CN115129260A - Hardware management method, device, equipment and medium for distributed storage cluster - Google Patents
Hardware management method, device, equipment and medium for distributed storage cluster Download PDFInfo
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- 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]
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- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
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- G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
- G06F3/0614—Improving the reliability of storage systems
- G06F3/0616—Improving the reliability of storage systems in relation to life time, e.g. increasing Mean Time Between Failures [MTBF]
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- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
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Abstract
The application discloses a hardware management method, a device, equipment and a medium of a distributed storage cluster, and relates to the technical field of information. The method comprises the following steps: acquiring target hardware data information corresponding to target hardware in a distributed storage cluster; determining parameters influencing the service life of the target hardware according to the target hardware data information to obtain service life reference value parameters of the target hardware; comparing the life reference value parameter with a life standard value parameter of target hardware to obtain a comparison result, and determining a life analysis frequency aiming at the target hardware according to the comparison result; and predicting the service life of the target hardware according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware based on the service life analysis frequency. By the scheme, the service life analysis frequency can be flexibly adjusted, hardware management in the distributed storage cluster can be effectively carried out, and failure prediction can be carried out on the hardware in use.
Description
Technical Field
The present invention relates to the field of information technologies, and in particular, to a method, an apparatus, a device, and a medium for managing hardware of a distributed storage cluster.
Background
Distributed storage is a data storage technology that uses disk space on each machine in an enterprise through a network, and these distributed storage resources constitute virtual storage devices, and data is distributed and stored in each corner of the enterprise. At present, the scale of a distributed storage cluster is continuously increased, the hardware configuration related to storage equipment is relatively complex, the data volume of various hardware information in the large-scale cluster is large, and when data acquisition is performed on each node, the situations of abnormal hardware adaptation, untimely hardware information acquisition and report and large system load can occur. When hardware information is collected, a large amount of information makes analysis and processing difficult for operation and maintenance personnel. In summary, how to effectively perform hardware management in the distributed storage cluster and perform failure prediction on the hardware in use is a problem to be further solved.
Disclosure of Invention
In view of this, an object of the present invention is to provide a method, an apparatus, a device, and a medium for managing hardware of a distributed storage cluster, which can effectively perform hardware management in the distributed storage cluster and perform failure prediction on hardware in use. The specific scheme is as follows:
in a first aspect, the present application discloses a hardware management method for a distributed storage cluster, including:
acquiring target hardware data information corresponding to target hardware in a distributed storage cluster;
determining parameters influencing the service life of the target hardware according to the target hardware data information to obtain service life reference value parameters of the target hardware;
comparing the life reference value parameter with a life standard value parameter of the target hardware to obtain a comparison result, and determining a life analysis frequency aiming at the target hardware according to the comparison result;
and predicting the service life of the target hardware according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware based on the service life analysis frequency.
Optionally, before obtaining the target hardware data information corresponding to the target hardware in the distributed storage cluster, the method further includes:
and acquiring target hardware basic information of target hardware through a hardware basic information acquisition command, and judging whether the target hardware is matched with other hardware in the distributed storage cluster according to the target hardware basic information.
Optionally, the obtaining, by the hardware basic information obtaining command, target hardware basic information of the target hardware, and determining, according to the target hardware basic information, whether the target hardware is adapted to other hardware in the distributed storage cluster, includes:
acquiring target hardware basic information of target hardware through a hardware basic information acquisition command, and judging whether the target hardware has a fault according to the target hardware basic information;
and if the target hardware has no fault, processing the basic information of the target hardware into basic information of a target format, and comparing the basic information of the target format with data in a preset hardware compatible database to judge whether the target hardware is matched with other hardware in the distributed storage cluster.
Optionally, after obtaining the target hardware basic information of the target hardware through the hardware basic information obtaining command and judging whether the target hardware is adapted to other hardware in the distributed storage cluster according to the target hardware basic information, the method further includes:
and adjusting the hardware adaptation interface view corresponding to the distributed storage cluster to ensure that the hardware data information corresponding to various types of hardware in the distributed storage cluster is normally acquired.
Optionally, the obtaining target hardware data information corresponding to target hardware in the distributed storage cluster includes:
acquiring the target hardware data information corresponding to the target hardware in the distributed storage cluster according to a preset data acquisition period, and storing the target hardware data information into a preset data information database;
correspondingly, the predicting the service life of the target hardware according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware based on the service life analysis frequency includes:
and reading the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware from the preset data information database, and predicting the service life of the target hardware according to the target hardware data information and the historical hardware data information based on the service life analysis frequency.
Optionally, the method further includes:
adjusting the preset data acquisition period according to the change frequency of the target hardware data information in the preset data information database;
if the change frequency of the target hardware data information is lower than a preset change threshold, correspondingly prolonging the preset data acquisition period according to the change frequency;
and if the change frequency of the target hardware data information is higher than a preset change threshold, correspondingly shortening the preset data acquisition period according to the change frequency.
Optionally, the predicting the life of the target hardware according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware based on the life analysis frequency includes:
and if the historical hardware data information of the historical hardware corresponding to the target hardware cannot be found in the preset data information database, predicting the service life of the target hardware according to the target hardware data information and the environmental information of the target hardware.
In a second aspect, the present application discloses a hardware management apparatus for a distributed storage cluster, including:
the hardware information acquisition module is used for acquiring target hardware data information corresponding to target hardware in the distributed storage cluster;
the service life reference value determining module is used for determining parameters influencing the service life of the target hardware according to the target hardware data information so as to obtain service life reference value parameters of the target hardware;
the frequency determination module is used for comparing the life reference value parameter with a life standard value parameter of target hardware to obtain a comparison result, and determining the life analysis frequency aiming at the target hardware according to the comparison result;
and the service life prediction module is used for predicting the service life of the target hardware according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware based on the service life analysis frequency.
In a third aspect, the present application discloses an electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the hardware management method of the distributed storage cluster disclosed in the foregoing.
In a fourth aspect, the present application discloses a computer readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the steps of the hardware management method of a distributed storage cluster as disclosed in the foregoing.
When the hardware management of the distributed storage cluster is carried out, firstly, target hardware data information corresponding to target hardware in the distributed storage cluster is obtained, parameters influencing the service life of the target hardware are determined according to the target hardware data information to obtain service life reference value parameters of the target hardware, then the service life reference value parameters are compared with the service life standard value parameters of the target hardware to obtain comparison results, the service life analysis frequency aiming at the target hardware is determined according to the comparison results, and finally the service life prediction aiming at the target hardware is carried out according to the service life analysis frequency and historical hardware data information of historical hardware corresponding to the target hardware. Therefore, when the hardware management of the distributed storage cluster is carried out, firstly, the target hardware data information of the target hardware in the distributed storage cluster is obtained, the parameters related to the service life of the target hardware are determined from the target hardware data information and serve as the service life reference value parameters of the target hardware, then the service life standard value parameters of the target hardware are compared with the service life reference value parameters of the target hardware, the service life analysis frequency aiming at the target hardware is determined through the comparison result, and finally, the service life of the target hardware is predicted according to the target hardware data information and the historical hardware data information of the historical hardware based on the service life analysis frequency. Therefore, when the hardware management of the distributed storage cluster is carried out, the target hardware data information of the target hardware is obtained, the service life reference value parameter is determined from the target hardware data information, and the service life analysis frequency is determined according to the comparison result of the service life reference value parameter of the target hardware and the service life standard value parameter, so that the service life analysis frequency of the target hardware can be flexibly adjusted during the hardware management, the consumption of a large amount of system resources caused by frequent service life analysis is avoided, the analysis of the target hardware adopts the appropriate frequency corresponding to the frequency to timely determine the fault probability and the expected failure time of the target hardware, and a user can conveniently judge the service life of the target hardware and prepare for replacement in advance on the premise of fully using the hardware. In conclusion, the method and the device can effectively manage the hardware in the distributed storage cluster and predict the failure of the hardware in use.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, 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 a hardware management method for a distributed storage cluster according to the present application;
