CN110703986A - Cloud hard disk creation method, device, equipment and storage medium - Google Patents
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Abstract
The application provides a cloud hard disk creating method, a cloud hard disk creating device, cloud hard disk creating equipment and a storage medium. The cloud hard disk creation method comprises the following steps: acquiring creating information of a cloud hard disk; determining a plurality of storage pools that match the creation information; obtaining a plurality of items of usage data for each of the storage pools; determining a composite score for each of the storage pools from the plurality of usage data for each of the storage pools; and creating the cloud hard disk in the storage pool with the maximum comprehensive score according to the creation information. According to the cloud hard disk storage method and device, the information of all dimensions of the storage pools is collected, so that the comprehensive score of each storage pool is calculated, the real use condition of each storage pool can be truly and effectively reflected through the comprehensive score, the optimal storage pool is selected to be used for creating the cloud hard disk, and the resources of each storage pool can be maximally utilized.
Description
Technical Field
The application relates to the technical field of cloud, in particular to a cloud hard disk creating method, a cloud hard disk creating device, cloud hard disk creating equipment and a storage medium.
Background
The cloud computing market is rapidly increased year by year, the demands of customers on cloud computing are increasingly large and diversified, the requirements on stability, reliability and storage performance of storage products such as cloud hard disks are increasingly high, and the problem that how to ensure that the quality of the storage products is not lost on the premise that storage resources are efficiently utilized is a current concern.
However, the rules of the OpenStack platform currently specify an available area where a cloud hard disk is located, determine whether the remaining storage space of a storage pool is sufficient to create the cloud hard disk, and select a storage pool meeting the conditions by specifying characteristics of the cloud hard disk to create the cloud hard disk. However, the resource utilization condition of each storage pool cannot be completely evaluated by the above method, and only the storage pool is selected by simple judgment, so that it is difficult to effectively utilize the resources of the storage pools, which may cause uneven resource allocation, excessive resources in some storage pools and insufficient resources in another storage pool, and affect the stability, reliability and storage performance of the cloud hard disk.
Disclosure of Invention
An object of the embodiment of the present application is to provide a cloud disk creation method, apparatus, device, and storage medium, so as to ensure a technical effect that resources of each storage pool can be maximally utilized.
In a first aspect, a method for creating a cloud hard disk provided in an embodiment of the present application includes: acquiring creating information of a cloud hard disk; determining a plurality of storage pools that match the creation information; obtaining a plurality of items of usage data for each of the storage pools; determining a composite score for each of the storage pools from the plurality of usage data for each of the storage pools; and creating the cloud hard disk in the storage pool with the maximum comprehensive score according to the creation information.
In the implementation process, the information of each dimensionality of the storage pool is acquired, so that the comprehensive score of each storage pool is calculated, the real use condition of each storage pool can be really and effectively reflected through the comprehensive score, the optimal storage pool is selected to be used for creating the cloud hard disk, and the resources of each storage pool can be maximally utilized.
With reference to the first aspect, this example embodiment of this application provides a first possible implementation manner of the first aspect, where the determining a composite score for each storage pool according to the multiple items of usage data of each storage pool includes: determining a ranking of each of the plurality of usage data for each of the storage pools relative to corresponding items of data in storage pools other than the storage pool; obtaining a weight corresponding to each item of the usage data of each storage pool; determining a composite score for each of the storage pools based on the ranking and the weight.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present application provides a second possible implementation manner of the first aspect, where the composite score satisfies:
wherein s is used to characterize the composite score for each of the storage pools and the creation information; the n characterizes the number of the plurality of items of usage data, and the k characterizes the kth item of usage data of the plurality of items of usage data; said rkCharacterizing a ranking corresponding to the kth item of usage data; said wkCharacterizing the weight to which the k-th item of usage data corresponds.
With reference to the first possible implementation manner or the second possible implementation manner of the first aspect, an embodiment of the present application provides a third possible implementation manner of the first aspect, where the obtaining a weight corresponding to each of the usage data of each of the storage pools includes: determining a degree of change coefficient for said each usage data for each said storage pool; and determining the weight corresponding to each item of the use data according to the change degree coefficient.
