CN115499304B - Automatic deployment method, device, equipment and product for distributed storage - Google Patents
Automatic deployment method, device, equipment and product for distributed storage Download PDFInfo
- Publication number
- CN115499304B CN115499304B CN202210908295.7A CN202210908295A CN115499304B CN 115499304 B CN115499304 B CN 115499304B CN 202210908295 A CN202210908295 A CN 202210908295A CN 115499304 B CN115499304 B CN 115499304B
- Authority
- CN
- China
- Prior art keywords
- determining
- server
- cache
- capacity
- configuration information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000005192 partition Methods 0.000 claims description 57
- 238000004590 computer program Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000000638 solvent extraction Methods 0.000 claims description 7
- 238000012423 maintenance Methods 0.000 abstract description 7
- 238000013500 data storage Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 6
- 238000007726 management method Methods 0.000 description 6
- 230000010076 replication Effects 0.000 description 5
- 238000012512 characterization method Methods 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000002955 isolation Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0876—Aspects of the degree of configuration automation
- H04L41/0886—Fully automatic configuration
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0889—Techniques to speed-up the configuration process
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses an automatic deployment method, device, equipment and product of distributed storage, and relates to the field of distributed storage, wherein the method comprises the following steps: determining a deployment template corresponding to each server in the distributed storage cluster; the deployment template comprises data node configuration information of the server, cache disk configuration information and storage layer configuration information, wherein the data node configuration information is determined based on the combination of one or more parameters of the type, the hard disk capacity and the physical resources of the server, the cache disk configuration information is determined based on the number and the capacity of data disks of the server, and the storage layer configuration information is determined based on the use scene and the physical resources of the server; and based on the deployment template, deploying the corresponding server. The invention can save a great deal of manpower cost, greatly improve the deployment reliability and stability, reduce the manpower cost, avoid the problem of high misoperation rate of the manual deployment server, and improve the operation and maintenance efficiency and the accuracy.
Description
Technical Field
The invention relates to the field of distributed storage, in particular to an automatic deployment method, device, equipment and product of distributed storage.
Background
At present, the data storage scale of distributed storage also reaches over PB and EB levels, a single distributed storage cluster is built by thousands of servers, even tens of thousands of servers, data centers of different regions and complex network environments are involved in actual deployment, and different application scenes such as virtualization, high-performance calculation and the like are required to be supported, so how to deploy large-scale distributed storage is an important subject to be solved in the industry at present.
Disclosure of Invention
In view of the above, the embodiments of the present invention provide an automated deployment method, apparatus, device, and product for distributed storage, so as to solve the problem of how to deploy large-scale distributed storage.
According to a first aspect, an embodiment of the present invention provides an automated deployment method for distributed storage, the method including:
determining a deployment template corresponding to each server in the distributed storage cluster; the deployment template comprises data node configuration information of the server, cache disk configuration information and storage layer configuration information, wherein the data node configuration information is determined based on the combination of one or more parameters of the type, the hard disk capacity and the physical resources of the server, the cache disk configuration information is determined based on the number and the capacity of the data disks of the server, and the storage layer configuration information is determined based on the use scene and the physical resources of the server;
And based on the deployment template, deploying the corresponding server.
With reference to the first aspect, in a first implementation manner of the first aspect, the determining a deployment template corresponding to each server in the distributed storage cluster specifically includes:
determining resource information of each server in a distributed storage cluster, and determining the data node configuration information corresponding to each server based on the resource information; the resource information comprises one or more of the combination of parameters of the type of the server, the capacity of the hard disk and the physical resource;
determining the number and the capacity of the data disks of each server, and determining the cache disk configuration information corresponding to each server based on the number and the capacity of the data disks;
and determining the use scene and the physical resource of each server, and determining the storage layer configuration information corresponding to each server based on the use scene and the physical resource.
With reference to the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the determining resource information of each server in the distributed storage cluster, and determining the data node configuration information corresponding to each server based on the resource information specifically includes:
Determining the resource information of the server;
determining a configuration score and a configuration weight of each parameter in the resource information;
and determining the data node configuration information based on the configuration scores and the configuration weights of the parameters in the resource information.
With reference to the first embodiment of the first aspect, in a third implementation of the first aspect, the determining the number and the capacity of the data disks of each server, and determining the cache disk configuration information corresponding to each server based on the number and the capacity of the data disks specifically includes:
determining the number and capacity of data disks of the server and the number and capacity of cache disks;
determining the total capacity and the greatest common divisor of the capacities of all the data discs based on the number and the capacities of the data discs;
determining a cache benchmark based on the greatest common divisor and the total capacity of the data disk;
determining the total capacity of all the cache disks based on the number and the capacity of the cache disks, and determining the cache reference capacity based on the total capacity of the cache disks and the cache reference;
determining the cache capacity of each data disk based on the cache reference, the cache reference capacity and the capacity of each data disk, and determining the actual partition number of the cache disk based on the cache capacity;
And determining that the actual partition number exceeds the minimum cache partition number, and partitioning the corresponding cache disk based on the actual partition number and the cache reference capacity.
With reference to the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect, the method further includes the following steps:
determining that the actual partition number does not exceed the minimum cache partition number, subtracting a preset value from the cache reference capacity, and re-determining the cache capacity of each data disk, the cache partition number of each cache disk and the actual partition number based on the subtracted cache reference capacity until the actual partition number exceeds the minimum cache partition number;
and partitioning the corresponding cache disk based on the actual partition number and the subtracted cache reference capacity.
With reference to the first implementation manner of the first aspect, in a fifth implementation manner of the first aspect, the determining a usage scenario and a physical resource of each server, and determining, based on the usage scenario and the physical resource, the storage layer configuration information corresponding to each server specifically includes:
determining the use scene of a server, and determining the type and the number of storage pools based on the use scene;
Determining a placement group share of each storage pool based on the type and the number of the storage pools;
determining the number of the data disks of a server, and determining the number of the placed groups of each storage pool based on the number of the data disks, the placed group shares and the types of the storage pools;
based on the number of placed groups, a corresponding storage pool is created.
According to a second aspect, an embodiment of the present invention further provides an automated deployment apparatus for distributed storage, the apparatus comprising:
the template determining module is used for determining deployment templates corresponding to the servers in the distributed storage cluster; the deployment template comprises data node configuration information of the server, cache disk configuration information and storage layer configuration information, wherein the data node configuration information is determined based on the combination of one or more parameters of the type, the hard disk capacity and the physical resources of the server, the cache disk configuration information is determined based on the number and the capacity of the data disks of the server, and the storage layer configuration information is determined based on the use scene and the physical resources of the server;
and the automatic deployment module is used for deploying the corresponding servers based on the deployment template.
According to a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method for automatically deploying distributed storage according to any one of the above.
According to a fourth aspect, embodiments of the present invention also provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of an automated deployment method of distributed storage as described in any of the above.
According to a fifth aspect, embodiments of the present invention also provide a computer program product comprising a computer program which, when executed by a processor, implements the steps of an automated deployment method of distributed storage as described in any of the above.
According to the automatic deployment method, device, equipment and product for distributed storage, the deployment templates corresponding to the servers in the cluster are formed through the parameter information corresponding to the servers in the distributed storage cluster, corresponding deployment work of the corresponding servers is carried out through the deployment templates, and then the servers can be deployed again based on the deployment templates, so that batch and rapid replication and deployment of the servers in the cluster are realized, additional input configuration is not needed, the problem that a large number of related personnel such as storage and calculation are needed to be deployed in sequence when the servers are deployed integrally is solved, a large number of manpower cost can be saved, the deployment reliability and stability are greatly improved, the manpower cost is reduced, the problem of high misoperation rate of the manually deployed servers is avoided, and the operation and maintenance efficiency and the accuracy are improved.
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and should not be construed as limiting the invention in any way, in which:
FIG. 1 is a flow diagram of an automated deployment method for distributed storage provided by the present invention;
fig. 2 is a specific flow schematic diagram of step S10 in the method for automatically deploying distributed storage provided by the present invention;
fig. 3 is a specific flow schematic diagram of step S11 in the method for automatically deploying distributed storage provided by the present invention;
fig. 4 shows one specific flow diagram of step S12 in the method for automatically deploying distributed storage according to the present invention;
FIG. 5 is a second specific flowchart of step S12 in the method for automatically deploying distributed storage according to the present invention;
fig. 6 is a specific flow schematic diagram of step S13 in the method for automatically deploying distributed storage provided by the present invention;
FIG. 7 is a schematic diagram of an automated deployment apparatus for distributed storage provided by the present invention;
fig. 8 shows a schematic structural diagram of an electronic device provided by the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
The distributed storage rack at present forms a preferred scheme for cloud computing, and the file storage has the advantages of sharing, low price and the like. In the file storage system, data is organized into files for storage and access, for example, a cStor distributed cloud storage system can support protocols such as a network file system (Network File System, NFS), a universal network file system (Common Internet File System, CIFS) and the like, and meanwhile, the system supports multi-platform access, and has the advantages of being flexible and extensible, easy to operate for users and the like.
The distributed storage has realized large-scale application in the scenes of cloud computing, big data, artificial intelligence (Artificial Intelligence, AI) and the like, the data storage scale of the distributed storage also reaches PB and EB levels, a single distributed storage cluster is built by thousands or even tens of thousands of servers, the actual deployment involves data centers of different regions and complex network environments, and different application scenes such as virtualization, high-performance computing and the like need to be supported. In the prior art, a large amount of script parameter configuration needs to be carried out in the operation and maintenance deployment process of the distributed storage system, and the problems of low visual interface parameter configuration efficiency, high labor cost, high misoperation rate and the like exist.
Therefore, how to deploy, manage and monitor large-scale distributed storage is an important issue to be solved in the industry.
An automated deployment method of distributed storage of the present invention is described below in conjunction with FIG. 1, the method comprising the steps of:
s10, determining deployment templates corresponding to the servers in the distributed storage cluster. In the method, a deployment template comprises data node configuration information, cache disk configuration information and storage layer configuration information of a server.
The data node configuration information is a data node type of the server specifically divided, and the data node type includes but is not limited to: data storage nodes, service management nodes, service gateway nodes, etc., i.e. what node the server acts as in the cluster; the configuration information of the buffer disk is the buffer capacity required by the server and is used for meeting the data buffer requirement of the server; storage tier configuration information is a storage tier parameter of a server including, but not limited to: the number of storage pools that the server needs to create, the number of Placement Groups (PGs) corresponding to each storage pool, etc. are used to meet the data storage requirements of the service.
The data node configuration information is determined based on the combination of one or more parameters of the type of the server, the capacity of a hard disk and physical resources, wherein the capacity of the hard disk comprises the capacity of a system disk and/or a data disk, and the physical resources comprise hardware resource information such as CPU (central processing unit), memory, network, disk I/O (input/output) delay and the like; the cache disk configuration information is determined based on the number and capacity of the data disks of the server; the storage layer configuration information is determined based on the usage scenario of the server and the physical resources.
In this embodiment, the deployment template is dynamically configured according to the above parameter information of the servers, that is, the deployment template is a deployment template corresponding to each server generated according to different parameter information of each server in the distributed storage cluster. Because the deployment template contains various pieces of configuration information required by server deployment, when the server parameter information is unchanged, server redeployment can be performed based on the deployment template, batch and rapid replication and deployment of servers in the cluster are realized, additional input configuration is not needed, and if the server parameter information is changed, corresponding configuration information in the corresponding deployment template is adjusted.
S20, based on the deployment template, corresponding server deployment is carried out.
After the deployment templates corresponding to the servers in the distributed cluster are determined, the servers are deployed based on the deployment templates corresponding to the servers, and because the deployment templates contain data node configuration information, cache disk configuration information and storage layer configuration information, the deployment templates are different from a scheme of deploying the servers by configuring a large number of script parameter files for a single server, so that the operation and maintenance efficiency and the accuracy are improved, the servers can be deployed through the deployment templates, storage isolation domains and storage pools in the clusters can be automatically planned, and the clusters corresponding to the servers can be built according to use scenes by deploying the servers through the deployment templates.
In this embodiment, the deployment template is a csm-CLI file, so when the visual interface cannot be used for a part of environments, the automatic deployment of the server can still be performed based on the deployment template and the CLI command line arrangement tool. Specifically, the deployment template is analyzed through the CLI command line arrangement tool to obtain a deployment command, and the corresponding server is arranged based on the deployment command to complete automatic generation of the dependent file, so that an integrated deployment scheme is achieved.
In these embodiments, since the deployment template is a csm-cli file, the management and monitoring of large-scale distributed storage is also possible.
According to the automatic deployment method for the distributed storage, the deployment templates corresponding to the servers in the cluster are formed through the parameter information corresponding to the servers in the distributed storage cluster, corresponding deployment work of the corresponding servers is carried out through the deployment templates, and then the servers can be deployed again based on the deployment templates, so that batch and rapid replication and deployment of the servers in the cluster are realized, additional input configuration is not needed, the problem that a large number of related personnel are required to be deployed in sequence during integral deployment of the servers is solved, a large number of manpower cost is saved, the deployment reliability and stability are greatly improved, the manpower cost is reduced, the problem of high misoperation rate of the manually deployed servers is avoided, and the operation and maintenance efficiency and the accuracy are improved.
The following describes an automated deployment method of distributed storage according to the present invention with reference to fig. 2, and step S10 specifically includes:
s11, determining resource information of each server in the distributed storage cluster, and determining data node configuration information corresponding to each server based on the resource information.
And S11, selecting part of servers in the cluster as data storage nodes, sequentially selecting service gateway nodes from the rest of servers, and finally selecting service management nodes, wherein two or all kinds of nodes in the data storage nodes, the service management nodes and the service gateway nodes can be deployed at the same time by the same server, so that the optimal utilization of resources is achieved, the rationality of hardware use is ensured, and the use efficiency of the cluster under the same hardware resources is further improved.
And S12, determining the number and the capacity of the data disks of each server, and determining cache disk configuration information corresponding to each server based on the number and the capacity of the data disks.
Step S12 carries out automatic partition on the cache disk of each server, and carries out dynamic allocation according to the number and the capacity of the data disks in the partition process, so that the optimal allocation of the existing physical resources is realized, storage deployment interruption caused by partition calculation errors is avoided, and particularly, in complex implementation scenes such as large-scale deployment, heterogeneous server deployment and the like, the calculation time is saved, and the efficiency and the stability can be effectively improved.
S13, determining the use scene and the physical resources of each server, and determining the storage layer configuration information corresponding to each server based on the use scene and the physical resources.
Step S13 automatically creates storage pools according to the use scene of the server and the physical resources, the number of the storage pools and the PG number of the storage pools are automatically distributed, the planning and the working of deployment personnel are reduced, the storage pools are rapidly distributed, the professional threshold of the deployment personnel is reduced, the implementation cost is saved, and the implementation efficiency is improved under the large-scale cluster deployment scene.
The following describes an automated deployment method of distributed storage according to the present invention with reference to fig. 3, and step S11 specifically includes:
s111, determining resource information of the server.
S112, determining the configuration score and the configuration weight of each parameter in the resource information.
In step S112, based on one or more parameters selected from the resource information, for example, three parameters including a type of a server, a capacity of a hard disk, and a physical resource are selected as parameters used for determining configuration information of a data node and configuration weights of each parameter, then configuration scores of the selected parameters are determined, that is, scoring is performed based on specific values/quality degrees of the parameters, so as to obtain configuration scores, wherein the higher the values of the configuration scores are, the better the parameters are represented, and conversely, the lower the values of the configuration scores are, the worse the parameters are represented.
In this embodiment, the configuration score may be a specific score value or a score level.
S113, determining data node configuration information based on the configuration scores and the configuration weights of the parameters in the resource information.
And finally, multiplying the configuration weight of each parameter by the configuration score, and carrying out weighted calculation on the product of the configuration weight of each parameter and the configuration score to obtain a comprehensive score, so that the comprehensive score can also be used for scoring the characterization server, wherein the higher the score is, the better the comprehensive performance of the characterization server is, and the lower the score is, the worse the comprehensive performance of the characterization server is.
In this application, one or more nodes that a server may specifically configure are determined based on the aggregate scores of the servers.
In some possible embodiments, the configuration score of the hard disk capacity is obtained by scoring the hard disk capacity of the server, and when the system disk or the data disk capacity in the server does not meet the configuration score corresponding to the minimum requirement of the data storage node, the data storage node will not select the server. It should be noted that the minimum requirement user can configure itself.
In other possible embodiments, the type, the hard disk capacity and the physical resources of the server are respectively scored to obtain configuration scores corresponding to the parameters respectively, and the configuration weights and the configuration scores based on the parameters are weighted to obtain the comprehensive scores of the server; and then, not selecting a server with the comprehensive score lower than the first preset score as a data storage node, not selecting a server with the comprehensive score lower than the second preset score as a gateway service node, and not selecting a server with the comprehensive score lower than the third preset score as a service management node.
The following describes an automated deployment method of distributed storage according to the present invention with reference to fig. 4, and step S12 specifically includes:
s121, determining the number and capacity of data disks of a single server, and the number and capacity of cache disks.
S122, determining the total capacity of all data disks in a single server and the greatest common divisor of the capacities based on the number and the capacities of the data disks, wherein the greatest common divisor refers to the greatest common divisor among the capacities of the data disks.
For example, a certain server uses a mechanical Hard Disk (HDD) as a data Disk and uses a solid state Disk (Solid State Drive, SSD) as a buffer Disk, and then the number of data disks is the number of mechanical Hard disks and the number of buffer disks is the number of solid state disks.
S123, determining a cache benchmark based on the greatest common divisor and the total capacity of the data disk.
Where cache benchmark = total capacity of data disk/greatest common divisor.
S124, determining the total capacity of all the cache disks in a single server based on the number and the capacity of the cache disks, and determining the cache reference capacity based on the total capacity of the cache disks and the cache reference.
Where cache reference capacity = total capacity of the cache disk/cache reference. The buffer reference capacity and the buffer reference are used for buffer disk partition.
S125, determining the required cache capacity of each data disk based on the cache reference, the cache reference capacity and the capacity of each data disk, and determining the actual partition number of the cache disk based on the cache capacity, wherein the actual partition number is the total partition number calculated by all the cache disks.
Wherein, the buffer capacity of each data disk=buffer reference capacity×the capacity corresponding to the data disk/buffer reference; the actual partition number is obtained based on the cache capacity of the data disk, that is, the partitions that all the cache disks can divide are calculated based on the cache capacity.
S126, determining that the actual partition number exceeds the minimum partition number of the cache, and partitioning the corresponding cache disk based on the cache partition number and the cache reference capacity. The actual number of partitions, i.e. the total number of partitions theoretically required for all data disks.
For example, the number of data disks is 12, and the number of cache disks is 2, and then the minimum number of cache partitions is 12, which is understood to be the number of data disks.
Since in an actual partition, if there is a margin by dividing the capacity of the buffer disk by the buffer reference capacity, for example, the buffer reference capacity is 20G and the capacity of the buffer disk is 128G, then division of 128 by 20 will have a margin of 8, but this 8G is not available for partition, and combining the margins of multiple buffer disks can result in the buffer reference capacity, but in an actual partition, the capacity of each margin cannot be partitioned alone.
Thus, the automated deployment method of distributed storage of the present invention is described below in conjunction with FIG. 5, with step S12 of the method further comprising the steps of:
s127, determining that the actual partition number does not exceed the minimum partition number of the cache, subtracting a preset value (for example, the preset value is 1G) from the reference cache capacity, and re-determining the cache capacity of each data disk, the cache partition number and the actual partition number of each cache disk based on the subtracted reference cache capacity until the actual partition number exceeds the minimum partition number of the cache. Specifically, the buffer reference capacity subtracted with the preset value is taken as a new buffer reference capacity, when the new buffer reference capacity is greater than 0, the corresponding steps of step S125 and step S126 are re-executed to obtain the total partition number calculated by all the buffer disks at this time, if the actual partition number of all the buffer disks still exceeds the buffer minimum partition number, the preset value is continuously subtracted, and the corresponding steps of step S125 and step S126 are re-executed again until the actual partition number does not exceed the buffer minimum partition number, that is, until the buffer requirement of all the data disks is met.
In this embodiment, when the value of the buffer reference capacity is lower than the preset capacity value after at least one time of subtracting the preset value in step S127, the number and capacity of the data disks of the single server are counted again, and the number and capacity of the buffer disks are reduced, for example, by reducing the buffer requirement of the data disks, and each step in step S12 is executed again, so that automatic partition of the buffer disks can be performed afterwards.
S128, partitioning the corresponding cache disk based on the actual partition number and the subtracted cache reference capacity.
The following describes an automated deployment method of distributed storage according to the present invention with reference to fig. 6, and step S13 specifically includes:
s131, determining the use scene of the single server, and determining the type and the number of the storage pools based on the use scene. I.e., determine the specifications of the respective storage pools.
S132, determining the PG share needed by each storage pool in a single server based on the types and the quantity of the storage pools. For example, three storage pools A, B and C are required, and based on the type of storage pool, the proportion of PG shares for storage pool A, B, C may be determined to be 4:2:1.
s133, determining the number of data disks of the single server, and determining the PG number of each storage pool in the single server based on the number of data disks, the placement group share and the type of the storage pool.
