 11, it is assumed that in one scenario the value of a given diagnostic parameter is represented by dash line 1112. Based on value 1112 of the diagnostic parameter, a lookup operation is performed on both failed disk quantile distribution graph 1101 and working disk quantile distribution graph 1102 to determine whether value 1112 is represented by both quantile distribution graphs 1101-1102. In this example, it is assumed value 1112 is found at point 1121 of quantile distribution graph 1101. Based on point 1121, its X axis value (e.g., decile) is determined, in this example, between 2 and 3 (e.g., P=2, Q=3). Similarly, it is assumed value 1112 can be found at point 1122 of quantile distribution graph 1102, and its X axis value is determined, in this example, between 8 and 9 (e.g., M=8, N=9). Based on the formula described above, the weight assigned to value 1112 will be Q−(10−N)=2. The probability of disk failures for the value 1112 will be determined based on weight of 2 in this example.
In another scenario, it is assumed that the value of the parameter is represented by dash line 1111. Based on value 1111 of the diagnostic parameter, a lookup operation is performed on both failed disk quantile distribution graph 1101 and working disk quantile distribution graph 1102 to determine whether value 1111 is represented by both quantile distribution graphs 1101-1102. In this example, only quantile distribution graph 1101 contains value 1111 at point 1123. Therefore, since point 1123 falls between 8 and 9, the weight assigned to value 1111 will be 9 (e.g., Q=9).
Therefore, for a given value of a diagnostic parameter, the weight assigned to that value of the diagnostic parameter is (a first weight given obtained from the failed disk quantile distribution graph−(the maximum weight−a second weight obtained from the working disk quantile distribution graph)). If the calculated weight is negative, a predetermined minimum weight (e.g., zero) is assigned to that value of the diagnostic parameter.
FIG. 12 is a flow diagram illustrating a method for predicting disk failures according to one embodiment of the invention. Method 1200 may be performed by processing logic which may include software, hardware, or a combination thereof, such as, for example, disk predictor 152 and/or analysis module 153 of FIG. 1. Referring to FIG. 12, at block 1201, for a given diagnostic parameter, a first quantile distribution graph is generated based on values of the diagnostic parameter that are collected from a set of known failed disks. A second quantile distribution graph is generated based on values of the diagnostic parameter that are collected from a set of known working disks. At block 1202, processing logic assigns a default weight or score to each of the intervals or deciles of the quantile distribution, representing a degree of potential disk failures. Subsequently at block 1203, when a value of the corresponding diagnostic parameter is received for the purpose of predicting potential disk failure of a target disk, a weight is determined based on a first weight obtained from the first quantile distribution graph and a second weight obtained from the second quantile distribution graph based on the value of the diagnostic parameter (e.g., Q−(10−N)). At block 1204, a probability of disk failures of the target disk is determined based on the weight that is derived from the first and second weights obtained from both quantile distribution graphs (e.g., based on calculated Euclidean length).
FIG. 13 is a flow diagram illustrating a method for predicting disk failures according to another embodiment of the invention. Method 1300 may be performed by processing logic which may include software, hardware, or a combination thereof, such as, for example, disk predictor 152 and/or analysis module 153 of FIG. 1. Referring to FIG. 13, at block 1301, in response to a diagnostic parameter collected from a target storage disk for the purpose of predicting disk failures, processing logic determines whether a value of the diagnostic parameter is represented by a first quantile distribution graph and a second quantile distribution graph generated based on a set of known failed disks and a set of known working disks, respectively. That is, processing logic determines whether the value of the received diagnostic parameter is contained in both the quantile distribution graphs. If so, at block 1302, processing logic calculates a weight factor for the received diagnostic parameter based on a first weight obtained from the first quantile distribution graph and a second weight obtained from the second quantile distribution graph, for example, using the formula (e.g., Q−(10−N)) described above. Thereafter, the probability of potential disk failures is determined based on the calculated weight factor at block 1304. On the other hand, if the value of the received diagnostic parameter is only represented by the first quantile distribution graph, at block 1303, the weight obtained from the first quantile distribution graph based on the value of the received diagnostic parameter is used to determine the probability of potential disk failures at block 1304.
The above weight assignment process results in a weight vector. Each element of the weight vector reflects an error degree of that particular fault type and represents its corresponding failure probability from that error prospective. A variety of different methods can be used to determine a probability of potential disk failures. In one embodiment, a disk failure probability can be determined by consolidating all weights together dependent upon the understanding of relationships amongst those faults.
According to one embodiment, a Euclidean length (also referred to as Euclidean distance) is utilized to combine all the weights because it takes into account all the possibilities and works more efficient. In mathematics, the Euclidean distance or Euclidean metric is the “ordinary” distance between two points that one would measure with a ruler, and is given by the Pythagorean formula. By using this formula as distance, Euclidean space (or even any inner product space) becomes a metric space. Given an input weight vector of [p1, p2, . . . pn], the Euclidean length=square root (p1^2+p2^2+ . . . +pn^2). For example, if the weight vector=[3, 4, 2], the Euclidean length=square root (3*3+4*4+2*2)=square root (29)=5.3. For a given set of values of a particular diagnostic parameter collected from a particular disk, the weight vector used to predict disk failures of that particular disk is generated using the techniques described above, dependent upon whether there is an overlapped area between a failed disk quantile distribution graph and a working disk quantile distribution graph. Thereafter, the Euclidean length of the weight vector is calculated for that disk and the calculated Euclidean length is compared to a predetermined failure threshold.