fig. 2 is a flowchart of a specific hardware management method for a distributed storage cluster according to the present disclosure;
fig. 3 is a flowchart of a specific hardware management method for a distributed storage cluster according to the present application;
fig. 4 is a schematic structural diagram of a hardware management apparatus of a distributed storage cluster provided in the present application;
fig. 5 is a block diagram of an electronic device provided in the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
Distributed storage is a data storage technology that uses disk space on each machine in an enterprise through a network, and these distributed storage resources constitute virtual storage devices, and data is distributed and stored in all corners of the enterprise. At present, the scale of a distributed storage cluster is continuously increased, the hardware configuration related to storage equipment is relatively complex, the data volume of various hardware information in a large-scale cluster is large, and when data acquisition is performed on each node, the conditions of abnormal hardware adaptation, untimely hardware information acquisition and reporting, and large system load can occur. When hardware information is collected, a large amount of information makes it difficult for operation and maintenance personnel to analyze and process the hardware information. Therefore, the problem that the hardware management in the distributed storage cluster can be effectively performed and the failure prediction of the hardware in use is performed by the hardware management method of the distributed storage cluster provided by the application is to be further solved.
The embodiment of the invention discloses a hardware management method of a distributed storage cluster, which is shown in figure 1 and comprises the following steps:
step S11: and acquiring target hardware data information corresponding to target hardware in the distributed storage cluster.
In this embodiment, target hardware data information of target hardware in a distributed storage cluster is obtained, where the target hardware is the hardware in the distributed storage cluster, and the target hardware data information of the target hardware is collected by a preset data collection module to obtain the target hardware data information of the target hardware in the distributed storage cluster. By the technical scheme, the target hardware data information is obtained, so that parameters influencing the service life of the target hardware can be determined according to the target hardware data information subsequently, and the service life reference value parameters of the target hardware can be obtained.
Step S12: and determining parameters influencing the service life of the target hardware according to the target hardware data information so as to obtain service life reference value parameters of the target hardware.
In this embodiment, data related to the lifetime of the target hardware is determined from the collected data information of the target hardware, so as to obtain a lifetime reference value parameter of the target hardware. The life reference value parameter is data corresponding to target hardware. By the technical scheme, the life reference value of the target hardware is obtained, so that the life reference value parameter and the life standard value parameter of the target hardware are compared to obtain a comparison result in the following process, and the life analysis frequency of the target hardware is determined according to the comparison result.
Step S13: and comparing the life reference value parameter with the life standard value parameter of the target hardware to obtain a comparison result, and determining the life analysis frequency aiming at the target hardware according to the comparison result.
In this embodiment, the standard life value of the target hardware is a standard life value parameter of a parameter corresponding to the target hardware that is recognized in the industry. Specifically, the life reference value parameter is compared with a life standard value parameter of the target hardware to obtain a comparison result, that is, whether the target hardware is close to the life limit is judged, if the life reference value parameter of the target hardware exceeds the life standard value parameter, it represents that the target hardware is close to the life limit, and then the analysis frequency of the life of the target hardware, which is higher in use, is analyzed; and if the life reference value parameter of the target hardware does not exceed the life standard value parameter, representing that the target hardware is not close to the life limit, analyzing the lower life analysis frequency of the target hardware. It is understood that both the higher frequency of life analysis and the lower frequency of life analysis can be set according to actual use conditions. According to the technical scheme, the service life reference value parameter is compared with the service life standard value parameter of the target hardware to obtain a comparison result, and the service life analysis frequency aiming at the target hardware is determined according to the comparison result, so that the dynamic frequency analysis is carried out on the target hardware, the service life analysis frequency aiming at the target hardware can be flexibly adjusted when the hardware is managed, and a large amount of system resources are prevented from being consumed due to frequent service life analysis.