Preferably, the total amount of PG is determined based on the number of data disks, the total amount of pg=a preset PG value (e.g., the PG value is 200) ×the number of data disks, then the number of PG shares of the storage pool is determined based on the total amount of PG, the number of PG shares of the storage pool=total amount of PG/total number of PG shares of all storage pools (e.g., 4+2+1=7, the total number of PG shares is 7)/the number of copies corresponding to the storage pool type (e.g., 3), and then a maximum value that is not more than the power of 2 of the number of PG shares of the storage pool is determined. Assuming that the number of PG shares of a storage pool is 30, the maximum value which is not more than the power of 30 to 2 is 16. Then, based on the power of 2 multiplied by the corresponding PG shares, the number of PGs in each storage pool is calculated, and if the ratio of the PG shares in the storage pool A, B, C is 4:2: the power of 1,2 is 16, then the number of PGs in the storage pools A, B and C in this case correspond to 64, 32, and 16, respectively.
The total number of PGs required is thus 64+32+16=112, multiplied by the number of copies is 112×3=336, 336 is no more than 200×3=600 and 336×2=672 is greater than 600, i.e. the total number of PGs required is less than and closest to the total number of PGs.
S134, creating a corresponding storage pool based on the number of PGs.
The automatic deployment device of the distributed storage provided by the invention is described below, and the automatic deployment device of the distributed storage described below and the automatic deployment method of the distributed storage described above can be correspondingly referred to each other.
An automated deployment apparatus of distributed storage of the present invention is described below in conjunction with FIG. 7, the apparatus comprising the steps of:
the template determining module 10 is configured to determine a deployment template corresponding to each server in the distributed storage cluster. In the device, the deployment template comprises data node configuration information, cache disk configuration information and storage layer configuration information of the server.
The data node configuration information is a data node type of the server specifically divided, and the data node type includes but is not limited to: data storage nodes, service management nodes, service gateway nodes, etc., i.e. what node the server acts as in the cluster; the configuration information of the buffer disk is the buffer capacity required by the server and is used for meeting the data buffer requirement of the server; storage tier configuration information is a storage tier parameter of a server including, but not limited to: the number of storage pools that the server needs to create, the number of PGs corresponding to each storage pool, etc. are used to meet the data storage requirements of the service.
The data node configuration information is determined based on the combination of one or more parameters of the type of the server, the capacity of a hard disk and physical resources, wherein the capacity of the hard disk comprises the capacity of a system disk and/or a data disk, and the physical resources comprise hardware resource information such as a CPU (central processing unit), a memory and the like; the cache disk configuration information is determined based on the number and capacity of the data disks of the server; the storage layer configuration information is determined based on the usage scenario of the server and the physical resources.
In this embodiment, the deployment template is dynamically configured according to the above parameter information of the servers, that is, the deployment template is a deployment template corresponding to each server generated according to different parameter information of each server in the distributed storage cluster. Because the deployment template contains various pieces of configuration information required by server deployment, when the server parameter information is unchanged, server redeployment can be performed based on the deployment template, batch and rapid replication and deployment of servers in the cluster are realized, additional input configuration is not needed, and if the server parameter information is changed, corresponding configuration information in the corresponding deployment template is adjusted.
The automatic deployment module 20 is configured to perform deployment of the corresponding server based on the deployment template.
After the deployment templates corresponding to the servers in the distributed cluster are determined, the servers are deployed based on the deployment templates corresponding to the servers, and because the deployment templates contain data node configuration information, cache disk configuration information and storage layer configuration information, the deployment templates are different from a scheme of deploying the servers by configuring a large number of script parameter files for a single server, so that the operation and maintenance efficiency and the accuracy are improved, the servers can be deployed through the deployment templates, storage isolation domains and storage pools in the clusters can be automatically planned, and the clusters corresponding to the servers can be built according to use scenes by deploying the servers through the deployment templates.
In this embodiment, the deployment template is a csm-CLI file, so when the visual interface cannot be used for a part of environments, the automatic deployment of the server can still be performed based on the deployment template and the CLI command line arrangement tool. Specifically, the deployment template is analyzed through the CLI command line arrangement tool to obtain a deployment command, and the corresponding server is arranged based on the deployment command to complete automatic generation of the dependent file, so that an integrated deployment scheme is achieved.
According to the automatic deployment device for the distributed storage, the deployment templates corresponding to the servers in the cluster are formed through the parameter information corresponding to the servers in the distributed storage cluster, corresponding deployment work of the corresponding servers is carried out through the deployment templates, and then the servers can be deployed again based on the deployment templates, so that batch and rapid replication and deployment of the servers in the cluster are realized, additional input configuration is not needed, the problem that a large number of related personnel are required to be deployed in sequence during integral deployment of the servers is solved, a large number of manpower cost is saved, the deployment reliability and stability are greatly improved, the manpower cost is reduced, the problem of high misoperation rate of the manually deployed servers is avoided, and the operation and maintenance efficiency and the accuracy are improved.
Fig. 8 illustrates a physical structure diagram of an electronic device, as shown in fig. 8, which may include: processor 810, communication interface (Communications Interface) 820, memory 830, and communication bus 840, wherein processor 810, communication interface 820, memory 830 accomplish communication with each other through communication bus 840. The processor 810 may invoke logic instructions in the memory 830 to perform an automated deployment method of distributed storage, the method comprising:
Determining a deployment template corresponding to each server in the distributed storage cluster; the deployment template comprises data node configuration information of the server, cache disk configuration information and storage layer configuration information, wherein the data node configuration information is determined based on the combination of one or more parameters of the type, the hard disk capacity and the physical resources of the server, the cache disk configuration information is determined based on the number and the capacity of the data disks of the server, and the storage layer configuration information is determined based on the use scene and the physical resources of the server;
and based on the deployment template, deploying the corresponding server.