If the Euclidean length of the disk weight vector exceeds a predetermined failure threshold T, the disk in question is considered to be a soon-to-fail disk. The two quantile distribution graphs represent the tradeoff between the true positive number, which includes impending disk failures captured by the above formula(s) and the false alarm amount, which includes working disks identified incorrectly. By comparing these two quantile distribution representations over the entire range of the identification threshold(s), one can take into account all possible cost-based scenarios in terms of the tradeoff between missing impending disk failures versus failing working ones incorrectly.
FIG. 10 is a block diagram illustrating a deduplication storage system according to one embodiment of the invention. For example, deduplication storage system 1000 may be implemented as part of a deduplication storage system as described above, such as, for example, the deduplication storage system as shown in FIG. 1. In one embodiment, storage system 1000 may represent a file server (e.g., an appliance used to provide network attached storage (NAS) capability), a block-based storage server (e.g., used to provide SAN capability), a unified storage device (e.g., one which combines NAS and SAN capabilities), a nearline storage device, a direct attached storage (DAS) device, a tape backup device, or essentially any other type of data storage device. Storage system 1000 may have a distributed architecture, or all of its components may be integrated into a single unit. Storage system 1000 may be implemented as part of an archive and/or backup system such as a deduplicating storage system available from EMC® Corporation of Hopkinton, Mass.
In one embodiment, storage system 1000 includes a deduplication engine 1001 interfacing one or more clients 1014 with one or more storage units 1010 storing metadata 1016 and data objects 1018. Clients 1014 may be any kinds of clients, such as, for example, a client application, backup software, or a garbage collector, located locally or remotely over a network. A network may be any type of networks such as a local area network (LAN), a wide area network (WAN) such as the Internet, a corporate intranet, a metropolitan area network (MAN), a storage area network (SAN), a bus, or a combination thereof, wired and/or wireless.
Storage devices or units 1010 may be implemented locally (e.g., single node operating environment) or remotely (e.g., multi-node operating environment) via an interconnect, which may be a bus and/or a network. In one embodiment, one of storage units 1010 operates as an active storage to receive and store external or fresh user data, while the another one of storage units 1010 operates as a target storage unit to periodically archive data from the active storage unit according to an archiving policy or scheme. Storage units 1010 may be, for example, conventional magnetic disks, optical disks such as CD-ROM or DVD based storage, magnetic tape storage, magneto-optical (MO) storage media, solid state disks, flash memory based devices, or any other type of non-volatile storage devices suitable for storing large volumes of data. Storage units 1010 may also be combinations of such devices. In the case of disk storage media, the storage units 1010 may be organized into one or more volumes of redundant array of inexpensive disks (RAID). Data stored in the storage units may be stored in a compressed form (e.g., lossless compression: HUFFMAN coding, LEMPEL-ZIV WELCH coding; delta encoding: a reference to a chunk plus a difference; etc.). In one embodiment, different storage units may use different compression methods (e.g., main or active storage unit from other storage units, one storage unit from another storage unit, etc.).
The metadata, such as metadata 1016, may be stored in at least some of storage units 1010, such that files can be accessed independent of another storage unit. Metadata of each storage unit includes enough information to provide access to the files it contains. In one embodiment, metadata may include fingerprints contained within data objects 1018, where a data object may represent a data chunk (also referred to as a data segment), a compression region (CR) of one or more data chunks, or a container of one or more CRs. Fingerprints are mapped to a particular data object via metadata 1016, enabling the system to identify the location of the data object containing a chunk represented by a particular fingerprint. When an active storage unit fails, metadata contained in another storage unit may be utilized to recover the active storage unit. When one storage unit is unavailable (e.g., the storage unit has failed, or is being upgraded, etc.), the system remains up to provide access to any file not stored in the failed storage unit. When a file is deleted, the metadata associated with the files in the system is updated to reflect that the file has been deleted.
In one embodiment, the metadata information includes a file name, a storage unit identifier identifying a storage unit in which the chunks associated with the file name are stored, reconstruction information for the file using the chunks, and any other appropriate metadata information. In one embodiment, a copy of the metadata is stored on a storage unit for files stored on a storage unit so that files that are stored on the storage unit can be accessed using only the information stored on the storage unit. In one embodiment, a main set of metadata information can be reconstructed by using information of other storage units associated with the storage system in the event that the main metadata is lost, corrupted, damaged, etc. Metadata for a storage unit can be reconstructed using metadata information stored on a main storage unit or other storage unit (e.g., replica storage unit). Metadata information further includes index information (e.g., location information for chunks in storage units, identifying specific data objects).
In one embodiment, deduplication storage engine 1001 includes file service interface 1002, segmenter 1004, duplicate eliminator 1006, file system control 1008, and storage unit interface 1012. Deduplication storage engine 1001 receives a file or files (or data item(s)) via file service interface 1002, which may be part of a file system namespace 1020 of a file system associated with the deduplication storage engine 1001. The file system namespace 1020 refers to the way files are identified and organized in the system. An example is to organize the files hierarchically into directories or folders, which may be managed by directory manager 1022. File service interface 1012 supports a variety of protocols, including a network file system (NFS), a common Internet file system (CIFS), and a virtual tape library interface (VTL), etc.
