CN111274164B - LBA (logical Block addressing) distribution method, device, equipment and readable storage medium - Google Patents
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Abstract
The application discloses a method, a device and equipment for LBA distribution and a readable storage medium. The method disclosed by the application comprises the following steps: acquiring I/O data to be stored; judging whether the data size of the I/O data exceeds a preset threshold value by using a logistic regression algorithm; and if so, processing the I/O data by using a linear regression algorithm to obtain continuous LBA identification information, and distributing continuous LBAs for the I/O data according to the continuous LBA identification information. The method and the device can realize continuous storage of large blocks of I/O data in a physical space, and can not bring troubles to addressing of subsequent read-write operation. The logistic regression algorithm and the linear regression algorithm can also improve the accuracy and efficiency of LBA allocation. Accordingly, the LBA distribution apparatus, and LBA distribution readable storage medium disclosed in the present application also have the technical effects described above.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a LBA allocation method, apparatus, device, and readable storage medium.
Background
At present, when storing I/O data, LBA (Logical Block Address) is randomly allocated to the I/O data, and the LBA in the subsequent order can be considered at the same time, so that data can be written into a physical space corresponding to the LBA in the subsequent order, and a situation that data is repeatedly written into some physical spaces but data is not always written into some physical spaces does not occur. However, in the process of randomly allocating the LBA, the I/O data in a larger block needs to be divided into small particles, which may cause the original whole block of data to be stored dispersedly, which is not favorable for addressing in the subsequent read/write operation.
Therefore, how to allocate the LBA reasonably for the I/O data is a problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, an object of the present application is to provide a LBA allocation method, apparatus, device and readable storage medium, so as to allocate LBAs for I/O data reasonably. The specific scheme is as follows:
in a first aspect, the present application provides a LBA allocation method, including:
acquiring I/O data to be stored;
judging whether the data size of the I/O data exceeds a preset threshold value by using a logistic regression algorithm;
and if so, processing the I/O data by using a linear regression algorithm to obtain continuous LBA identification information, and distributing continuous LBAs to the I/O data according to the continuous LBA identification information.
Preferably, the determining, by using a logistic regression algorithm, whether the data size of the I/O data exceeds a preset threshold includes:
calculating a predicted value of the I/O data by using the logistic regression algorithm;
mapping the predicted value to a target value;
judging whether the target value exceeds the preset threshold value;
if so, determining that the data size of the I/O data exceeds the preset threshold;
and if not, determining that the data size of the I/O data does not exceed the preset threshold.
Preferably, the calculating the predicted value of the I/O data by using the logistic regression algorithm includes:
calculating the predicted value according to a target formula, wherein the target formula is as follows: y is*＝w0+w1×x1+w2×x2；
Wherein, y*As the predicted value, w0To preset offset, w1A first parameter, w, corresponding to the size of the data volume of the I/O data2A second parameter, x, corresponding to the current I/O rate1Is the data size, x, of the I/O data2Is the current I/O rate.
Preferably, the processing the I/O data by using a linear regression algorithm to obtain continuous LBA identification information includes:
and inputting the data size of the I/O data and the current IO rate into the linear regression algorithm, and outputting the continuous LBA identification information.
Preferably, after allocating the continuous LBA to the I/O data according to the continuous LBA identification information, the method further includes:
and storing the I/O data to a continuous physical space corresponding to the continuous LBA.
Preferably, the method further comprises the following steps:
and if the data size of the I/O data does not exceed the preset threshold value, distributing the target LBA for the I/O data by utilizing a consistent hash algorithm.
Preferably, the allocating the target LBA to the I/O data by using the consistent hashing algorithm includes:
determining an identification value corresponding to the I/O data;
processing the identification value by using the consistent hash algorithm to obtain target identification information of the target LBA;
and distributing the target LBA for the I/O data according to the target identification.
In a second aspect, the present application provides an LBA allocation apparatus, including:
the acquisition module is used for acquiring the I/O data to be stored;
the judging module is used for judging whether the data size of the I/O data exceeds a preset threshold value by utilizing a logistic regression algorithm;
and the allocation module is used for processing the I/O data by using a linear regression algorithm to obtain continuous LBA identification information if the data size of the I/O data exceeds the preset threshold value, and allocating continuous LBAs to the I/O data according to the continuous LBA identification information.
In a third aspect, the present application provides an LBA allocation apparatus, including:
a memory for storing a computer program;
a processor for executing the computer program to implement the LBA allocation method disclosed in the foregoing.
In a fourth aspect, the present application provides a readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the LBA allocation method disclosed in the foregoing.
According to the above scheme, the present application provides an LBA allocation method, including: acquiring I/O data to be stored; judging whether the data size of the I/O data exceeds a preset threshold value by using a logistic regression algorithm; and if so, processing the I/O data by using a linear regression algorithm to obtain continuous LBA identification information, and distributing continuous LBAs to the I/O data according to the continuous LBA identification information.
According to the method, after the I/O data to be stored are obtained, a logistic regression algorithm is used for judging whether the data volume of the I/O data exceeds a preset threshold value or not; and if the data size of the I/O data exceeds a preset threshold value, processing the I/O data by using a linear regression algorithm to obtain continuous LBA identification information, and distributing continuous LBAs for the I/O data according to the continuous LBA identification information. In the process of storing the I/O data, continuous LBAs are distributed to large blocks of I/O data (namely, the I/O data with the data volume exceeding the preset threshold), so that the large blocks of I/O data can be continuously stored in a physical space, and the trouble of addressing of subsequent read-write operation can not be brought. Moreover, the logistic regression algorithm and the linear regression algorithm can accurately and quickly determine the continuous LBAs, so that the accuracy and the efficiency of LBA allocation are improved.
Accordingly, the LBA distribution apparatus, and LBA distribution readable storage medium provided in the present application also have the technical effects described above.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of an LBA allocation method disclosed in the present application;
FIG. 2 is a detailed flowchart of step S102 in FIG. 1;
FIG. 3 is a flow chart of another LBA allocation method disclosed herein;
fig. 4 is a schematic diagram of an LBA allocation apparatus according to the present disclosure;
fig. 5 is a schematic diagram of an LBA allocation apparatus according to the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
At present, in the process of randomly distributing the LBA, a large block of I/O data needs to be divided into small particles, which may cause the original whole block of data to be stored dispersedly, which is not favorable for addressing of subsequent read-write operations. Therefore, the LBA allocation scheme is provided, troubles brought to addressing of subsequent read-write operation can be avoided, and the LBA allocation accuracy and efficiency are improved.
Referring to fig. 1, an embodiment of the present application discloses a LBA allocation method, including:
s101, obtaining I/O data to be stored.
It should be noted that the I/O data to be stored may be I/O data corresponding to a write operation at a host end, or may be I/O data corresponding to a garbage collection operation in a hard disk.
S102, judging whether the data size of the I/O data exceeds a preset threshold value by using a logistic regression algorithm; if yes, executing S103; if not, executing S104.
S103, processing the I/O data by using a linear regression algorithm to obtain continuous LBA identification information, and distributing continuous LBAs for the I/O data according to the continuous LBA identification information.
In one embodiment, the processing the I/O data by using a linear regression algorithm to obtain the continuous LBA identification information includes: and inputting the data size of the I/O data and the current IO rate into a linear regression algorithm, and outputting continuous LBA identification information. Specifically, in this embodiment, the data size of the I/O data and the current IO rate are input to the linear regression algorithm, and the specific processing procedure of the linear regression algorithm may refer to the prior art, which is not described herein again.
And S104, distributing the target LBA for the I/O data by utilizing a consistent hash algorithm.
In one embodiment, allocating a target LBA to I/O data using a consistent hashing algorithm includes: determining an identification value corresponding to the I/O data; processing the identification value by using a consistent Hash algorithm to obtain target identification information of the target LBA; and allocating target LBAs for the I/O data according to the target identification.
Specifically, when the data size of the I/O data does not exceed the preset threshold, it is indicated that the current I/O data only needs one LBA, and for such I/O data that only needs one LBA, the I/O data is sequentially marked in an increasing manner, and the marked value is used as the identification value.
For example: marking the first I/O data needing only one LBA in the current disk as 1, wherein 1 is the identification value corresponding to the I/O data; then the second I/O data in the current disk only needs one LBA, and mark it as 2, where 2 is the identification value corresponding to the current I/O data; correspondingly, the third I/O data in the current disk that only needs one LBA is marked as 3, and at this time, 3 is the identification value corresponding to the current I/O data. By analogy, each I/O data that requires only one LBA has a corresponding identification value.
After the identification value corresponding to the I/O data is determined, the hash value corresponding to the identification value is calculated by using a consistent hash algorithm, the hash value is the target identification information of the target LBA, and then the target LBA is allocated to the I/O data according to the target identification.
In a specific embodiment, after allocating continuous LBAs for the I/O data according to the continuous LBA identification information, the method further includes; and storing the I/O data to the continuous physical space corresponding to the continuous LBA. Correspondingly, after the I/O data is allocated with the target LBA by utilizing the consistent hash algorithm, the method also comprises the following steps; and storing the I/O data to a target physical space corresponding to the target LBA.
Referring to fig. 2, fig. 2 is a detailed flowchart of step S102 in fig. 1. The specific implementation steps of S102 in fig. 1 include:
s201, calculating a predicted value of the I/O data by using a logistic regression algorithm.
In one embodiment, the method for calculating the predicted value of the I/O data by using a logistic regression algorithm comprises the following steps: calculating a predicted value according to a target formula, wherein the target formula is as follows: y is*＝w0+w1×x1+w2×x2(ii) a Wherein, y*To predict value, w0To presetOffset, w1A first parameter, w, corresponding to the size of the data volume of the I/O data2A second parameter, x, corresponding to the current I/O rate1Is the data size, x, of the I/O data2Is the current I/O rate.
It should be noted that the preset offset, the first parameter, and the second parameter are all available parameters obtained by training. The preset offset, the first parameter and the second parameter may be trained using the following equations.
Wherein, wjA preset offset, a first parameter or a second parameter which needs to be trained, alpha is a learning rate, m is the number of I/O data used in the training process, and y*Is a predicted value, yiAnd E, a true value is manually marked, the predicted value corresponds to the true value, and J is a training error. And when the preset offset, the first parameter or the second parameter obtained by training conforms to the training error, finishing the training and obtaining the available preset offset, the first parameter or the second parameter.
Wherein the content of the first and second substances,
training error J vs. wjThe derivative of (c) is:
wherein when j is 0, i.e. wj＝w0At this time, the training error J is relative to w0The derivative of (c) is:
s202, the predicted value is mapped to a target value.
Specifically, for comparison, the predicted value is mapped to a target value in the interval of [0,1], and the preset threshold may be set to 0.5.
S203, judging whether the target value exceeds a preset threshold value; if yes, executing S204; if not, go to S205.
And S204, determining that the data size of the I/O data exceeds a preset threshold value.
S205, determining that the data size of the I/O data does not exceed a preset threshold.
The embodiment can be realized by adopting Thin rendering bottom layer technology (namely Thin bottom layer), and the technology can realize the sharing of the storage space.
Therefore, after the I/O data to be stored are acquired, the method and the device for storing the I/O data judge whether the data size of the I/O data exceeds a preset threshold value by using a logistic regression algorithm; and if the data volume of the I/O data exceeds a preset threshold value, processing the I/O data by using a linear regression algorithm to obtain continuous LBA identification information, and distributing continuous LBAs for the I/O data according to the continuous LBA identification information. In the process of storing the I/O data, continuous LBAs are distributed to large blocks of I/O data (namely, the I/O data with the data volume exceeding the preset threshold), so that the large blocks of I/O data can be continuously stored in a physical space, and the trouble of addressing of subsequent read-write operation can not be brought. Moreover, the logistic regression algorithm and the linear regression algorithm can accurately and quickly determine the continuous LBAs, so that the accuracy and the efficiency of LBA allocation are improved. The logistic regression algorithm and the linear regression algorithm in this embodiment may be replaced with a neural network algorithm, and the accuracy and efficiency of LBA allocation can be further improved by using the neural network algorithm.
Referring to fig. 3, an embodiment of the present application discloses another LBA allocation method, including:
s301, collecting I/O data;
s302, judging the size of the I/O data by using a logistic regression algorithm;
s303, if the size is larger, distributing the LBAs by using a linear regression algorithm;
and S304, if the size is smaller, distributing the LBAs by utilizing a consistent hash algorithm.
Specifically, the judging of the size of the I/O data by the logistic regression algorithm comprises the following steps: using a target formula y*＝w0+w1×x1+w2×x2Calculating a predicted value, and mapping the predicted valueIs [0,1]]A target value within the interval; judging whether the target value exceeds 0.5; if yes, determining that the I/O data is larger; if not, determining that the I/O data is smaller.
When the linear regression algorithm is used for distributing the LBAs, the input of the algorithm is the data size of the I/O data and the current IO rate. When the LBA is allocated by using the consistent hash algorithm, the input of the algorithm is an identification value (key) of the I/O data, and the order of the key is increased. The consistent hash algorithm is stable, distribution in all physical disk ranges can be guaranteed, and the performance of the hard disk is fully utilized.
Therefore, according to the embodiment, the LBA is accurately and quickly allocated by using the logistic regression algorithm and the linear regression algorithm, the accuracy and the efficiency of LBA allocation are improved, and the utilization rate of the hard disk can also be improved.
In the following, a LBA allocation apparatus provided in an embodiment of the present application is introduced, and an LBA allocation apparatus described below and an LBA allocation method described above may refer to each other.
Referring to fig. 4, an embodiment of the present application discloses an LBA allocation apparatus, including:
an obtaining module 401, configured to obtain I/O data to be stored;
a judging module 402, configured to judge whether the data size of the I/O data exceeds a preset threshold by using a logistic regression algorithm;
the allocating module 403 is configured to, if the data size of the I/O data exceeds the preset threshold, process the I/O data by using a linear regression algorithm to obtain continuous LBA identification information, and allocate a continuous LBA to the I/O data according to the continuous LBA identification information.
In one embodiment, the determining module includes:
the computing unit is used for computing a predicted value of the I/O data by using a logistic regression algorithm;
a mapping unit for mapping the predicted value to a target value;
a judging unit for judging whether the target value exceeds a preset threshold value;
a first determining unit, configured to determine that a data size of the I/O data exceeds a preset threshold if the target value exceeds the preset threshold;
and the second determining unit is used for determining that the data size of the I/O data does not exceed the preset threshold value if the target value does not exceed the preset threshold value.
In a specific embodiment, the computing unit is specifically configured to:
calculating the predicted value according to a target formula, wherein the target formula is as follows: y is*＝w0+w1×x1+w2×x2；
Wherein, y*To predict value, w0To preset offset, w1A first parameter, w, corresponding to the size of the data volume of the I/O data2A second parameter, x, corresponding to the current I/O rate1Is the data size, x, of the I/O data2Is the current I/O rate.
In a specific embodiment, the allocation module is specifically configured to:
and inputting the data size of the I/O data and the current IO rate into a linear regression algorithm, and outputting continuous LBA identification information.
In a specific embodiment, the method further comprises the following steps:
and the storage module is used for storing the I/O data to the continuous physical space corresponding to the continuous LBA.
In a specific embodiment, the method further comprises the following steps:
and the target LBA allocation module is used for allocating the target LBA for the I/O data by using a consistent hash algorithm if the data size of the I/O data does not exceed a preset threshold value.
In one embodiment, the target LBA allocation module includes:
a third determining unit, configured to determine an identification value corresponding to the I/O data;
the processing unit is used for processing the identification value by utilizing a consistent Hash algorithm to obtain target identification information of the target LBA;
and the distribution unit is used for distributing the target LBA for the I/O data according to the target identifier.
For more specific working processes of each module and unit in this embodiment, reference may be made to corresponding contents disclosed in the foregoing embodiments, and details are not described here again.
Therefore, the embodiment provides an LBA allocation apparatus, which can implement continuous storage of large blocks of I/O data in a physical space, and does not bring trouble to addressing of subsequent read-write operations. The logistic regression algorithm and the linear regression algorithm can also improve the accuracy and efficiency of LBA allocation. Accordingly, the LBA distribution apparatus, and LBA distribution readable storage medium provided in the present application also have the technical effects described above.
In the following, a LBA allocation apparatus provided in an embodiment of the present application is introduced, and an LBA allocation apparatus described below and an LBA allocation method and apparatus described above may refer to each other.
Referring to fig. 5, an embodiment of the present application discloses an LBA allocation apparatus, including:
a memory 501 for storing a computer program;
a processor 502 for executing the computer program to implement the method disclosed by any of the embodiments described above.
In the following, a readable storage medium provided by an embodiment of the present application is introduced, and a readable storage medium described below and an LBA allocation method, apparatus, and device described above may be referred to each other.
A readable storage medium for storing a computer program, wherein the computer program when executed by a processor implements the LBA allocation method disclosed in the foregoing embodiments. For the specific steps of the method, reference may be made to the corresponding contents disclosed in the foregoing embodiments, which are not described herein again.
References in this application to "first," "second," "third," "fourth," etc., if any, are intended to distinguish between similar elements and not necessarily to describe a particular order or sequence. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises" and "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, or apparatus.
It should be noted that the descriptions in this application referring to "first", "second", etc. are for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present application.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of readable storage medium known in the art.
The principle and the implementation of the present application are explained herein by applying specific examples, and the above description of the embodiments is only used to help understand the method and the core idea of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (8)
1. A LBA allocation method, comprising:
acquiring I/O data to be stored;
judging whether the data size of the I/O data exceeds a preset threshold value by using a logistic regression algorithm;
if so, processing the I/O data by using a linear regression algorithm to obtain continuous LBA identification information, and distributing continuous LBAs to the I/O data according to the continuous LBA identification information;
wherein, the judging whether the data size of the I/O data exceeds a preset threshold value by using a logistic regression algorithm comprises:
calculating a predicted value of the I/O data by using the logistic regression algorithm;
mapping the predicted value to a target value;
judging whether the target value exceeds the preset threshold value;
if so, determining that the data size of the I/O data exceeds the preset threshold;
if not, determining that the data size of the I/O data does not exceed the preset threshold;
wherein the calculating the predicted value of the I/O data using the logistic regression algorithm comprises:
calculating the predicted value according to a target formula, wherein the target formula is as follows:y*＝w 0+w 1×x 1+w 2×x 2；
wherein the content of the first and second substances,yis the predicted value of the current time,w 0in order to set the offset amount in advance,w 1a first parameter corresponding to the size of the data size of the I/O data,w 2a second parameter corresponding to the current I/O rate,x 1is the data size of the I/O data,x 2is the current I/O rate.
2. The LBA allocation method according to claim 1, wherein the processing the I/O data by using a linear regression algorithm to obtain consecutive LBA identification information includes:
and inputting the data size of the I/O data and the current IO rate into the linear regression algorithm, and outputting the continuous LBA identification information.
3. The LBA allocation method according to claim 1, wherein after allocating consecutive LBAs to the I/O data according to the consecutive LBA identification information, the method further includes:
and storing the I/O data to a continuous physical space corresponding to the continuous LBA.
4. The LBA allocation method according to any one of claims 1 to 3, further comprising:
and if the data volume of the I/O data does not exceed the preset threshold value, distributing the target LBA to the I/O data by using a consistent hash algorithm.
5. The LBA allocation method according to claim 4, wherein the allocating a target LBA to the I/O data using a consistent hashing algorithm includes:
determining an identification value corresponding to the I/O data;
processing the identification value by using the consistent hash algorithm to obtain target identification information of the target LBA;
and distributing the target LBA for the I/O data according to the target identification.
6. An LBA allocation apparatus, comprising:
the acquisition module is used for acquiring the I/O data to be stored;
the judging module is used for judging whether the data size of the I/O data exceeds a preset threshold value by utilizing a logistic regression algorithm;
the allocation module is used for processing the I/O data by using a linear regression algorithm to obtain continuous LBA identification information if the data size of the I/O data exceeds the preset threshold value, and allocating continuous LBAs to the I/O data according to the continuous LBA identification information;
wherein, the judging module includes:
a calculation unit for calculating a predicted value of the I/O data using the logistic regression algorithm;
a mapping unit for mapping the predicted value to a target value;
the judging unit is used for judging whether the target value exceeds the preset threshold value;
a first determining unit, configured to determine that a data size of the I/O data exceeds a preset threshold if the target value exceeds the preset threshold;
a second determining unit, configured to determine that the data size of the I/O data does not exceed a preset threshold if the target value does not exceed the preset threshold;
wherein the computing unit is specifically configured to:
calculating the predicted value according to a target formula, wherein the target formula is as follows:y*＝w 0+w 1×x 1+w 2×x 2；
wherein the content of the first and second substances,yis the predicted value of the current time,w 0in order to set the offset amount in advance,w 1a first parameter corresponding to the size of the data size of the I/O data,w 2a second parameter corresponding to the current I/O rate,x 1is the data size of the I/O data,x 2is the current I/O rate.
7. An LBA allocation apparatus, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the LBA allocation method according to any one of claims 1 to 5.
8. A readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the LBA allocation method according to any one of claims 1 to 5.
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| WO2017133615A1 (en) * | 2016-02-03 | 2017-08-10 | 腾讯科技（深圳）有限公司 | Service parameter acquisition method and apparatus |
| CN109446115A (en) * | 2018-11-13 | 2019-03-08 | 郑州云海信息技术有限公司 | A kind of mapping table management method, device and computer readable storage medium |
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| WO2017133615A1 (en) * | 2016-02-03 | 2017-08-10 | 腾讯科技（深圳）有限公司 | Service parameter acquisition method and apparatus |
| CN109446115A (en) * | 2018-11-13 | 2019-03-08 | 郑州云海信息技术有限公司 | A kind of mapping table management method, device and computer readable storage medium |
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