Step S14: and predicting the service life of the target hardware according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware based on the service life analysis frequency.
In this embodiment, the life prediction is performed on the target hardware data information and the historical hardware data information corresponding to the target hardware according to the life analysis frequency. The historical hardware is replaced before the target hardware is used, namely, the data curves of the current target hardware and the replaced historical hardware in the distributed storage cluster are compared and analyzed, so that the service life of the target hardware is predicted. According to the technical scheme, due to the fact that hardware information parameters are more, historical data are more, for example, the smart information of the magnetic disk has a plurality of parameters, a user can difficultly distinguish effective parameters, the historical hardware data information of the historical hardware corresponding to the target hardware is analyzed, so that the target hardware is analyzed with appropriate frequency corresponding to the historical hardware to determine the fault probability and the expected failure time of the target hardware in time, and the user can conveniently judge the service life of the target hardware and make preparation for replacement in advance on the premise that the hardware is fully used.
As can be seen, in this embodiment, when hardware management of a distributed storage cluster is performed, target hardware data information of target hardware in the distributed storage cluster is first obtained, a parameter related to the life of the target hardware is determined from the target hardware data information and is used as a life reference value parameter of the target hardware, then a life standard value parameter of the target hardware is compared with a life reference value parameter of the target hardware, a life analysis frequency for the target hardware is determined according to a comparison result, and finally, life prediction is performed on the target hardware according to the target hardware data information and historical hardware data information of historical hardware based on the life analysis frequency. Therefore, when the hardware management of the distributed storage cluster is carried out, the target hardware data information of the target hardware is obtained, the life reference value parameter is determined from the target hardware data information, and the life analysis frequency is determined according to the comparison result of the life reference value parameter of the target hardware and the life standard value parameter, so that the life analysis frequency aiming at the target hardware can be flexibly adjusted when the hardware management is carried out, the consumption of a large number of system resources caused by frequent life analysis is avoided, the analysis aiming at the target hardware adopts the appropriate frequency corresponding to the frequency to timely determine the fault probability and the expected failure time of the target hardware, and a user can conveniently judge the life of the target hardware and prepare for replacement in advance on the premise of fully using the hardware. In conclusion, the method and the device can effectively manage the hardware in the distributed storage cluster and predict the failure of the hardware in use.
Referring to fig. 2, an embodiment of the present invention discloses a specific hardware management method for a distributed storage cluster, and this embodiment further explains and optimizes the technical solution with respect to the previous embodiment.
Step S21: and acquiring target hardware basic information of target hardware through a hardware basic information acquisition command, and judging whether the target hardware is matched with other hardware in the distributed storage cluster according to the target hardware basic information.
In this embodiment, the obtaining target hardware basic information of target hardware through the hardware basic information obtaining command, and determining whether the target hardware is adapted to other hardware in the distributed storage cluster according to the target hardware basic information includes: acquiring target hardware basic information of target hardware through a hardware basic information acquisition command, and judging whether the target hardware has a fault according to the target hardware basic information; and if the target hardware has no fault, processing the basic information of the target hardware into basic information of a target format, and comparing the basic information of the target format with data in a preset hardware compatible database to judge whether the target hardware is matched with other hardware in the distributed storage cluster.