The file(s) is/are processed by segmenter 1004 and file system control 1008. Segmenter 1004, also referred to as a content store, breaks the file(s) into variable-length chunks based on a variety of rules or considerations. For example, the file(s) may be broken into chunks by identifying chunk boundaries using a content-based technique (e.g., a function is calculated at various locations of a file, when the function is equal to a value or when the value is a minimum, a maximum, or other value relative to other function values calculated for the file), a non-content-based technique (e.g., based on size of the chunk), or any other appropriate technique. In one embodiment, a chunk is restricted to a minimum and/or maximum length, to a minimum or maximum number of chunks per file, or any other appropriate limitation.
In one embodiment, file system control 1008, also referred to as a file system manager, processes information to indicate the chunk(s) association with a file. In some embodiments, a list of fingerprints is used to indicate chunk(s) associated with a file. File system control 1008 passes chunk association information (e.g., representative data such as a fingerprint) to index 1024. Index 1024 is used to locate stored chunks in storage units 1010 via storage unit interface 1012. Duplicate eliminator 1006, also referred to as a segment store, identifies whether a newly received chunk has already been stored in storage units 1010. In the event that a chunk has already been stored in storage unit(s), a reference to the previously stored chunk is stored, for example, in a chunk tree associated with the file, instead of storing the newly received chunk. A chunk tree of a file may include one or more nodes and each node represents or references one of the deduplicated chunks stored in storage units 1010 that make up the file. Chunks are then packed by a container manager (which may be implemented as part of storage unit interface 1012) into one or more storage containers stored in storage units 1010. The deduplicated chunks may be further compressed into one or more CRs using a variation of compression algorithms, such as a Lempel-Ziv algorithm before being stored. A container may contains one or more CRs and each CR may contain one or more deduplicated chunks (also referred to deduplicated segments). A container may further contain the metadata such as fingerprints, type of the data chunks, etc. that are associated with the data chunks stored therein.
When a file is to be retrieved, file service interface 1002 is configured to communicate with file system control 1008 to identify appropriate chunks stored in storage units 1010 via storage unit interface 1012. Storage unit interface 1012 may be implemented as part of a container manager. File system control 1008 communicates (e.g., via segmenter 1004) with index 1024 to locate appropriate chunks stored in storage units via storage unit interface 1012. Appropriate chunks are retrieved from the associated containers via the container manager and are used to construct the requested file. The file is provided via interface 1002 in response to the request. In one embodiment, file system control 1008 utilizes a tree (e.g., a chunk tree obtained from namespace 1020) of content-based identifiers (e.g., fingerprints) to associate a file with data chunks and their locations in storage unit(s). In the event that a chunk associated with a given file or file changes, the content-based identifiers will change and the changes will ripple from the bottom to the top of the tree associated with the file efficiently since the appropriate content-based identifiers are easily identified using the tree structure. Note that some or all of the components as shown as part of deduplication engine 1001 may be implemented in software, hardware, or a combination thereof. For example, deduplication engine 1001 may be implemented in a form of executable instructions that can be stored in a machine-readable storage medium, where the instructions can be executed in a memory by a processor.
In one embodiment, storage system 1000 may be used as a tier of storage in a storage hierarchy that comprises other tiers of storage. One or more tiers of storage in this hierarchy may utilize different kinds of storage devices and/or may be optimized for different characteristics such as random update performance. Files are periodically moved among the tiers based on data management policies to achieve a cost-effective match to the current storage requirements of the files. For example, a file may initially be stored in a tier of storage that offers high performance for reads and writes. As the file ages, it may be moved into a tier of storage according to one embodiment of the invention. In various embodiments, tiers include different storage technologies (e.g., tape, hard drives, semiconductor-based memories, optical drives, etc.), different locations (e.g., local computer storage, local network storage, remote network storage, distributed storage, cloud storage, archive storage, vault storage, etc.), or any other appropriate storage for a tiered data storage system.
Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the ways used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities.
It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as those set forth in the claims below, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
Embodiments of the invention also relate to an apparatus for performing the operations herein. Such a computer program is stored in a non-transitory computer readable medium. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., a computer) readable storage medium (e.g., read only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices).
The processes or methods depicted in the preceding figures may be performed by processing logic that comprises hardware (e.g. circuitry, dedicated logic, etc.), software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be appreciated that some of the operations described may be performed in a different order. Moreover, some operations may be performed in parallel rather than sequentially.
Embodiments of the present invention are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the invention as described herein.
In the foregoing specification, embodiments of the invention have been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications may be made thereto without departing from the broader spirit and scope of the invention as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense.

## Claims

1. A computer-implemented method of predicting disk failures, the method comprising:
receiving, by a processor, a diagnostic parameter of a target storage disk of a storage system having a plurality of storage disks;
determining, by the processor, a first weight factor from a first quantile distribution representation based on a value of the diagnostic parameter, wherein the first quantile distribution representation represents a quantile distribution of values of the diagnostic parameter of a set of known failed disks;
determining, by the processor, a second weight factor from a second quantile distribution representation based on the value of the diagnostic parameter, wherein the second quantile distribution representation represents a quantile distribution of values of the diagnostic parameter of a set of known working disks;
calculating, by the processor, a third weight factor for the diagnostic parameter for the target storage disk based on the first weight factor and the second weight factor; and
determining, by the processor, a probability of potential disk failure of the target storage disk based on the third weight factor.

2. The method of claim 1, wherein the diagnostic parameter is one of reallocated sector count, medium error, timeout, pending sector count, uncorrectable sector count, connection error, and data error of the target storage disk.

3. The method of claim 1, further comprising:
prior to receiving the diagnostic parameter of the target storage disk, collecting values of the diagnostic parameter from the set of failed disks and the set of working disks;
generating the first and second quantile distribution representations based on the collected values of the diagnostic parameter in a predetermined number of deciles; and
assigning a default weight factor to each of the decile, the default weight factor representing a degree of disk failure of the corresponding decile.

4. The method of claim 3, wherein determining a first weight factor from a first quantile distribution representation comprises:
performing a lookup operation on the first quantile distribution representation based on the value of the diagnostic parameter;
determining a first decile based on the value of the diagnostic parameter; and
determining the first weight factor based on a first default weight factor assigned to the first decile.

5. The method of claim 4, wherein determining a second weight factor from a second quantile distribution representation comprises:
performing a lookup operation on the second quantile distribution representation based on the value of the diagnostic parameter;
determining a second decile based on the value of the diagnostic parameter; and
determining the second weight factor based on a second default weight factor assigned to the second decile.

6. The method of claim 5, wherein calculating a third weight factor comprises:
calculating a first difference value between a maximum weight factor and the second weight factor; and
computing the third weight factor based on a difference between the first weight factor and the first difference value.

7. The method of claim 6, further comprising assigning the third weight factor with a predetermined minimum weight factor if the calculated third weight factor is below a predetermined threshold.

8. The method of claim 6, further comprising assigning the first weight factor to the third weight factor if the second weight factor cannot be determined from the second quantile representation based on the value of the diagnostic parameter.

9. A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations of predicting disk failures, the operations comprising:
receiving a diagnostic parameter of a target storage disk of a storage system having a plurality of storage disks;
determining a first weight factor from a first quantile distribution representation based on a value of the diagnostic parameter, wherein the first quantile distribution representation represents a quantile distribution of values of the diagnostic parameter of a set of known failed disks;
determining a second weight factor from a second quantile distribution representation based on the value of the diagnostic parameter, wherein the second quantile distribution representation represents a quantile distribution of values of the diagnostic parameter of a set of known working disks;
calculating a third weight factor for the diagnostic parameter for the target storage disk based on the first weight factor and the second weight factor; and
determining a probability of potential disk failure of the target storage disk based on the third weight factor.

10. The non-transitory machine-readable medium of claim 9, wherein the diagnostic parameter is one of reallocated sector count, medium error, timeout, pending sector count, uncorrectable sector count, connection error, and data error of the target storage disk.

11. The non-transitory machine-readable medium of claim 9, wherein the operations further comprise:
prior to receiving the diagnostic parameter of the target storage disk, collecting values of the diagnostic parameter from the set of failed disks and the set of working disks;
generating the first and second quantile distribution representations based on the collected values of the diagnostic parameter in a predetermined number of deciles; and
assigning a default weight factor to each of the decile, the default weight factor representing a degree of disk failure of the corresponding decile.

12. The non-transitory machine-readable medium of claim 11, wherein determining a first weight factor from a first quantile distribution representation comprises:
performing a lookup operation on the first quantile distribution representation based on the value of the diagnostic parameter;
determining a first decile based on the value of the diagnostic parameter; and
determining the first weight factor based on a first default weight factor assigned to the first decile.

13. The non-transitory machine-readable medium of claim 12, wherein determining a second weight factor from a second quantile distribution representation comprises:
performing a lookup operation on the second quantile distribution representation based on the value of the diagnostic parameter;
determining a second decile based on the value of the diagnostic parameter; and
determining the second weight factor based on a second default weight factor assigned to the second decile.

14. The non-transitory machine-readable medium of claim 13, wherein calculating a third weight factor comprises:
calculating a first difference value between a maximum weight factor and the second weight factor; and
computing the third weight factor based on a difference between the first weight factor and the first difference value.

15. The non-transitory machine-readable medium of claim 14, wherein the operations further comprise assigning the third weight factor with a predetermined minimum weight factor if the calculated third weight factor is below a predetermined threshold.

16. The non-transitory machine-readable medium of claim 14, wherein the operations further comprise assigning the first weight factor to the third weight factor if the second weight factor cannot be determined from the second quantile representation based on the value of the diagnostic parameter.

17. A data processing system, comprising:
a processor; and
a memory storing instructions, which when executed from the memory, cause the processor to perform operations, the operations including
receiving a diagnostic parameter of a target storage disk of a storage system having a plurality of storage disks,
determining a first weight factor from a first quantile distribution representation based on a value of the diagnostic parameter, wherein the first quantile distribution representation represents a quantile distribution of values of the diagnostic parameter of a set of known failed disks,
determining a second weight factor from a second quantile distribution representation based on the value of the diagnostic parameter, wherein the second quantile distribution representation represents a quantile distribution of values of the diagnostic parameter of a set of known working disks,
calculating a third weight factor for the diagnostic parameter for the target storage disk based on the first weight factor and the second weight factor, and
determining a probability of potential disk failure of the target storage disk based on the third weight factor.

18. The system of claim 17, wherein the diagnostic parameter is one of reallocated sector count, medium error, timeout, pending sector count, uncorrectable sector count, connection error, and data error of the target storage disk.

19. The system of claim 17, wherein the operations further comprise:
prior to receiving the diagnostic parameter of the target storage disk, collecting values of the diagnostic parameter from the set of failed disks and the set of working disks;
generating the first and second quantile distribution representations based on the collected values of the diagnostic parameter in a predetermined number of deciles; and
assigning a default weight factor to each of the decile, the default weight factor representing a degree of disk failure of the corresponding decile.

20. The system of claim 19, wherein determining a first weight factor from a first quantile distribution representation comprises:
performing a lookup operation on the first quantile distribution representation based on the value of the diagnostic parameter;
determining a first decile based on the value of the diagnostic parameter; and
determining the first weight factor based on a first default weight factor assigned to the first decile.

21. The system of claim 20, wherein determining a second weight factor from a second quantile distribution representation comprises:
performing a lookup operation on the second quantile distribution representation based on the value of the diagnostic parameter;
determining a second decile based on the value of the diagnostic parameter; and
determining the second weight factor based on a second default weight factor assigned to the second decile.

22. The system of claim 21, wherein calculating a third weight factor comprises:
calculating a first difference value between a maximum weight factor and the second weight factor; and
computing the third weight factor based on a difference between the first weight factor and the first difference value.

23. The system of claim 22, wherein the operations further comprise assigning the third weight factor with a predetermined minimum weight factor if the calculated third weight factor is below a predetermined threshold.

24. The system of claim 22, wherein the operations further comprise assigning the first weight factor to the third weight factor if the second weight factor cannot be determined from the second quantile representation based on the value of the diagnostic parameter.