In the implementation process, the corresponding weight is determined by obtaining the change degree coefficient of each item of the use data, so that the determined weight is more accurate.
With reference to the third possible implementation manner of the first aspect, this application example provides a fourth possible implementation manner of the first aspect, where the determining a coefficient of a degree of change of each of the usage data of each of the storage pools includes: determining a difference between a maximum value and a minimum value of usage data for each of the same items in the plurality of storage pools; determining an average of usage data for each identical item in the plurality of storage pools; and using the quotient of the difference value and the average value as the coefficient of the degree of change of each item of the usage data of each storage pool.
With reference to the fourth possible implementation manner of the first aspect, an embodiment of the present application provides a fifth possible implementation manner of the first aspect, where the coefficient of variation satisfies:
wherein, ckCharacterizing the coefficient of variation, said vkCharacterizing an aggregate belonging to the kth usage data in each of the storage pools; max (v)k) For determining the maximum value in the set; min (v)k) For determining a minimum value in said set; average (v)k) For determining the average of said set.
In the implementation process, the multi-dimensional storage pool data is included in the calculation, so that the influence of partial dominant data of the storage pool on the result can be effectively reduced, and all data can play a role; and also by
The data difference of each storage pool is converted into the relative ranking difference of the data size, so that the influence of the data difference on the result is reduced, and the calculation result can reflect the real situation; and passing through a preset weight value wkThe impact of various usage data in the storage pool on the results may be adjusted to suit different scenarios.
With reference to the third possible implementation manner of the first aspect, an embodiment of the present application provides a sixth possible implementation manner of the first aspect, where the determining, according to the coefficient of degree of change, a weight corresponding to each item of usage data includes: determining an importance level of said each item of usage data; and determining the weight corresponding to each item of the use data according to the degree of change coefficient corresponding to each item of the use data and the importance level.
With reference to any one implementation manner of the first aspect to the second possible implementation manner of the first aspect, an embodiment of the present application provides a seventh possible implementation manner of the first aspect, where the multiple items of usage data include first usage data used to characterize remaining storage space of the storage pool, second usage data used to characterize used storage space of the storage pool, third usage data used to characterize the number of cloud disks that have been created by the storage pool, fourth usage data used to characterize the number of snapshots that have been created by the storage pool, fifth usage data used to characterize the current number of read and write operations per second of the storage pool, and sixth usage data used to characterize throughput of the storage pool.
In a second aspect, an apparatus for creating a cloud hard disk provided in an embodiment of the present application includes: the first acquisition unit is used for acquiring the creation information of the cloud hard disk; a first processing unit for determining a plurality of storage pools matching the creation information; a second obtaining unit, configured to obtain multiple items of usage data of each of the storage pools; a second processing unit for determining a composite score for each of the storage pools based on the plurality of items of usage data for each of the storage pools; and the creating unit is used for creating the cloud hard disk in the storage pool with the maximum comprehensive score according to the creating information.
In combination with the second aspect, in a first possible implementation manner of the second aspect, the second processing unit is further configured to determine a ranking of each of the usage data in the multiple items of usage data of each of the storage pools relative to corresponding items of data in other storage pools except for the storage pool; obtaining a weight corresponding to each item of the usage data of each storage pool; determining a composite score for each of the storage pools based on the ranking and the weight.
In combination with the first possible implementation manner of the second aspect, the present embodiments provide a second possible implementation manner of the second aspect, and the composite score satisfies:
wherein s is used to characterize the composite score for each of the storage pools and the creation information; the n characterizes the number of the plurality of items of usage data, and the k characterizes the kth item of usage data of the plurality of items of usage data; said rkCharacterizing the kth termRanking corresponding to the usage data; said wkCharacterizing the weight to which the k-th item of usage data corresponds.
With reference to the first possible implementation manner or the second possible implementation manner of the second aspect, in an embodiment of the present application, providing a third possible implementation manner of the second aspect, where the obtaining a weight corresponding to each of the usage data of each of the storage pools includes: determining a degree of change coefficient for said each usage data for each said storage pool; and determining the weight corresponding to each item of the use data according to the change degree coefficient.
With reference to the third possible implementation manner of the second aspect, this example provides a fourth possible implementation manner of the second aspect, where the determining a coefficient of a degree of change of each usage data of each storage pool includes: determining a difference between a maximum value and a minimum value of usage data for each of the same items in the plurality of storage pools; determining an average of usage data for each identical item in the plurality of storage pools; and using the quotient of the difference value and the average value as the coefficient of the degree of change of each item of the usage data of each storage pool.
With reference to the fourth possible implementation manner of the second aspect, the present application provides a fifth possible implementation manner of the second aspect, and the coefficient of variation satisfies:
wherein, ckCharacterizing the coefficient of variation, said vkCharacterizing an aggregate belonging to the kth usage data in each of the storage pools; max (v)k) For determining the maximum value in the set; min (v)k) For determining a minimum value in said set; average (v)k) For determining the average of said set.
With reference to the third possible implementation manner of the second aspect, this embodiment provides a sixth possible implementation manner of the second aspect, where the determining, according to the coefficient of degree of change, a weight corresponding to each item of usage data includes: determining an importance level of said each item of usage data; and determining the weight corresponding to each item of the use data according to the degree of change coefficient corresponding to each item of the use data and the importance level.
In combination with any one of the second possible implementation manner of the third aspect to the second aspect, an embodiment of the present application provides a seventh possible implementation manner of the second aspect, where the plurality of items of usage data include first usage data for characterizing remaining storage space of the storage pool, second usage data for characterizing used storage space of the storage pool, third usage data for characterizing the number of cloud disks already created by the storage pool, fourth usage data for characterizing the number of snapshots already created by the storage pool, fifth usage data for characterizing the current number of read/write operations per second of the storage pool, and sixth usage data for characterizing throughput of the storage pool.
In a third aspect, an electronic device provided in an embodiment of the present application includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the cloud hard disk creation method according to any one of the first aspect when executing the computer program.
In a fourth aspect, a storage medium is provided in an embodiment of the present application, where the storage medium stores instructions, and when the instructions are run on a computer, the instructions cause the computer to execute the cloud hard disk creation method according to any one of the first aspect.
In a fifth aspect, a computer program product provided in an embodiment of the present application, when running on a computer, causes the computer to execute the cloud hard disk creation method according to any one of the first aspect.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part may be learned by the practice of the above-described techniques of the disclosure, or may be learned by practice of the disclosure.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of a cloud disk creation method provided in an embodiment of the present application;
fig. 2 is a timing diagram of a cloud disk creation method according to an embodiment of the present application;
fig. 3 is a flowchart of another cloud hard disk creation method provided in the embodiment of the present application;
fig. 4 is a schematic structural diagram of a cloud disk creation apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Referring to fig. 1, fig. 1 is a flowchart of a cloud hard disk creation method provided in this embodiment, it should be understood that the method may be executed by a cloud hard disk creation apparatus shown in fig. 4, where the apparatus corresponds to an electronic device shown in fig. 5, and the electronic device may be various devices capable of executing the method, such as a computer or a cloud server, and this embodiment of the present application is not limited thereto, and specifically includes the following steps:
step S101, obtaining the creation information of the cloud hard disk.
Optionally, the creation information includes a specification of a cloud hard disk to be created. Such as storage size and/or cloud hard disk type. The cloud hard disk type refers to a storage type of the cloud hard disk, and for example, the storage type may be direct connection storage or networked storage.
The cloud hard disk is used for providing multiple types of persistent storage space for the cloud host. Like a cloud hard disk on a traditional server, a user can perform operations such as partitioning a block storage space mounted to a cloud host, creating a file system, and the like.
As an embodiment, a user may input creation information of a cloud hard disk to be created through an operation interface provided by an electronic device (e.g., a server or a computer). For example, a user initiates a request for creating a cloud hard disk to a cloud platform (e.g., a cloud server) through HTTP (HyperText transfer Protocol), where the request carries creation information.
As another embodiment, the user may input the creation information through a third party, and the creation information is forwarded to the electronic device in the present application through a network. I.e. the creation information may be retrieved over the network.
It is to be understood that the above description is intended to be illustrative, and not restrictive.
Step S102, a plurality of storage pools matched with the creation information are determined.
Optionally, the cloud platform manages multiple storage pools. For example, there may be 10 or 20 etc. storage pools. Here, the number of the carbon atoms is not particularly limited.
Alternatively, a storage pool is an aggregate of a set of storage hardware on which a cloud hard disk may be created using distributed storage techniques.
Optionally, each storage pool includes a storage type, status information, and a plurality of items of usage data.
Optionally, the status information is used to describe the status of the storage pool.
Optionally, the plurality of items of usage data include first usage data (e.g., identified by the field Remain _ space) that characterizes remaining storage space of the storage pool, second usage data (e.g., identified by field Used _ space) characterizing Used storage space of the storage pool, third usage data (e.g., identified by field Disk num) characterizing an amount of cloud disks created in the storage pool, fourth usage data (which may be identified by field Snapshot _ num) for characterizing the number of snapshots that have been created for the pool, fifth usage data (which may be identified by field Input/Output Operations per second) for characterizing the current number of read and write Operations per second for the pool, and sixth usage data (which may be identified by field Perf _ throughput) for characterizing the throughput for the pool.
Wherein, the snapshot refers to a copy that is completely available through a specified cloud hard disk at a specified time point. The created snapshots are also stored in the storage pool, and the more snapshots created has a certain influence on the storage performance.
Of course, in actual use, the plurality of items of usage data include, but are not limited to, the above-mentioned six kinds of data. Here, the number of the carbon atoms is not particularly limited.
As one implementation, step S102 includes: a plurality of storage pools satisfying the creation information are found among all the storage pools. Namely, determining that the Remain _ space is larger than the storage space in the creation information and/or the storage pools with the same storage types as the cloud disk types in the creation information from all the storage pools.
That is, the remaining _ space of the storage pool is larger than the storage space in the creation information and/or the storage type of the storage pool is the same as the cloud disk type in the creation information, i.e., the storage pool is matched with the creation information.
Of course, in actual use, there may be only one determined storage pool, and when there is only one, the cloud hard disk may be created directly on the storage pool. Here, the number of the carbon atoms is not particularly limited.
As another embodiment, step S102 includes: and inquiring a plurality of storage pools of which the Remain _ space is larger than the storage space in the creating information and/or the storage type of the storage pool is the same as the cloud disk type in the creating information in the database according to the creating information.
Alternatively, the database may be a MySQL database.
Of course, in actual use, the database may also be mdbplus (mbd database) or oracle database. Here, the number of the carbon atoms is not particularly limited.
Optionally, the information of the storage pool stored in the database is updated in real time. For example: periodically acquiring various use data of various storage pools from each storage pool; and updating various use data of various historical storage pools in the database.
Alternatively, the timing time may be 1 hour or 30 minutes.
Optionally, the timing duration may be set according to a user requirement, and is not specifically limited herein.
Of course, in actual use, after the creation information is acquired, the database may be triggered to perform data update. Here, the number of the carbon atoms is not particularly limited.
Optionally, during updating, all the Perf _ IOPS and the Perf _ throughput in the preset time period in each storage pool may be averaged, so as to obtain a corresponding average value, and the average value is used as the latest value of the Perf _ IOPS and the Perf _ throughput in the storage pool at the current time.
Alternatively, the preset time period may be 24 hours or 12 hours.
Optionally, the setting of the preset time period may be set according to a user requirement, and is not specifically limited herein.
In other words, the update of the Used data of the remaining _ space, Used _ space, