CN111782480B - Disk usage monitoring method, device, system and medium - Google Patents
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- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
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Abstract
The present disclosure provides a method for monitoring usage of a disk, including: obtaining the utilization rate of the disk at m preset historical moments to obtain m utilization rates; according to the m utilization rates, p utilization rate prediction values of the magnetic disk are determined by using a preset prediction model; and under the condition that p predicted utilization rate values meet the early warning condition for the magnetic disk, generating warning information for the magnetic disk, wherein m and p are integers greater than or equal to 2, and m is greater than p. The present disclosure also provides a disk usage monitoring device, a computer system, and a computer-readable storage medium.
Description
Technical Field
The present disclosure relates to the field of operation and maintenance technologies, and in particular, to a method, an apparatus, a system, and a medium for monitoring disk usage.
Background
The normal operation of the application system is not supported by sufficient disk capacity. With the rapid growth of business, disk capacity is also increasing. In order to prevent the situation that the application system cannot be used due to insufficient disk capacity, the prediction of the increasing trend of the disk usage is important.
In implementing the concepts of the present disclosure, the inventors found that there are at least the following problems in the related art: because the demands of different types of data are different, the increasing trend of the disk usage rate corresponding to the different types of data is different. In the related art, when predicting the increasing trend of the disk usage, the prediction is usually performed for each different data type, so that the prediction method is more and complex, and is not convenient to popularize. Moreover, in the related art, the usage rate prediction is often performed according to the usage rate of the disk at each sampling point, but there may be a case that the sampling result at a certain sampling point is inaccurate, which may affect the accuracy of the prediction result to a certain extent, and thus may cause false triggering of the alarm mechanism.
Disclosure of Invention
In view of this, the present disclosure provides a method and apparatus for monitoring disk usage that can predict disk usage with a unified method for different data types.
In one aspect, the disclosure provides a method for monitoring usage of a disk, including: obtaining the utilization rate of the disk at m preset historical moments to obtain m utilization rates; according to the m utilization rates, p utilization rate prediction values of the magnetic disk are determined by using a preset prediction model; and under the condition that p predicted utilization rate values meet the early warning condition for the magnetic disk, generating warning information for the magnetic disk, wherein m and p are integers greater than or equal to 2, and m is greater than p.
According to an embodiment of the present disclosure, the method for monitoring usage of a disk further includes constructing a preset prediction model, including: acquiring the utilization rate of each disk in the q disks at n preset historical moments; according to the utilization rate of each disk at n preset historical moments, determining the slope of an r-element linear model for each disk to obtain q slopes; determining a target slope in q slopes according to a preset rule, and taking the target slope as the slope of the r-element linear model to obtain a preset prediction model, wherein q magnetic disks are used for respectively storing q groups of data of different categories; n, q and r are integers greater than or equal to 2, n is greater than m, m is greater than r, and m is greater than r+p-1.
According to an embodiment of the present disclosure, determining p usage prediction values for a disk using a preset prediction model includes: sequencing m utilization rates from front to back according to a preset historical moment to obtain a utilization rate sequence; and determining one usage predictor of the disk using a preset predictive model according to r usages in the usage sequence, wherein each usage in the usage sequence is used at least once when determining p usage predictors.
According to an embodiment of the present disclosure, the above-mentioned r-ary linear model is expressed as: y= (x) 1 +x 2 +…+x r ) R+b t; wherein Y is a predicted value of the utilization rate; x is x 1 、x 2 、…、x r The utilization rate of r historical moments; b is the slope, t is the offset of the usage at r historic times.
According to an embodiment of the present disclosure, the value of the offset t is obtained by the following formula: r=x 1 +2×x 2 +…+r×x r -[(1+2+…+r)/r]×(x 1 +x 2 +…+x r )。
According to an embodiment of the present disclosure, obtaining usage of a disk at m predetermined historical moments includes: obtaining the usage rate of the magnetic disk at m preset historical moments in other time periods except for the preset time period aiming at the magnetic disk, wherein the preset time period aiming at the magnetic disk comprises a time period when generation of target data is in an abnormal state; the target data belongs to the class to which the data stored on the disk belongs.
According to an embodiment of the present disclosure, obtaining usage of a disk at m predetermined historical moments includes: and obtaining the utilization rate of the target disk at m preset historical moments, wherein the target disk comprises other disks except the preset disk in the q disks.