Further, the logic instructions in the memory 830 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of performing the method of automated deployment of distributed storage provided by the methods described above, the method comprising:
determining a deployment template corresponding to each server in the distributed storage cluster; the deployment template comprises data node configuration information of the server, cache disk configuration information and storage layer configuration information, wherein the data node configuration information is determined based on the combination of one or more parameters of the type, the hard disk capacity and the physical resources of the server, the cache disk configuration information is determined based on the number and the capacity of the data disks of the server, and the storage layer configuration information is determined based on the use scene and the physical resources of the server;
and based on the deployment template, deploying the corresponding server.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements an automated deployment method for distributed storage provided by the methods described above, the method comprising:
Determining a deployment template corresponding to each server in the distributed storage cluster; the deployment template comprises data node configuration information of the server, cache disk configuration information and storage layer configuration information, wherein the data node configuration information is determined based on the combination of one or more parameters of the type, the hard disk capacity and the physical resources of the server, the cache disk configuration information is determined based on the number and the capacity of the data disks of the server, and the storage layer configuration information is determined based on the use scene and the physical resources of the server;
and based on the deployment template, deploying the corresponding server.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. An automated deployment method for distributed storage, the method comprising:
determining a deployment template corresponding to each server in the distributed storage cluster; the deployment template comprises data node configuration information of the server, cache disk configuration information and storage layer configuration information, wherein the data node configuration information is determined based on the combination of one or more parameters of the type, the hard disk capacity and the physical resources of the server, the cache disk configuration information is determined based on the number and the capacity of the data disks of the server, and the storage layer configuration information is determined based on the use scene and the physical resources of the server;
based on the deployment template, deploying the corresponding server;
the determining a deployment template corresponding to each server in the distributed storage cluster specifically comprises the following steps:
determining resource information of each server in a distributed storage cluster, and determining the data node configuration information corresponding to each server based on the resource information; the resource information comprises one or more of the combination of parameters of the type of the server, the capacity of the hard disk and the physical resource;
Determining the number and the capacity of the data disks of each server, and determining the cache disk configuration information corresponding to each server based on the number and the capacity of the data disks;
determining the use scene and physical resources of each server, and determining the storage layer configuration information corresponding to each server based on the use scene and the physical resources;
the determining the resource information of each server in the distributed storage cluster, and determining the data node configuration information corresponding to each server based on the resource information specifically includes:
determining the resource information of the server;
determining a configuration score and a configuration weight of each parameter in the resource information;
determining the data node configuration information based on the configuration scores and the configuration weights of the parameters in the resource information;
the method further comprises the steps of: multiplying the configuration weight of each parameter by the configuration score, and carrying out weighted calculation on the product of the configuration weight of each parameter multiplied by the configuration score to obtain a comprehensive score; determining one or more nodes of the server specific configuration according to the comprehensive score of the server;
The determining the number and the capacity of the data discs of each server, and determining the configuration information of the cache disc corresponding to each server based on the number and the capacity of the data discs specifically includes:
determining the number and capacity of data disks of the server and the number and capacity of cache disks;
determining the total capacity and the greatest common divisor of the capacities of all the data discs based on the number and the capacities of the data discs;
determining a cache benchmark based on the greatest common divisor and the total capacity of the data disk;
determining the total capacity of all the cache disks based on the number and the capacity of the cache disks, and determining the cache reference capacity based on the total capacity of the cache disks and the cache reference;
determining the cache capacity of each data disk based on the cache reference, the cache reference capacity and the capacity of each data disk, and determining the actual partition number of the cache disk based on the cache capacity;
determining that the actual partition number exceeds the minimum cache partition number, and partitioning the corresponding cache disk based on the actual partition number and the cache reference capacity;
the determining the usage scenario and the physical resources of each server, and determining the storage layer configuration information corresponding to each server based on the usage scenario and the physical resources specifically includes:
Determining the use scene of a server, and determining the type and the number of storage pools based on the use scene;
determining a placement group share of each storage pool based on the type and the number of the storage pools;
determining the number of the data disks of a server, and determining the number of the placed groups of each storage pool based on the number of the data disks, the placed group shares and the types of the storage pools;
based on the number of placed groups, a corresponding storage pool is created.
2. The automated deployment method of distributed storage of claim 1, further comprising the steps of:
determining that the actual partition number does not exceed the minimum cache partition number, subtracting a preset value from the cache reference capacity, and re-determining the cache capacity of each data disk, the cache partition number of each cache disk and the actual partition number based on the subtracted cache reference capacity until the actual partition number exceeds the minimum cache partition number;
and partitioning the corresponding cache disk based on the actual partition number and the subtracted cache reference capacity.