It will be appreciated that the hardware in the distributed storage cluster first needs to be adapted before data collection by the hardware can take place. In a distributed storage system, the hardware condition is complicated, and there are problems such as different manufacturers, different supported firmware versions, the maximum number of devices supported by a server, and different SAS cards and RAID cards, so the hardware in the distributed storage cluster needs to be adapted. Specifically, whether the target hardware is available is checked firstly, and if the target hardware has a fault, an alarm prompt of the fault of the target hardware is carried out. And then acquiring basic information of the target hardware, checking whether the target hardware is available or not according to the target hardware, acquiring target hardware basic information of the target hardware through a hardware basic information acquisition command, judging whether the target hardware has a fault or not according to the target hardware basic information, processing hardware basic information with different formats of different manufacturers into target format basic information if the target hardware has no fault, and comparing the target format basic information with data in a preset hardware compatible database to judge whether the target hardware is matched with other hardware in the distributed storage cluster or not. The preset hardware compatible database is a database written in the compatible relation among various types of hardware in advance.
In this embodiment, after determining whether the target hardware is adapted to other hardware in the distributed storage cluster, the method further includes: and adjusting the hardware adaptation interface view corresponding to the distributed storage cluster to ensure that the hardware data information corresponding to various types of hardware in the distributed storage cluster is normally acquired. By the technical scheme, whether the target hardware is adapted or not is judged before the target hardware information is acquired, so that system abnormity caused by insufficient hardware adaptation check can be effectively avoided, and hardware adaptation is made in advance.
Step S22: and acquiring target hardware data information corresponding to the target hardware in the distributed storage cluster.
Step S23: and determining parameters influencing the service life of the target hardware according to the target hardware data information so as to obtain service life reference value parameters of the target hardware.
Step S24: and comparing the life reference value parameter with the life standard value parameter of the target hardware to obtain a comparison result, and determining the life analysis frequency aiming at the target hardware according to the comparison result.
Step S25: and predicting the service life of the target hardware according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware based on the service life analysis frequency.
Therefore, in the embodiment, by judging whether the target hardware is adapted before acquiring the data information of the target hardware, system abnormity caused by insufficient hardware adaptation check can be effectively avoided, and hardware adaptation is made in advance.
Referring to fig. 3, an embodiment of the present invention discloses a specific hardware management method for a distributed storage cluster, and compared with the previous embodiment, this embodiment further describes and optimizes the technical solution.
Step S31: acquiring target hardware data information corresponding to target hardware in the distributed storage cluster according to a preset data acquisition period, and storing the target hardware data information into a preset data information database.
In this embodiment, after acquiring the target hardware data information corresponding to the target hardware in the distributed storage cluster according to a preset data acquisition period and storing the target hardware data information in a preset data information database, the method further includes: adjusting the preset data acquisition period according to the change frequency of the target hardware data information in the preset data information database; if the change frequency of the target hardware data information is lower than a preset change threshold, correspondingly prolonging the preset data acquisition period according to the change frequency; and if the change frequency of the target hardware data information is higher than a preset change threshold, correspondingly shortening the preset data acquisition period according to the change frequency. It can be understood that resource consumption caused by frequent queries can be reduced by means of collection and caching, but information is not updated timely due to an excessively long collection period, and unnecessary consumption is still caused due to an excessively short collection period. Specifically, the preset data acquisition period is analyzed, and if the target hardware data information change frequency of the target hardware is low, the preset data acquisition period is prolonged; and if the change frequency of the target hardware data information of the target hardware is higher, shortening the preset data acquisition period. Further, the collected target hardware data information is stored in a preset data information database of a node where the target hardware of the distributed storage cluster is located, when the stored data exceeds a certain time, representative data in historical data in the preset data information database and data capable of reflecting the change of the target hardware condition are reserved, and other data are cleaned. According to the technical scheme, the target hardware data information corresponding to the target hardware in the distributed storage cluster is collected according to a preset data collection period, and the target hardware data information is stored in a preset data information database, so that the preset data collection period is dynamically adjusted according to the feedback of the target hardware data information, and unnecessary resource consumption in the data collection process is avoided; meanwhile, data cleaning is carried out according to the type of the historical data, and the problem that resources are wasted due to overlarge storage space occupied by the historical data is solved.
Step S32: and determining parameters influencing the service life of the target hardware according to the target hardware data information so as to obtain service life reference value parameters of the target hardware.
Step S33: and comparing the life reference value parameter with the life standard value parameter of the target hardware to obtain a comparison result, and determining the life analysis frequency aiming at the target hardware according to the comparison result.
Step S34: and reading the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware from the preset data information database, and predicting the service life of the target hardware according to the target hardware data information and the historical hardware data information based on the service life analysis frequency.
In this embodiment, the method further includes; and if the historical hardware data information of the historical hardware corresponding to the target hardware cannot be found in the preset data information database, predicting the service life of the target hardware according to the target hardware data information and the environmental information of the target hardware. The environmental information of the target hardware includes, but is not limited to, temperature information, so that comprehensive analysis and prediction of the service life of the target hardware are performed according to the information such as the data information of the target hardware and the temperature of the target hardware.
As can be seen, in this embodiment, the target hardware data information corresponding to the target hardware in the distributed storage cluster is collected according to a preset data acquisition period, and the target hardware data information is stored in a preset data information database, so that the preset data acquisition period is dynamically adjusted according to feedback of the target hardware data information, thereby avoiding unnecessary resource consumption during data acquisition; meanwhile, data cleaning is carried out according to the type of the historical data, and the problem that resources are wasted due to the fact that the storage space occupied by the historical data is too large is solved.
Referring to fig. 4, an embodiment of the present application discloses a hardware management apparatus for a distributed storage cluster, including:
the hardware information acquisition module 11 is configured to acquire target hardware data information corresponding to target hardware in the distributed storage cluster;
a life reference value determining module 12, configured to determine, according to the target hardware data information, a parameter that affects the life of the target hardware, so as to obtain a life reference value parameter of the target hardware;
a frequency determining module 13, configured to compare the life reference value parameter with a life standard value parameter of the target hardware to obtain a comparison result, and determine a life analysis frequency for the target hardware according to the comparison result;
and the service life prediction module 14 is configured to perform service life prediction on the target hardware according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware based on the service life analysis frequency.
It can be seen that, in this embodiment, when hardware management of a distributed storage cluster is performed, target hardware data information of target hardware in the distributed storage cluster is first obtained, a parameter related to the life of the target hardware is determined from the target hardware data information and is used as a life reference value parameter of the target hardware, then a life standard value parameter of the target hardware is compared with the life reference value parameter of the target hardware, a life analysis frequency for the target hardware is determined according to a comparison result, and finally, life prediction is performed on the target hardware according to the target hardware data information and historical hardware data information of historical hardware based on the life analysis frequency. Therefore, when the hardware management of the distributed storage cluster is carried out, the target hardware data information of the target hardware is obtained, the service life reference value parameter is determined from the target hardware data information, and the service life analysis frequency is determined according to the comparison result of the service life reference value parameter of the target hardware and the service life standard value parameter, so that the service life analysis frequency of the target hardware can be flexibly adjusted during the hardware management, the consumption of a large amount of system resources caused by frequent service life analysis is avoided, the analysis of the target hardware adopts the appropriate frequency corresponding to the frequency to timely determine the fault probability and the expected failure time of the target hardware, and a user can conveniently judge the service life of the target hardware and prepare for replacement in advance on the premise of fully using the hardware. In conclusion, the method and the device can effectively manage the hardware in the distributed storage cluster and predict the failure of the hardware in use.
In some specific embodiments, the hardware management apparatus of the distributed storage cluster further includes:
and the hardware adaptation module is used for acquiring the target hardware basic information of the target hardware through the hardware basic information acquisition command and judging whether the target hardware is adapted to other hardware in the distributed storage cluster or not according to the target hardware basic information.
In some specific embodiments, the hardware adaptation module specifically includes:
the fault checking unit is used for acquiring target hardware basic information of target hardware through a hardware basic information acquisition command and judging whether the target hardware has a fault according to the target hardware basic information;
and the compatibility judging unit is used for processing the basic information of the target hardware into basic information of a target format if the target hardware has no fault, and comparing the basic information of the target format with data in a preset hardware compatible database to judge whether the target hardware is matched with other hardware in the distributed storage cluster.
In some specific embodiments, the hardware management apparatus of the distributed storage cluster further includes:
and the view adjusting module is used for adjusting the hardware adaptation interface view corresponding to the distributed storage cluster so as to ensure that the hardware data information corresponding to various types of hardware in the distributed storage cluster is normally acquired.
In some specific embodiments, the hardware information obtaining module 11 is specifically configured to: acquiring the target hardware data information corresponding to the target hardware in the distributed storage cluster according to a preset data acquisition period, and storing the target hardware data information into a preset data information database;
accordingly, the life prediction module 14 is specifically configured to: and reading the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware from the preset data information database, and predicting the service life of the target hardware according to the target hardware data information and the historical hardware data information based on the service life analysis frequency.
In some specific embodiments, the hardware management apparatus of the distributed storage cluster further includes:
the period adjusting module is used for adjusting the preset data acquisition period according to the change frequency of the target hardware data information in the preset data information database;
the period prolonging module is used for correspondingly prolonging the preset data acquisition period according to the change frequency if the change frequency of the target hardware data information is lower than a preset change threshold;
and the period shortening module is used for correspondingly shortening the preset data acquisition period according to the change frequency if the change frequency of the target hardware data information is higher than a preset change threshold value.
In some embodiments, the lifetime prediction module 14 is specifically configured to: and if the historical hardware data information of the historical hardware corresponding to the target hardware cannot be found in the preset data information database, predicting the service life of the target hardware according to the target hardware data information and the environmental information of the target hardware.
Fig. 5 shows an electronic device 20 according to an embodiment of the present application. The electronic device 20 may further include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. The memory 22 is configured to store a computer program, and the computer program is loaded and executed by the processor 21 to implement relevant steps in the hardware management method for a distributed storage cluster disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in the present embodiment may be specifically an electronic computer.
In this embodiment, the power supply 23 is used to provide voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and a communication protocol followed by the communication interface is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited herein; the input/output interface 25 is configured to acquire external input data or output data to the outside, and a specific interface type thereof may be selected according to specific application requirements, which is not specifically limited herein.
In addition, the memory 22 is used as a carrier for resource storage, and may be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., and the resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage manner may be a transient storage manner or a permanent storage manner.
The operating system 221 is used for managing and controlling each hardware device on the electronic device 20, and the computer program 222 may be Windows Server, Netware, Unix, Linux, or the like. The computer program 222 may further include a computer program that can be used to perform other specific tasks in addition to the computer program that can be used to perform the hardware management method of the distributed storage cluster executed by the electronic device 20 disclosed in any of the foregoing embodiments.
Further, the present application also discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the hardware management method of a distributed storage cluster disclosed in the foregoing. 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.
Finally, it should also be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The hardware management method, apparatus, device and medium of a distributed storage cluster provided by the present invention are introduced in detail, and a specific example is applied in the present document to explain the principle and the implementation of the present invention, and the description of the above embodiment is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, 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 invention.
Claims (10)
1. A hardware management method of a distributed storage cluster is characterized by comprising the following steps:
acquiring target hardware data information corresponding to target hardware in a distributed storage cluster;
determining parameters influencing the service life of the target hardware according to the target hardware data information to obtain service life reference value parameters of the target hardware;
comparing the life reference value parameter with a life standard value parameter of the target hardware to obtain a comparison result, and determining a life analysis frequency aiming at the target hardware according to the comparison result;
and predicting the service life of the target hardware according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware based on the service life analysis frequency.