According to an embodiment of the present disclosure, in the case that the p usage prediction values satisfy the early warning condition for the disk, generating the warning information for the disk includes at least one of: under the condition that p predicted utilization rate values are all larger than a first utilization rate threshold value, generating alarm information aiming at a magnetic disk; generating alarm information for the magnetic disk under the conditions that p predicted utilization rate values are all larger than a second utilization rate threshold value and the utilization rate at the latest moment in the utilization rates of m preset historical moments is larger than a third utilization rate threshold value; and generating alarm information for the magnetic disk under the condition that p predicted utilization values are all larger than a second utilization threshold value and the predicted utilization value determined according to r utilization values with the later sequence is larger than the predicted utilization value determined according to r utilization values with the earlier sequence.
Another aspect of the present disclosure provides a disk usage monitoring device, the device comprising: the utilization rate acquisition module is used for acquiring the utilization rates of the magnetic disk at m preset historical moments to obtain m utilization rates; the predicted value determining module is used for determining p predicted values of the utilization rate of the magnetic disk by utilizing a preset predicted model according to the m utilization rates; and the alarm generation module is used for generating alarm information for the magnetic disk under the condition that p predicted utilization rate values meet the early warning condition for the magnetic disk, wherein m and p are integers greater than or equal to 2, and m is greater than p.
Another aspect of the present disclosure provides a computer system comprising: one or more processors; and a storage device for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the disk usage monitoring method described above.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are for performing a disk usage monitoring method as described above.
Another aspect of the present disclosure provides a computer program comprising computer executable instructions which when executed are for implementing a disk usage monitoring method as described above.
According to the embodiment of the disclosure, the technical problems that in the related art, a plurality of prediction methods are required to be adopted for different types of data, and the algorithm is complex and various, and the popularization is inconvenient can be at least partially avoided. The disk usage rate prediction method of the embodiment can predict the disk usage rates stored by various different types of data by adopting a preset prediction model, and can realize unified prediction of the disk usage rates stored by different data. Furthermore, by generating a plurality of prediction results according to the acquired utilization rate of the historical moment and determining whether to alarm according to the plurality of prediction results, the problems of large prediction error and low alarm accuracy caused by unreasonable utilization rate of a certain historical moment can be avoided, and the prediction and alarm accuracy can be improved.
Drawings
The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an application scenario of a disk usage monitoring method, apparatus, system, and medium according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a disk usage monitoring method according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow chart of constructing a preset predictive model in accordance with an embodiment of the present disclosure;
FIG. 4A schematically illustrates a flow chart for determining p predicted values of usage of a disk using a preset predictive model in accordance with an embodiment of the present disclosure;
FIG. 4B schematically illustrates a schematic diagram of determining P utilization predictions for a disk in accordance with an embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of a disk usage monitoring device according to an embodiment of the present disclosure; and
FIG. 6 schematically illustrates a block diagram of a computer system adapted to perform a disk usage monitoring method in accordance with an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
The embodiment of the disclosure provides a disk usage monitoring method, which comprises the following steps: obtaining the utilization rate of the disk at m preset historical moments to obtain m utilization rates; according to the m utilization rates, p utilization rate prediction values of the magnetic disk are determined by using a preset prediction model; and under the condition that p predicted utilization rate values meet the early warning condition for the magnetic disk, generating warning information for the magnetic disk, wherein m and p are integers greater than or equal to 2, and m is greater than p.
Fig. 1 schematically illustrates an application scenario of a disk usage monitoring method, apparatus, system and medium according to an embodiment of the present disclosure. It should be noted that fig. 1 illustrates only an example of an application scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments, or scenarios.
As shown in fig. 1, the application scenario 100 of this embodiment may include, for example, magnetic disks 111, 112, 113 and a mainframe 120.
Mainframe 120 may generate large amounts of data during system operation. In order to facilitate storage and management of the large amount of data generated, the large amount of data can be stored in different multiple disks in a classified manner, and the disks storing the same type of data form a disk group.
In order to avoid that the mainframe 120 cannot operate normally due to insufficient disk capacity, as shown in fig. 1, the application scenario 100 of this embodiment may further include, for example, a network 130 and a terminal device 140. The network 130 is the medium used to provide communication links between the terminal equipment 140 and the mainframe 120. The network 130 may include various connection types, such as wired, wireless communication links, and the like.
In an embodiment, the terminal device 140 may be, for example, various electronic devices having a display and installed with client applications. Among other things, electronic devices include, but are not limited to, smartphones, tablets, laptop portable computers, desktop computers, servers, and the like. The installed client applications include, but are not limited to: hardware performance test type application, disk monitoring type application, instant messaging type application, etc.
According to the embodiment of the disclosure, the terminal device 140 may, for example, periodically obtain the usage rate of the disk, and predict the usage rate of the disk at a future time according to a plurality of usage rates obtained at different times, so as to prompt an operation and maintenance personnel to perform operations such as disk capacity expansion in time when the capacity of the disk is insufficient.
It should be noted that, the method for monitoring the disk usage in the embodiment of the disclosure may be generally executed by the terminal device. Accordingly, the method for monitoring the disk usage in the embodiment of the present disclosure may be generally set in the terminal device.
It should be understood that the types of mainframes, disks, terminal devices, and networks in fig. 1 are merely illustrative. Any type of mainframe, disk, terminal equipment, and network may be provided as desired for implementation.
The following describes the disk usage monitoring method according to the embodiment of the present disclosure in detail with reference to fig. 2 to 3, and fig. 4A to 4B.
FIG. 2 schematically illustrates a flow chart of a disk usage monitoring method according to an embodiment of the present disclosure.
As shown in fig. 2, the disk usage monitoring method of this embodiment may include operations S210 to S230.
In operation S210, the usage of the disk at m predetermined history times is acquired, resulting in m usage rates.
According to embodiments of the present disclosure, the disk may be, for example, any one of the aforementioned plurality of disks for storing different categories of data. Alternatively, the disk may be a disk group in which at least two disks of the same class are stored. This embodiment may, for example, periodically obtain the usage of the disk. The usage rates at m predetermined history times in this operation S210 may be the latest m usage rates obtained before (including) the current time. Wherein m is an integer of 2 or more.
Illustratively, embodiments of the present disclosure may obtain the usage of the disk once every 1 hour, for example. If the current time is 11:00 and the value of m is 6, the obtained m utilization rates are utilization rates obtained at the time points 6:00, 7:00, 8:00, 9:00, 10:00 and 11:00 respectively. Wherein the interval duration of obtaining the usage rate of the disk is merely used as an example to facilitate understanding of the present disclosure, which is not limited thereto. However, the interval duration is not too long, and the excessive interval duration may cause the system to miss the defect of the optimal alarm time. The interval duration is not too small, which causes defects of unobvious trend of increase of the disk utilization rate and inaccurate prediction of the disk utilization rate, and unnecessary performance consumption is brought to the system. The usage of the disk may be obtained, for example, by running a pre-written script.
According to the embodiment of the disclosure, when the applications on the production system are intensively run in batches, the batch running applications present an unstable state of the usage rate of the disk corresponding to the data generated in the running process (i.e. the disk used for storing the class data to which the generated data belongs), and if the usage rate of the disk is predicted in the period, the prediction result is inaccurate. Therefore, in order to ensure the accuracy of the prediction result, the use rate of the corresponding disk may not be predicted in the period, but the use rate of the disk may be continuously monitored. Therefore, in order to avoid the subsequent use rate prediction for the corresponding disk, the operation S210 may perform the acquisition of m use rates at other periods than the predetermined period, for example.
The predetermined period is a period for a disk, that is, the predetermined period corresponds to the disk, and the predetermined period refers to a period when generation of target data is abnormal, and the target data belongs to a class to which the data stored in the corresponding disk belongs. The target data is in an abnormal state, namely, a batch generation state where the target data is in due to the concentrated operation of the application batch. The predetermined period of time may be different for different magnetic disks, for example, and may be set according to actual demands (characteristics of operation of the production system). For example, if the system runs batch jobs intensively in a period of 0:00-7:00 a day, the usage rate of the disk for storing data generated by the batch jobs varies greatly, and the predetermined period for the disk for storing data generated by the batch jobs is 0:00-7:00.
According to the embodiment of the disclosure, in order to improve flexibility of monitoring the disk usage, the embodiment may further be preset with a configuration file, where information such as an ID number of a predetermined disk is recorded in the configuration file. The preset magnetic disk is a magnetic disk which does not need to monitor the use rate according to actual requirements. Therefore, this operation S210 may take the other disks than the predetermined disk as the target disk when the usage rate of the disk is acquired, and acquire the usage rates of the target disk at m predetermined history times when the usage rate is acquired. With this arrangement, when a predetermined disk described in the configuration file is polled in the process of the terminal device 140 polling a plurality of disks storing data of different types, the predetermined disk is directly ignored, and the next disk other than the predetermined disk is monitored.
The predetermined disk may be, for example, a disk in which the usage rate is severely dithered due to a change demand. And/or, a certain time is required in the process of performing operations such as capacity expansion according to the alarm information by the operation and maintenance personnel after the alarm information for a certain disk is generated. In the process, a certain disk for which the generated alarm information is aimed can be used as a preset disk, so that the alarm for the certain disk is prevented from being repeatedly generated.
In operation S220, p usage prediction values of the disk are determined using a preset prediction model according to the m usage rates.
According to an embodiment of the present disclosure, the preset prediction model may be, for example, a multivariate model, that is, a model having a plurality of variables, and the operation S220 may output one usage prediction value of the obtained disk by using, as an input of the preset prediction model, a usage equal to the number of variables of the preset prediction model among the m usage. The plurality of usage rates as inputs of the preset prediction model may be any combination of usage rates among the m usage rates. And the combination of the utilization rates adopted for obtaining the different utilization rate predicted values is different.
According to the embodiment of the disclosure, the value of p in the p utilization rate predicted values can be set according to actual requirements. In order to avoid the situation that the prediction result cannot be ensured due to the unreasonable use rate of the obtained m use rates, if the value of p is 2 at minimum, p is an integer greater than or equal to 2. Furthermore, to ensure that the combination of usage rates employed to obtain any two of the p usage rate predictors is different, p should be an integer less than m.
According to an embodiment of the present disclosure, the preset prediction model may be constructed, for example, through the flow described in fig. 3. Accordingly, this operation S220 may be performed by the principle described in the following fig. 4B using the flow described in the following fig. 4A to obtain p usage prediction values.
In operation S230, in case that the p usage prediction values satisfy the early warning condition for the disk, warning information for the disk is generated.
According to an embodiment of the present disclosure, the operation S230 may be to generate the alert information in case that each of the p usage prediction values is greater than the first usage threshold value. The first usage threshold may be any value not less than 0.8, for example. It is to be understood that the implementation of the operation S230 and the value of the first usage threshold are merely examples to facilitate understanding of the present disclosure, which is not limited by the present disclosure. Again, this implementation of operation S230 is merely exemplary to facilitate understanding of the present disclosure, and the present disclosure may also generate alert information, for example, in a case where a duty cycle of a usage prediction value greater than a first usage threshold value of p usage prediction values is greater than a predetermined duty cycle (e.g., a value of any of 0.6, 0.8, etc. greater than 0.5).
According to an embodiment of the present disclosure, the early warning condition may be set according to a relationship between p usage prediction values, for example. In an embodiment, operation S230 may be implemented as: and generating alarm information when p predicted utilization rate values are larger than the second predicted utilization rate value and the utilization rate at the latest moment is larger than a third utilization rate threshold value in m utilization rates adopted when the p predicted utilization rate values are obtained. The second usage rate prediction value may be the same as or different from the first usage rate prediction value, and the third usage rate threshold value may be any value not less than 0.5, for example.
According to the embodiment of the present disclosure, the early warning condition may also be set according to, for example, the magnitude relation between p usage prediction values and the usage threshold value, and the arrangement order between p usage prediction values. In an embodiment, the p usage rates may be ordered according to the historical time corresponding to the usage rate from front to back, to obtain the usage rate sequence. Operation S230 may be implemented as: and generating alarm information for the magnetic disk under the condition that p predicted utilization values are all larger than a fourth utilization threshold value and the predicted utilization value determined according to the utilization rate with the later sequence is larger than the predicted utilization value determined according to the utilization rate with the earlier sequence. The fourth usage threshold may be the same as or different from the first usage threshold and the second usage threshold.
According to embodiments of the present disclosure, the early warning conditions may be different, for example, for different disks storing different types of data. For example, when storing large amounts of data in different disks in a sorted manner, the large amounts of data may be sorted, for example, according to the purpose of the data. For example, data used by the database may be categorized as one type, and data invoked by the bulk file runtime may be categorized as one type. For example, when storing large amounts of data in different disks in a sorted manner, the large amounts of data may be sorted, for example, by the format of the data, or by the source of the data.
As can be seen from the above, in the embodiment of the present disclosure, by generating a plurality of prediction results according to the obtained utilization rate of the historical time, and determining whether to alarm according to the plurality of prediction results, the problem of large prediction error and low alarm accuracy caused by unreasonable utilization rate of a certain historical time can be avoided, and thus prediction and alarm accuracy can be improved.
Fig. 3 schematically illustrates a flowchart of constructing a preset predictive model according to an embodiment of the present disclosure.
In accordance with embodiments of the present disclosure, to obtain a predictive model, embodiments of the present disclosure may first select an initial model. The initial model may choose a linear model, considering that the usage of the disk generally tends to increase linearly. Furthermore, in order to improve the accuracy of the prediction result, the linear model may be, for example, an r-element linear model. Wherein r is an integer of 2 or more. After the initial model is selected, the slope of the r-ary linear model can be selected according to the disk usage rate stored by different types of data.
After the initial model is selected, the disk usage monitoring method of the embodiment of the disclosure further includes an operation of constructing a preset prediction model. As shown in fig. 3, the operation of constructing the preset prediction model may include, for example, operations S340 to S360.
In operation S340, the usage of each of the q disks at n predetermined history times is obtained.
According to the embodiment of the disclosure, q is equal to the number of categories obtained by classifying the large amount of data. q is an integer greater than 2. q disks are used for storing q groups of data of different categories respectively, each group of data in the q groups of data is classified into the same category, and one disk is used for storing data of one category.
According to an embodiment of the present disclosure, operation S340 may obtain the usage of each disk at n predetermined history times, for example, by a method similar to the aforementioned operation S210. In order to improve the accuracy of the slope determined later, the value of n should be greater than the value of m in this embodiment.
In operation S350, according to the usage of each disk at n predetermined history moments, the slope of the r-element linear model for each disk is determined, and q slopes are obtained.
According to an embodiment of the present disclosure, the operation S350 may be to linearly fit n usage rates of n predetermined history times of each disk, to obtain a slope for each disk.
The r-ary linear model may be shown in the following equation (1), where Y is the usage prediction value, and r is the usage x 1 、x 2 、…、x r Is taken as intercept, b is slope, t is the average value which can reflect rThe value of the trend of the usage rate. In one embodiment, t may be an offset of r usage rates.
Y＝(x 1 +x 2 +…+x r ) R+b. Formula (1)
According to the embodiment of the disclosure, in order to obtain the value of the slope b through fitting, a plurality of groups of values of t and intercept are obtained according to the usage rate of n preset historical moments. Considering that the utilization rate at the latest moment can reflect the change trend of the utilization rate of the disk more when predicting the utilization rate, r utilization rates can be assigned with weights from small to large from front to back according to the historical moment when calculating the offset t.
The value of the offset t can be calculated by the following formula (2), for example. The coefficient multiplied by each usage rate is a weight assigned to each usage rate. For example, to x r The assigned weight is r.
r＝x 1 +2×x 2 +…+r×x r -[(1+2+…+r)/r]×(x 1 +x 2 +…+x r ). Formula (2)
Illustratively, assuming n is 9,r as 4, the usage rate for 9 historical times for a certain disk, and the values of the obtained sets of t and intercept according to the usage rates for the 9 predetermined historical times may be as shown in table 1 below.
TABLE 1
| x 1 | x 2 | x 3 | x 4 | x 5 | x 6 | x 7 | x 8 | x 9 | |
| Utilization rate | 21.2 | 23 | 24.6 | 26.4 | 28.4 | 31.3 | 33.8 | 36.4 | 38.3 |
| t | 8.6 | 9 | 10.75 | 12.45 | 13.35 | 12.1 | |||
| Intercept of (intercept of) | 23.8 | 25.6 | 27.675 | 29.975 | 32.475 | 34.95 |
As can be seen from table 1, the number of t and intercept value sets obtained from the use rates at the 9 predetermined history times is 6. By substituting the 6 sets of values into the above formula (1), 6 predicted values of the usage rate can be obtained. By taking the values according to the 6 usage predictions and the 6 sets, a slope for the certain disk can be fitted. Q slopes can be obtained for each of the q disks. It will be appreciated that the value of r is 4 and the number of q is 9 is merely exemplary. In another embodiment, r may be 3, and q is 9, and the number of the obtained t and intercept sets is 7. The number of the value group may be a number obtained by q-r+1.
In operation S360, a target slope of the q slopes is determined according to a predetermined rule, so as to obtain a preset prediction model by using the target slope as a slope of the r-element linear model.
By way of example, the q slopes may be found in the range of [ -0.58,4.04] by experiment. In order to enable the final determined preset predictive model to be applied to q disks, it is not desirable to select either an excessive value or an excessive value when selecting the target slope. When an excessive value is selected, the predicted value of the usage rate of the magnetic disk with less severe trend of increasing the usage rate may be excessively large, so that unnecessary alarm information is generated. Conversely, when a value is selected to be too small, it may result in that the predicted value of the usage rate of some magnetic disks with a strong trend of increasing usage rate is too small, and the optimal warning moment is missed. This operation S360 may select, for example, a slope whose value is centered in the value range as the target slope. In the case of more centered slopes, the embodiment may first use the centered slope as the candidate slope in order to further increase the accuracy of the determined slope. And then selecting the slope which is most fit with the actual use rate change trend of the corresponding magnetic disk from the slopes to be selected as a target slope.
In summary, the embodiment of the disclosure determines the slope of the r-element linear model according to the usage rates of a plurality of disks at historical moments, so that the finally obtained model can be more used for predicting the usage rates of the disks storing different types of data. Therefore, the technical problems of complex and various prediction methods in the related art, which are caused by adopting different prediction models aiming at different types of data, can be solved. Therefore, the disk usage monitoring method of the embodiment of the disclosure has universality and is convenient for wide popularization.
According to the embodiment of the present disclosure, in order to facilitate that the p usage rate predicted values of the disk are different in the use process of the preset prediction model, the number m of usage rates obtained through operation S210 should be greater than r+p-1.
Fig. 4A schematically illustrates a flowchart for determining p utilization predictions of a disk using a preset predictive model in accordance with an embodiment of the present disclosure. Fig. 4B schematically illustrates a schematic diagram of determining P utilization predictions for a disk according to an embodiment of the present disclosure.
In accordance with an embodiment of the present disclosure, when the preset prediction model obtained through operations S340 to S360 is an r-ary linear model, as shown in fig. 4A, the aforementioned operation S220 of determining p usage prediction values of the disk using the preset prediction model may include operations S421 to S422, for example.
In operation S421, m usage rates are ordered according to a predetermined history time from front to back, resulting in a usage rate sequence.
In operation S422, a predicted value of the usage of the disk is determined using a preset prediction model according to r usages sequentially arranged in the usage sequence.
For example, assuming that the m usage rates obtained in operation S210 are the usage rate of the ith disk in the q disks and the value of m is 6, the usage rate sequence obtained by sorting the obtained m usage rates is shown in fig. 4B and may be represented as { x } 1i 、x 2i 、x 3i 、x 4i 、x 5i 、x 6i }。
Illustratively, when r has a value of 4, as shown in FIG. 4B, the usage x may be first determined by operation S422 1i 、x 2i 、x 3i 、x 4i Substituting the predicted value into the determined preset prediction model to calculate a predicted value p of the utilization rate of the magnetic disk 1 . Then the usage x 3i 、x 4i 、x 5i 、x 6i Substituting the predicted value into the determined preset prediction model to calculate a predicted value p of the utilization rate of the magnetic disk 2 . A total of two usage prediction values are obtained through operation S422.
Illustratively, when r has a value of 3, by operation S422, the value of x may be determined first 1i 、x 2i 、x 3i And calculating to obtain a predicted value of the utilization rate of the magnetic disk. Then according to x 2i 、x 3i 、x 4i And calculating to obtain a predicted value of the utilization rate of the magnetic disk. Then according to x 3i 、x 4i 、x 5i And calculating to obtain a predicted value of the utilization rate of the magnetic disk. Finally according to x 4i 、x 5i 、x 6i And calculating to obtain one predicted value of the utilization rate of the magnetic disk, and finally obtaining four predicted values of the utilization rate.
It is to be understood that, in operation S422, r usages in a sequential order may be selected by any rule, so as to mainly ensure that each usage in the usage sequence is used at least once when determining that p usage predictions are obtained. Illustratively, operation S422 may select r usage rates at equal intervals, for example, when determining p usage rate predictors for a disk.
FIG. 5 schematically illustrates a block diagram of a disk usage monitoring device according to an embodiment of the present disclosure.
As shown in fig. 5, the disk usage monitoring apparatus 500 of this embodiment may include, for example, a usage acquisition module 510, a predicted value determination module 520, and an alert generation module 530.
The usage rate obtaining module 510 is configured to obtain usage rates of the disk at m predetermined historical moments, to obtain m usage rates, where m is an integer greater than or equal to 2. In an embodiment, the usage obtaining module 510 may be used to perform the operation S210 described in fig. 2, which is not described herein.
The predicted value determining module 520 is configured to determine p predicted values of the usage rate of the disk according to the m usage rates by using a preset prediction model. Wherein p is an integer of 2 or more, and m is greater than p. In an embodiment, the predictor determining module 520 may be used to perform the operation S220 described in fig. 2, which is not described herein.
The alarm generation module 530 is configured to generate alarm information for a disk if p usage prediction values satisfy an early warning condition for the disk. In an embodiment, the alarm generation module 530 may be used to perform the operation S230 described in fig. 2, which is not described herein.
The disk usage monitoring device 500 may further include a model building module for building a preset prediction model, for example, according to an embodiment of the present disclosure. In particular, the model building module may be used, for example, to perform the following operations: acquiring the utilization rate of each disk in the q disks at n preset historical moments; according to the utilization rate of each disk at n preset historical moments, determining the slope of an r-element linear model for each disk to obtain q slopes; determining a target slope in q slopes according to a preset rule, and taking the target slope as the slope of the r-element linear model to obtain a preset prediction model, wherein q magnetic disks are used for respectively storing q groups of data of different categories; n, q and r are integers greater than or equal to 2, n is greater than m, and m is greater than r+p-1. In an embodiment, the model building module may be used to perform operations S340 to S360 described in fig. 3, which are not described herein.
The predictor determination module 520 may include, for example, a ranking sub-module and a predictor sub-module, according to an embodiment of the present disclosure. The sequencing sub-module is used for sequencing the m utilization rates from front to back according to the preset historical moment to obtain a utilization rate sequence. The prediction sub-module is used for determining a predicted value of the utilization rate of the magnetic disk by using a preset prediction model according to r utilization rates which are sequentially arranged in the utilization rate sequence. Wherein each usage in the usage sequence is used at least once when determining the p usage predictions.
According to an embodiment of the present disclosure, the above-mentioned r-ary linear model is expressed as: y= (x) 1 +x 2 +…+x r ) R+b t; wherein Y is a predicted value of the utilization rate; x is x 1 、x 2 、…、x r The utilization rate is r different moments; b is the slope and t is the offset of the usage at r different times.
According to an embodiment of the present disclosure, the value of the offset t is obtained by the following formula: r=x 1 +2×x 2 +…+r×x r -[(1+2+…+r)/r]*(x 1 +x 2 +…+x r )。
According to an embodiment of the present disclosure, obtaining usage of a disk at m predetermined historical moments includes: obtaining the usage rate of the magnetic disk at m preset historical moments in other time periods except for the preset time period aiming at the magnetic disk, wherein the preset time period aiming at the magnetic disk comprises a time period when generation of target data is in an abnormal state; the target data belongs to the class to which the data stored on the disk belongs.
According to an embodiment of the present disclosure, obtaining usage of a disk at m predetermined historical moments includes: and obtaining the utilization rate of the target disk at m preset historical moments, wherein the target disk comprises other disks except the preset disk in the q disks.
According to an embodiment of the present disclosure, in a case where the p usage prediction values satisfy the early warning condition for the disk, generating the warning information for the disk includes at least one of: under the condition that p predicted utilization rate values are all larger than a first utilization rate threshold value, generating alarm information aiming at a magnetic disk; generating alarm information for the magnetic disk under the conditions that p predicted utilization rate values are all larger than a second utilization rate threshold value and the utilization rate at the latest moment in the utilization rates of m preset historical moments is larger than a third utilization rate threshold value; and generating alarm information for the magnetic disk under the condition that p predicted utilization values are all larger than a fourth utilization threshold value and the predicted utilization value determined according to r utilization rates with the later sequence is larger than the predicted utilization value determined according to r utilization rates with the earlier sequence.
Any number of modules, sub-modules, units, sub-units, or at least some of the functionality of any number of the sub-units according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented as split into multiple modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or in any other reasonable manner of hardware or firmware that integrates or encapsulates the circuit, or in any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be at least partially implemented as computer program modules, which when executed, may perform the corresponding functions.
FIG. 6 schematically illustrates a block diagram of a computer system adapted to perform a disk usage monitoring method in accordance with an embodiment of the present disclosure.
As shown in fig. 6, a computer system 600 according to an embodiment of the present disclosure includes a processor 601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. The processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 601 may also include on-board memory for caching purposes. The processor 601 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.
In the RAM 603, various programs and data required for the operation of the computer system 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The processor 601 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or the RAM 603. Note that the program may be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, computer system 600 may also include an input/output (I/O) interface 605, with input/output (I/O) interface 605 also being connected to bus 604. Computer system 600 may also include one or more of the following components connected to I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
According to embodiments of the present disclosure, the method flow according to embodiments of the present disclosure may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the computer system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 602 and/or RAM 603 and/or one or more memories other than ROM 602 and RAM 603 described above.
The flowcharts and block diagrams in the figures 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 the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be combined in various combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.
Claims (10)
1. A method of disk usage monitoring, comprising:
obtaining the utilization rate of the disk at m preset historical moments to obtain m utilization rates;
according to the m utilization rates, p utilization rate prediction values of the magnetic disk are determined by using a preset prediction model; and
generating alarm information for the magnetic disk under the condition that the p predicted utilization values meet the early warning condition for the magnetic disk,
wherein m and p are integers greater than or equal to 2, and m is greater than p;
the method further comprises the steps of: constructing the preset prediction model;
the constructing the preset prediction model comprises the following steps:
acquiring the utilization rate of each disk in the q disks at n preset historical moments;
According to the utilization rate of each disk at n preset historical moments, determining the slope of an r-element linear model for each disk to obtain q slopes; and
determining a target slope in the q slopes according to a preset rule, taking the target slope as the slope of the r-element linear model to obtain the preset prediction model,
the q magnetic disks are used for respectively storing q groups of data of different categories; n, q and r are integers greater than or equal to 2, n is greater than m, and m is greater than r+p-1.
2. The method of claim 1, wherein determining p utilization predictions for the disk using a preset predictive model comprises:
sequencing the m utilization rates from front to back according to a preset historical moment to obtain a utilization rate sequence; and
determining a predicted value of the utilization rate of the magnetic disk by utilizing the preset prediction model according to r utilization rates which are orderly arranged in the utilization rate sequence,
wherein each usage in the usage sequence is used at least once when determining the p usage predictions.
3. The method of claim 1, wherein the r-ary linear model is represented as:
Y＝(x 1 +x 2 +…+x r )/r+b*t；
wherein Y is a predicted value of the utilization rate; x is x 1 、x 2 、…、x r The utilization rate is r different moments; b is the slope, t is the offset of the usage rate at the r different times.
4. A method according to claim 3, wherein the offset t is valued by the following formula:
r＝x 1 +2×x 2 +…+r×x r -[(1+2+…+r)/r]×(x 1 +x 2 +…+x r )。
5. the method of claim 1, wherein obtaining usage of the disk at m predetermined historical times comprises:
obtaining the usage of the disk at the m predetermined history times at other time periods than the predetermined time period for the disk,
wherein the predetermined period of time for the disk includes a period of time during which generation of the target data is in an abnormal state; the target data belongs to the category of the data stored in the magnetic disk.
6. The method of claim 1, wherein obtaining usage of the disk at m predetermined historical times comprises:
obtaining the utilization rate of the target disk at m preset historical moments,
the target disk comprises other disks except a preset disk in the q disks.
7. The method of claim 2, wherein the generating alert information for the disk if the p usage predictions satisfy an early warning condition for the disk comprises at least one of:
Generating alarm information for the magnetic disk under the condition that the p predicted utilization values are all larger than a first utilization threshold value;
generating alarm information for the magnetic disk under the condition that the p predicted utilization rate values are all larger than a second utilization rate threshold value and the utilization rate at the latest moment in the utilization rates of the m preset historical moments is larger than a third utilization rate threshold value;
and generating alarm information for the magnetic disk under the condition that the p predicted utilization values are all larger than a fourth utilization threshold value and the predicted utilization value determined according to the r utilization values with the later sequence is larger than the predicted utilization value determined according to the r utilization values with the earlier sequence.
8. A disk usage monitoring device, comprising:
the utilization rate acquisition module is used for acquiring the utilization rates of the magnetic disk at m preset historical moments to obtain m utilization rates;
the predicted value determining module is used for determining p predicted values of the utilization rate of the magnetic disk by utilizing a preset predicted model according to the m utilization rates; and
an alarm generating module for generating alarm information for the magnetic disk under the condition that the p predicted utilization values meet the early warning condition for the magnetic disk,
Wherein m and p are integers greater than or equal to 2, and m is greater than p;
the apparatus further comprises:
the model construction module is used for constructing the preset prediction model;
the construction of the preset prediction model is used for:
acquiring the utilization rate of each disk in the q disks at n preset historical moments;
according to the utilization rate of each disk at n preset historical moments, determining the slope of an r-element linear model for each disk to obtain q slopes; and
determining a target slope in the q slopes according to a preset rule, taking the target slope as the slope of the r-element linear model to obtain the preset prediction model,
the q magnetic disks are used for respectively storing q groups of data of different categories; n, q and r are integers greater than or equal to 2, n is greater than m, and m is greater than r+p-1.
9. A computer system, comprising:
one or more processors;
storage means for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-7.
10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1-7.
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