3. An automated deployment apparatus for distributed storage, the apparatus comprising:
The template determining module is used for determining deployment templates corresponding to the servers in the distributed storage cluster; the deployment template comprises data node configuration information of the server, cache disk configuration information and storage layer configuration information, wherein the data node configuration information is determined based on the combination of one or more parameters of the type, the hard disk capacity and the physical resources of the server, the cache disk configuration information is determined based on the number and the capacity of the data disks of the server, and the storage layer configuration information is determined based on the use scene and the physical resources of the server;
the automatic deployment module is used for deploying the corresponding servers based on the deployment template;
the template determining module is specifically configured to:
determining resource information of each server in a distributed storage cluster, and determining the data node configuration information corresponding to each server based on the resource information; the resource information comprises one or more of the combination of parameters of the type of the server, the capacity of the hard disk and the physical resource;
determining the number and the capacity of the data discs of each server, and determining the cache disc configuration information corresponding to each server based on the number and the capacity of the data discs, wherein the method specifically comprises the following steps: determining the number and capacity of data disks of the server and the number and capacity of cache disks; determining the total capacity and the greatest common divisor of the capacities of all the data discs based on the number and the capacities of the data discs; determining a cache benchmark based on the greatest common divisor and the total capacity of the data disk; determining the total capacity of all the cache disks based on the number and the capacity of the cache disks, and determining the cache reference capacity based on the total capacity of the cache disks and the cache reference; determining the cache capacity of each data disk based on the cache reference, the cache reference capacity and the capacity of each data disk, and determining the actual partition number of the cache disk based on the cache capacity; determining that the actual partition number exceeds the minimum cache partition number, and partitioning the corresponding cache disk based on the actual partition number and the cache reference capacity;
Determining the use scene and the physical resource of each server, and determining the storage layer configuration information corresponding to each server based on the use scene and the physical resource, wherein the method specifically comprises the following steps: determining the use scene of a server, and determining the type and the number of storage pools based on the use scene; determining a placement group share of each storage pool based on the type and the number of the storage pools; determining the number of the data disks of a server, and determining the number of the placed groups of each storage pool based on the number of the data disks, the placed group shares and the types of the storage pools; creating a corresponding storage pool based on the number of the placement groups;
the determining the resource information of each server in the distributed storage cluster, and determining the data node configuration information corresponding to each server based on the resource information specifically includes:
determining the resource information of the server;
determining a configuration score and a configuration weight of each parameter in the resource information;
determining the data node configuration information based on the configuration scores and the configuration weights of the parameters in the resource information;
The automatic deployment module is specifically used for multiplying the configuration weight of each parameter by the configuration score, and then carrying out weighted calculation on the product of the configuration weight of each parameter multiplied by the configuration score to obtain a comprehensive score; one or more nodes of the server specific configuration are determined based on the integrated scores of the servers.
4. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the distributed storage automated deployment method of any of claims 1 to 2 when the program is executed.
5. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the automated deployment method of distributed storage according to any of claims 1 to 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210908295.7A CN115499304B (en) | 2022-07-29 | 2022-07-29 | Automatic deployment method, device, equipment and product for distributed storage |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210908295.7A CN115499304B (en) | 2022-07-29 | 2022-07-29 | Automatic deployment method, device, equipment and product for distributed storage |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115499304A CN115499304A (en) | 2022-12-20 |
| CN115499304B true CN115499304B (en) | 2024-03-08 |
Family
ID=84466708
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210908295.7A Active CN115499304B (en) | 2022-07-29 | 2022-07-29 | Automatic deployment method, device, equipment and product for distributed storage |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115499304B (en) |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102841759A (en) * | 2012-05-10 | 2012-12-26 | 天津兆民云计算科技有限公司 | Memory system for ultra-large virtual machine cluster |
| WO2017206667A1 (en) * | 2016-06-03 | 2017-12-07 | 中兴通讯股份有限公司 | Method and device for distributively deploying hadoop cluster |
| CN110222013A (en) * | 2019-06-11 | 2019-09-10 | 深信服科技股份有限公司 | A kind of method, system, equipment and storage medium that cluster storage capacity determines |
| CN110784546A (en) * | 2019-10-31 | 2020-02-11 | 浙江大华技术股份有限公司 | Distributed cluster deployment method, server and storage device |
| CN110989923A (en) * | 2019-10-30 | 2020-04-10 | 烽火通信科技股份有限公司 | Deployment method and device of distributed storage system |
| CN111770130A (en) * | 2020-05-08 | 2020-10-13 | 贵阳信息技术研究院（中科院软件所贵阳分部） | Method for efficient collaborative multiplexing of software and hardware resources in block chain distributed networking |
| CN112822044A (en) * | 2020-12-30 | 2021-05-18 | 北京天融信网络安全技术有限公司 | Distributed cluster deployment method and device, electronic equipment and readable storage medium |
| CN113138776A (en) * | 2021-03-25 | 2021-07-20 | 杭州博联智能科技股份有限公司 | Template-based cluster automatic deployment method, device, equipment and medium |
| CN113448947A (en) * | 2021-07-09 | 2021-09-28 | 烽火通信科技股份有限公司 | Method and device for distributed deployment, operation and maintenance of mongo database |
| CN113515524A (en) * | 2021-07-29 | 2021-10-19 | 中国工商银行股份有限公司 | Automatic dynamic allocation method and device for distributed cache access layer nodes |
| CN113678100A (en) * | 2019-01-31 | 2021-11-19 | 伊姆西Ip控股有限责任公司 | Unified and automated installation, deployment, configuration and management of software defined storage assets |
| CN113795826A (en) * | 2019-06-27 | 2021-12-14 | 英特尔公司 | Automated resource management for distributed computing |
| CN113986139A (en) * | 2021-10-31 | 2022-01-28 | 济南浪潮数据技术有限公司 | Deployment method and device of hybrid storage cluster, computer and storage medium |
| CN114020214A (en) * | 2021-10-29 | 2022-02-08 | 济南浪潮数据技术有限公司 | Storage cluster capacity expansion method and device, electronic equipment and readable storage medium |
| CN114443332A (en) * | 2021-12-24 | 2022-05-06 | 苏州浪潮智能科技有限公司 | Storage pool detection method and device, electronic equipment and storage medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7761538B2 (en) * | 2006-08-30 | 2010-07-20 | Microsoft Corporation | Dynamically configuring, allocating and deploying computing systems |
| US8266254B2 (en) * | 2008-08-19 | 2012-09-11 | International Business Machines Corporation | Allocating resources in a distributed computing environment |
| US8627309B2 (en) * | 2010-02-25 | 2014-01-07 | Microsoft Corporation | Automated deployment and servicing of distributed applications |
| US11567680B2 (en) * | 2020-07-15 | 2023-01-31 | Dynavisor, Inc. | Method and system for dynamic storage scaling |
-
2022
- 2022-07-29 CN CN202210908295.7A patent/CN115499304B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102841759A (en) * | 2012-05-10 | 2012-12-26 | 天津兆民云计算科技有限公司 | Memory system for ultra-large virtual machine cluster |
| WO2017206667A1 (en) * | 2016-06-03 | 2017-12-07 | 中兴通讯股份有限公司 | Method and device for distributively deploying hadoop cluster |
| CN113678100A (en) * | 2019-01-31 | 2021-11-19 | 伊姆西Ip控股有限责任公司 | Unified and automated installation, deployment, configuration and management of software defined storage assets |
| CN110222013A (en) * | 2019-06-11 | 2019-09-10 | 深信服科技股份有限公司 | A kind of method, system, equipment and storage medium that cluster storage capacity determines |
| CN113795826A (en) * | 2019-06-27 | 2021-12-14 | 英特尔公司 | Automated resource management for distributed computing |
| CN110989923A (en) * | 2019-10-30 | 2020-04-10 | 烽火通信科技股份有限公司 | Deployment method and device of distributed storage system |
| CN110784546A (en) * | 2019-10-31 | 2020-02-11 | 浙江大华技术股份有限公司 | Distributed cluster deployment method, server and storage device |
| CN111770130A (en) * | 2020-05-08 | 2020-10-13 | 贵阳信息技术研究院（中科院软件所贵阳分部） | Method for efficient collaborative multiplexing of software and hardware resources in block chain distributed networking |
| CN112822044A (en) * | 2020-12-30 | 2021-05-18 | 北京天融信网络安全技术有限公司 | Distributed cluster deployment method and device, electronic equipment and readable storage medium |
| CN113138776A (en) * | 2021-03-25 | 2021-07-20 | 杭州博联智能科技股份有限公司 | Template-based cluster automatic deployment method, device, equipment and medium |
| CN113448947A (en) * | 2021-07-09 | 2021-09-28 | 烽火通信科技股份有限公司 | Method and device for distributed deployment, operation and maintenance of mongo database |
| CN113515524A (en) * | 2021-07-29 | 2021-10-19 | 中国工商银行股份有限公司 | Automatic dynamic allocation method and device for distributed cache access layer nodes |
| CN114020214A (en) * | 2021-10-29 | 2022-02-08 | 济南浪潮数据技术有限公司 | Storage cluster capacity expansion method and device, electronic equipment and readable storage medium |
| CN113986139A (en) * | 2021-10-31 | 2022-01-28 | 济南浪潮数据技术有限公司 | Deployment method and device of hybrid storage cluster, computer and storage medium |
| CN114443332A (en) * | 2021-12-24 | 2022-05-06 | 苏州浪潮智能科技有限公司 | Storage pool detection method and device, electronic equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| 一种分布式文件存储系统的探索与应用;陈正举;;中国市场(第12期);全文 * |
| 数据处理教学科研云的建设与应用;黄晓辉;余文涛;;实验科学与技术(第06期);全文 * |
| 超融合分布式存储架构在中移互联网资源池中的应用;孙文庆;郁文清;;移动通信(第07期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115499304A (en) | 2022-12-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11736561B2 (en) | Load balanced network file accesses | |
| EP3525096A1 (en) | Resource load balancing control method and cluster scheduler | |
| CN106133693B (en) | Moving method, device and the equipment of virtual machine | |
| US8959223B2 (en) | Automated high resiliency system pool | |
| US20170160965A1 (en) | Optimization of in-memory data grid placement | |
| US10289441B1 (en) | Intelligent scale-out federated restore | |
| CN113886089B (en) | Task processing method, device, system, equipment and medium | |
| CN108810115B (en) | Load balancing method and device suitable for distributed database and server | |
| US10795735B1 (en) | Method and apparatus for load balancing virtual data movers between nodes of a storage cluster | |
| EP4029197A1 (en) | Utilizing network analytics for service provisioning | |
| US20200244766A1 (en) | Systems and methods for semi-automatic workload domain deployments | |
| CN115499304B (en) | Automatic deployment method, device, equipment and product for distributed storage | |
| JP2011192049A (en) | Virtual machine system, automatic migration method, and automatic migration program | |
| EP2838023A2 (en) | Centralised information reporting in a large scale information processing system | |
| US20150178115A1 (en) | Optimal assignment of virtual machines and virtual disks using multiary tree | |
| CN110764876A (en) | Cloud host creation method, device, equipment and readable storage medium | |
| CN116302534A (en) | Method, device, equipment and medium for optimizing performance of server storage equipment | |
| CN111240577B (en) | MPP database-based data multi-fragment storage method and device | |
| US10298515B1 (en) | Methods, systems, and computer readable mediums for creating a tenant cloud | |
| US11042395B2 (en) | Systems and methods to manage workload domains with heterogeneous hardware specifications | |
| CN112612579A (en) | Virtual machine deployment method, storage medium, and computer device | |
| CN117149099B (en) | Calculation and storage split server system and control method | |
| US11546220B2 (en) | Systems and methods for optimizing the number of servers in a cluster | |
| US11099741B1 (en) | Parallel access volume I/O processing with intelligent alias selection across logical control units | |
| CN117851040A (en) | Resource integration method for realizing cloud platform computing nodes based on dynamic resource load |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |