CN113485648A - Storage resource control system based on cloud platform - Google Patents
Storage resource control system based on cloud platform Download PDFInfo
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- CN113485648A CN113485648A CN202110793459.1A CN202110793459A CN113485648A CN 113485648 A CN113485648 A CN 113485648A CN 202110793459 A CN202110793459 A CN 202110793459A CN 113485648 A CN113485648 A CN 113485648A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
- G06F3/062—Securing storage systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0628—Interfaces specially adapted for storage systems making use of a particular technique
- G06F3/0638—Organizing or formatting or addressing of data
- G06F3/064—Management of blocks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0668—Interfaces specially adapted for storage systems adopting a particular infrastructure
- G06F3/067—Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]
Abstract
The invention relates to a storage resource control system based on a cloud platform, which comprises M controllers deployed on M storage servers and a central node in communication connection with the M controllers, wherein each storage server comprises a plurality of disks, each controller is stored with an available disk I D list of the corresponding storage server and a disk failure probability corresponding to each disk I D, the available disks refer to disks with the disk residual space exceeding the size of a preset data block to be stored, and the central node comprises a processor and a memory stored with a computer program. The invention reduces the probability of the simultaneous failure of the disks storing the same data and improves the safety of data storage.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a storage resource control system based on a cloud platform.
Background
The existing storage technology based on a cloud platform mainly adopts a cloud disk three-copy technology (https:// support. huaweiicloud. com/product desc-evs/evs _01_0056.html), but the cloud disk three-copy technology has the defect (https:// zhuanlan. zhihu. com/p/354358093), and for a storage system with a certain scale, the event that two mechanical disk faults occur simultaneously cannot be avoided, and particularly after the system operates for a certain period of time, the probability of double or triple disk faults rises sharply along with the aging of hardware. In addition, when a large-scale power failure occurs in the storage system or a storage node is crashed unexpectedly, a plurality of mechanical disks may be damaged at the same time, and data security of the three-copy distributed storage is endangered. Therefore, on the basis of three-copy distributed storage, how to reduce the probability of simultaneous failure of the disks storing the same data and improve the safety of data storage becomes an urgent technical problem to be solved.
Disclosure of Invention
The invention aims to provide a storage resource control system based on a cloud platform, which reduces the probability of simultaneous failure of disks storing the same data and improves the safety of data storage.
According to a first aspect of the present invention, there is provided a storage resource control system based on a cloud platform, including: the system comprises M controllers deployed on M storage servers and a central node in communication connection with the M controllers, wherein each storage server comprises a plurality of disks, each controller stores an available disk ID list of the corresponding storage server and a disk failure probability corresponding to each disk ID, the available disks refer to disks with disk residual space exceeding the size of a preset data block to be stored, the central node comprises a processor and a memory stored with a computer program, and when the central node receives a storage request of the data block to be stored, the processor executes the computer program to realize the following steps:
s1, acquiring three copies of storage servers, namely Host1, Host2 and Host3, corresponding to the data blocks to be stored;
step S2, obtaining the available disk ID lists and the disk failure probabilities of Host1, Host2 and Host 3:
Host1＝{(ID11，p11)，(ID12，p12)，......，(ID1X，p1X)}
Host2＝{(ID21，p21)，(ID22，p22)，......，(ID2Y，p2Y)}
Host3＝{(ID31，p31)，(ID32，p32)，......，(ID3Z，p3Z)}
wherein, ID1xID, p1, representing the x-th available disk of Host1xTo representThe disk failure probability of the xth available disk of the Host1, wherein X represents the number of the available disks of the Host1, and the value range of X is 1 to X; ID1yID, p1, representing the y-th available disk of Host2yThe disk failure probability of the Y-th available disk of the Host2 is shown, Y shows the number of the available disks of the Host2, and the value range of Y is 1 to Y; ID1zID, p1, representing the z-th available disk of Host3zThe disk failure probability of the Z-th available disk of Host3 is shown, Z shows the number of available disks of Hos3, and the value range of Z is 1 to Z;
step S3, determining an available disk ID1 from the list of available disk IDs of Host1, Host2 and Host3 and the disk failure probability of the available disk ID1 from the list of Host1, Host2 and Host3 respectivelyf(x)、ID2f(y)、ID3f(z)And obtaining the magnetic disks of the three copies of the data block to be stored.
Compared with the prior art, the invention has obvious advantages and beneficial effects. By means of the technical scheme, the storage resource control system based on the cloud platform can achieve considerable technical progress and practicability, has industrial wide utilization value, and at least has the following advantages:
the invention determines the disks of the three copies of the data block to be stored by maintaining the available disk ID list of each storage server and the disk failure probability corresponding to each disk ID and based on the available disk ID lists of the three copies of servers and the disk failure probability corresponding to each disk ID, thereby reducing the probability of the simultaneous failure of the disks storing the same data and improving the safety of data storage.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic diagram of a storage resource control system based on a cloud platform according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description will be given to a specific implementation and effects of a cloud platform based storage resource control system according to the present invention with reference to the accompanying drawings and preferred embodiments.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
According to an embodiment of the present invention, a storage resource control system based on a cloud platform is provided, as shown in fig. 1, including M controllers deployed on M storage servers, and a central node in communication connection with the M controllers, where each storage server includes a plurality of disks, and each controller stores therein an available disk ID list of the corresponding storage server and a disk failure probability corresponding to each disk ID. The available disk refers to a disk whose disk remaining space exceeds a preset size of a data block to be stored, where the size of the data block is a size preset by a system, for example, each data block is 4M (megabyte), when a file is stored, for example, the size of the file to be stored is 1G, the central node needs to divide the 1G file to be stored into 256 data blocks to be stored of 4M, and then selects three corresponding copies of the disk for each data block to be stored. It should be noted that the disk failure probability of each disk ID is directly obtained by using the existing disk failure probability prediction algorithm, for example, KNN is used to predict the disk failure probability, which may be specifically referred tohttps://blog.csdn.net/ buchidanhuang/article/details/97812843And will not be described herein. The central node comprises a processor and a memory storing a computer program, when the central node receives a data block to be storedWhen the storage request is made, the processor executes the computer program to realize the following steps:
s1, acquiring three copies of storage servers, namely Host1, Host2 and Host3, corresponding to the data blocks to be stored;
in step S1, the method distribution mechanism of the Host1, the Host2, and the Host3 is according to the existing algorithm straight line such as Consistent Hashing algorithm (Consistent Hashing) or CRUSH algorithm, and specifically, reference may be made to the existing algorithm straight linehttps:// support.huaweicloud.com/productdesc-evs/evs_01_0056.htmlAnd will not be described herein.
Step S2, obtaining the available disk ID lists and the disk failure probabilities of Host1, Host2 and Host 3:
Host1＝{(ID11，p11)，(ID12，p12)，......，(ID1X，p1X)}
Host2＝{(ID21，p21)，(ID22，p22)，......，(ID2Y，p2Y)}
Host3＝{(ID31，p31)，(ID32，p32)，......，(ID3Z，p3Z)}
wherein, ID1xID, p1, representing the x-th available disk of Host1xThe method comprises the steps of representing the disk failure probability of the xth available disk of Host1, wherein X represents the number of the available disks of Host1, and the value range of X is 1-X; ID1yID, p1, representing the y-th available disk of Host2yThe disk failure probability of the Y-th available disk of the Host2 is shown, Y shows the number of the available disks of the Host2, and the value range of Y is 1 to Y; ID1zID, p1, representing the z-th available disk of Host3zThe disk failure probability of the Z-th available disk of Host3 is shown, Z shows the number of available disks of Hos3, and the value range of Z is 1 to Z;
step S3, determining an available disk ID1 from the list of available disk IDs of Host1, Host2 and Host3 and the disk failure probability of the available disk ID1 from the list of Host1, Host2 and Host3 respectivelyf(x)、ID2f(y)、ID3f(z)And obtaining the magnetic disks of the three copies of the data block to be stored.
The embodiment of the invention determines the disks of the three copies of the data block to be stored by maintaining the available disk ID list of each storage server and the disk failure probability corresponding to each disk ID and based on the available disk ID lists of the three copies of servers and the disk failure probability corresponding to each disk ID, thereby reducing the probability of the simultaneous failure of the disks storing the same data and improving the safety of data storage.
In the step S2, the obtaining of the available disk ID lists and the disk failure probabilities of the Host1, the Host2, and the Host3 includes the following two implementation manners:
the first embodiment,
Each controller respectively maintains an available disk ID list and a disk failure probability of a corresponding storage server, and needs to acquire the available disk ID list and the disk failure probability corresponding to Host1, Host2, and Host3, and the central node directly acquires the available disk ID list and the disk failure probability from the corresponding controller, specifically, step S2 includes:
step S21, the central node obtains the available disk ID lists and the disk failure probabilities of the Host1, the Host2, and the Host3 from the controllers corresponding to the Host1, the Host2, and the Host 3.
In the first embodiment, each controller may update the available disk ID list and the disk failure probability of the corresponding storage server in real time or at regular time, and the central node acquires the available disk ID list and the disk failure probability from the corresponding storage server according to a requirement.
The second embodiment,
The central node includes a database, configured to store the available disk ID list and the disk failure probability of each storage server, and when the controller of each storage server updates the corresponding available disk ID list and disk failure probability, the database is updated synchronously, where step S2 includes:
and step S22, the central node directly acquires the usable disk ID lists and the disk failure probabilities of the Host1, the Host2 and the Host3 from the database.
In the second embodiment, the central node maintains the ID list of the available disks and the disk failure probability in the global storage server, so that the central server can obtain the ID lists of the available disks and the disk failure probabilities of the Host1, the Host2 and the Host3 based on the to-be-processed data block without communicating with the controller, and further determine the disks of the three copies of the to-be-stored data block, thereby improving the data storage efficiency.
The following determines the disks of the three copies of the data block to be stored by several specific embodiments:
the first embodiment,
In step S2, the disk failure probabilities of the available disk IDs of the Host1, the Host2, and the Host3 satisfy p11≤p12≤，......，≤p1X，p21≤p22≤，......，≤p2Y，p31≤p32≤，......，≤p3ZMore preferably, p11≤p21≤p31And p2Y≥p3Z. The step S3 includes:
step S31, setting f (x) 1, f (Y) Y, f (z) M,
or
It should be noted that, in the first embodiment, the three disks respectively select the disk with the lowest failure probability, the highest failure probability, and the disk with the middle failure probability among the Host1, the Host2, and the Host3, so that it is possible to prevent the three disks from using the disk with the higher failure probability in the random selection process.
As an embodiment, D1 may specifically be a maximum probability threshold preset by the system for a tolerable data block to fail.
As an alternative to the above-described embodiment,
d0 is the user-specified maximum probability threshold for failure of a storage file that can be tolerated, Fsize is the file size stored by the user, Bsize is the data block size of the system, thus it can be seen that D1 is dynamically changingI.e., the corresponding D1 may be set based on different data storage scenarios. User designation means that when a user stores one or a batch of files, the system can be provided with a human-computer interaction interface, the user inputs the maximum probability value of the failure of the stored files in the human-computer interface, and the human-computer interface can be a GUI interface or a command line interface. The user specification may also be a maximum probability value of a failure of a storage file associated with the account of the user, which is preset by the user.
Example II,
On the basis of the first embodiment, the step S31 may further include:
step S311, comparison p11*p2Y*p3MAnd D1, D1 represents a preset failure probability threshold if p11*p2Y*p3M<D1, then set f (x) 1, f (Y) Y, f (z) M, if p11*p2Y*p3MIf D1 is equal to or greater than the preset value, go to step S312;
step S312, compare p11*p2Y*p31And D1, if p11*p2Y*p31<D1, setting f (Y) to Y, setting f (z) to p3f(z)<D1/(p11*p2Y) And argmax (p 3)f(z)-D1/(p11*p2Y) If p11*p2Y*p31If D1 is equal to or more than D, go to step S313;
as an example, in step S312, f (z) is from ID3MTo ID31The direction of (c) is obtained through traversal, so that the step S312 can obtain the f (z) meeting the condition as soon as possible, the calculation processing amount is reduced, and the calculation processing efficiency is improved.
Step S313, set f (z) to 1, set f (y) such that p2f(y)<D1/(p11*p31) And argmax (p 2)f(y)-D1/(p11*p31))。
As an embodiment, in step S313, f (y) is obtained by binary search, which enables step S313 to obtain f (y) meeting the condition as soon as possible, reduces the amount of calculation processing, and improves the efficiency of calculation processing. It should be noted that binary search is an existing algorithm, and is not described herein again.
As an example, if the satisfactory f (y) can not be found in step S313, the current Host1, Host2, Host3 are rejected, or max (min) (p 1) is rejectedi),min(p2j),min(p3k) ) returns to perform step S1 to retrieve the Host1, Host2, and Host 3.
The second embodiment is optimized on the basis of the first embodiment, so that the disk loads of the storage servers Host1, Host2 and Host3 are more balanced, and the overall requirement of the storage failure rate of the data blocks is met.
Example III,
On the basis of the first embodiment, the step S31 includes:
step S301, compare p11*p2Y*p3MAnd D1, D1 represents a preset failure probability threshold if p11*p2Y*p3M<D1, then set f (x) 1, f (Y) Y, f (z) M, if p11*p2Y*p3MIf D1 is equal to or more than the preset value, executing step S302;
step S302, compare p11*p21*p31And D1 if p11*p21*p31<D1, if f (x) is 1, f (y) is 1, f (z) is 1, if p11*p21*p31If D1 is equal to or more than the preset value, executing step S303;
step S303, removing the current Host1, Host2 and Host3, or removing max (min) (p 1)i),min(p2j),min(p3k) ) the corresponding storage server, returns to execute step S1.
The third embodiment is also implemented on the basis of the first embodiment, and compared with the second embodiment, the algorithm can be simplified, and the calculation load of the central node is reduced.
As an embodiment, the step S2 may further include:
step S30, mixing min (p 1)x)*min(p2y)*min(p3z) Comparison with D1, if min (p 1)x)*min(p2y)*min(p3z) If less than D1, execute stepS3, otherwise, eliminating max (min (p 1)x),min(p2y),min(p3z) ) and returns to perform step S1. Therefore, under the condition that the currently selected Host1, Host2 and Host3 are not ideal, the file is directly reselected, useless calculation processing is reduced, and the file storage efficiency is improved.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A storage resource control system based on a cloud platform is characterized in that,
the method comprises the following steps: the system comprises M controllers deployed on M storage servers and a central node in communication connection with the M controllers, wherein each storage server comprises a plurality of disks, each controller stores an available disk ID list of the corresponding storage server and a disk failure probability corresponding to each disk ID, the available disks refer to disks with disk residual space exceeding the size of a preset data block to be stored, the central node comprises a processor and a memory stored with a computer program, and when the central node receives a storage request of the data block to be stored, the processor executes the computer program to realize the following steps:
s1, acquiring three copies of storage servers, namely Host1, Host2 and Host3, corresponding to the data blocks to be stored;
step S2, obtaining the available disk ID lists and the disk failure probabilities of Host1, Host2 and Host 3:
Host1＝{(ID11，p11)，(ID12，p12)，......，(ID1X，p1X)}
Host2＝{(ID21，p21)，(ID22，p22)，......，(ID2Y，p2Y)}
Host3＝{(ID31，p31)，(ID32，p32)，......，(ID3Z，p3Z)}
wherein, ID1xID, p1, representing the x-th available disk of Host1xThe method comprises the steps of representing the disk failure probability of the xth available disk of Host1, wherein X represents the number of the available disks of Host1, and the value range of X is 1-X; ID1yID, p1, representing the y-th available disk of Host2yThe disk failure probability of the Y-th available disk of the Host2 is shown, Y shows the number of the available disks of the Host2, and the value range of Y is 1 to Y; ID1zID, p1, representing the z-th available disk of Host3zThe disk failure probability of the Z-th available disk of Host3 is shown, Z shows the number of available disks of Hos3, and the value range of Z is 1 to Z;
step S3, determining an available disk ID1 from the list of available disk IDs of Host1, Host2 and Host3 and the disk failure probability of the available disk ID1 from the list of Host1, Host2 and Host3 respectivelyf(x)、ID2f(y)、ID3f(z)And obtaining the magnetic disks of the three copies of the data block to be stored.
2. The system of claim 1,
in step S2, the disk failure probabilities of the available disk IDs of the Host1, the Host2, and the Host3 satisfy p11≤p12≤，......，≤p1X，p21≤p22≤，......，≤p2Y，p31≤p32≤，......，≤p3Z，p11≤p21≤p31And p2Y≥p3Z；
The step S3 includes:
step S31, setting f (x) 1, f (Y) Y, f (z) M,
3. the system of claim 2,
the step S31 includes:
step S311, comparison p11*p2Y*p3MAnd D1, D1 represents a preset failure probability threshold if p11*p2Y*p3M<D1, then set f (x) 1, f (Y) Y, f (z) M, if p11*p2Y*p3MIf D1 is equal to or greater than the preset value, go to step S312;
step S312, compare p11*p2Y*p31And D1, if p11*p2Y*p31<D1, setting f (Y) to Y, setting f (z) to p3f(z)<D1/(p11*p2Y) And argmax (p 3)f(z)-D1/(p11*p2Y) If p11*p2Y*p31If D1 is equal to or more than D, go to step S313;
step S313, set f (z) to 1, set f (y) such that p2f(y)<D1/(p11*p31) And argmax (p 2)f(y)-D1/(p11*p31))。
4. The system of claim 3,
in step S312, f (z) slave ID3MTo ID31Is obtained through traversal of the direction of (c).
5. The system of claim 3,
in step S313, f (y) is obtained by way of binary search.
6. The system of claim 3,
if the required f (y) can not be found in the step S313, the current Host1, Host2, Host3 or max (min) (p 1) is eliminatedi),min(p2j),min(p3k) Corresponding storage serviceAnd returns to step S1.
7. The system of claim 2,
the step S31 includes:
step S301, compare p11*p2Y*p3MAnd D1, D1 represents a preset failure probability threshold if p11*p2Y*p3M<D1, then set f (x) 1, f (Y) Y, f (z) M, if p11*p2Y*p3MIf D1 is equal to or more than the preset value, executing step S302;
step S302, compare p11*p21*p31And D1 if p11*p21*p31<D1, if f (x) is 1, f (y) is 1, f (z) is 1, if p11*p21*p31If D1 is equal to or more than the preset value, executing step S303;
step S303, removing the current Host1, Host2 and Host3, or removing max (min) (p 1)i),min(p2j),min(p3k) ) the corresponding storage server, returns to execute step S1.
8. The system of claim 1,
step S30, mixing min (p 1)x)*min(p2y)*min(p3z) Compared with D1, D1 represents a preset failure probability threshold if min (p 1)x)*min(p2y)*min(p3z) If less than D1, go to step S3, otherwise, reject max (min (p 1)x),min(p2y),min(p3z) ) and returns to perform step S1.
9. The system according to any one of claims 3 to 8,
d1 is a maximum probability threshold preset by the system for a tolerable data block to fail.
10. The system according to any one of claims 3 to 8,
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| CN103763155A (en) * | 2014-01-24 | 2014-04-30 | 国家电网公司 | Multi-service heartbeat monitoring method for distributed type cloud storage system |
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| CN112579551A (en) * | 2019-09-30 | 2021-03-30 | 北京金山云网络技术有限公司 | Data storage and reading method, device, client, management server and system |
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