2. The hardware management method for the distributed storage cluster according to claim 1, wherein before the obtaining of the target hardware data information corresponding to the target hardware in the distributed storage cluster, the method further comprises:
and acquiring target hardware basic information of target hardware through a hardware basic information acquisition command, and judging whether the target hardware is matched with other hardware in the distributed storage cluster or not according to the target hardware basic information.
3. The hardware management method of the distributed storage cluster according to claim 2, wherein the obtaining of the target hardware basic information of the target hardware through the hardware basic information obtaining command and the judging of whether the target hardware is adapted to other hardware in the distributed storage cluster according to the target hardware basic information include:
acquiring target hardware basic information of target hardware through a hardware basic information acquisition command, and judging whether the target hardware has a fault according to the target hardware basic information;
and if the target hardware has no fault, processing the basic information of the target hardware into basic information of a target format, and comparing the basic information of the target format with data in a preset hardware compatible database to judge whether the target hardware is matched with other hardware in the distributed storage cluster.
4. The hardware management method of the distributed storage cluster according to claim 2, wherein after acquiring the target hardware basic information of the target hardware through the hardware basic information acquisition command and determining whether the target hardware is adapted to other hardware in the distributed storage cluster according to the target hardware basic information, the method further comprises:
and adjusting the hardware adaptation interface view corresponding to the distributed storage cluster to ensure that the hardware data information corresponding to various types of hardware in the distributed storage cluster is normally acquired.
5. The hardware management method for the distributed storage cluster according to any one of claims 1 to 4, wherein the acquiring target hardware data information corresponding to target hardware in the distributed storage cluster includes:
acquiring the target hardware data information corresponding to the target hardware in the distributed storage cluster according to a preset data acquisition period, and storing the target hardware data information into a preset data information database;
correspondingly, the predicting the service life of the target hardware according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware based on the service life analysis frequency includes:
and reading the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware from the preset data information database, and predicting the service life of the target hardware according to the target hardware data information and the historical hardware data information based on the service life analysis frequency.
6. The method for hardware management of a distributed storage cluster according to claim 5, further comprising:
adjusting the preset data acquisition period according to the change frequency of the target hardware data information in the preset data information database;
if the change frequency of the target hardware data information is lower than a preset change threshold, correspondingly prolonging the preset data acquisition period according to the change frequency;
and if the change frequency of the target hardware data information is higher than a preset change threshold, correspondingly shortening the preset data acquisition period according to the change frequency.
7. The hardware management method of the distributed storage cluster according to claim 5, wherein the predicting the lifetime of the target hardware according to the historical hardware data information of the target hardware corresponding to the target hardware based on the lifetime analysis frequency comprises:
and if the historical hardware data information of the historical hardware corresponding to the target hardware cannot be found in the preset data information database, predicting the service life of the target hardware according to the target hardware data information and the environmental information of the target hardware.
8. A hardware management apparatus for a distributed storage cluster, comprising:
the hardware information acquisition module is used for acquiring target hardware data information corresponding to target hardware in the distributed storage cluster;
the service life reference value determining module is used for determining parameters influencing the service life of the target hardware according to the target hardware data information so as to obtain service life reference value parameters of the target hardware;
the frequency determination module is used for comparing the life reference value parameter with a life standard value parameter of target hardware to obtain a comparison result, and determining the life analysis frequency aiming at the target hardware according to the comparison result;
and the service life prediction module is used for predicting the service life of the target hardware according to the target hardware data information and historical hardware data information of historical hardware corresponding to the target hardware based on the service life analysis frequency.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing said computer program for implementing the steps of the hardware management method of a distributed storage cluster according to any of claims 1 to 7.
10. A computer-readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the steps of a method of hardware management of a distributed storage cluster according to any of claims 1 to 7.
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| CN202210868319.0A CN115129260A (en) | 2022-07-22 | 2022-07-22 | Hardware management method, device, equipment and medium for distributed storage cluster |
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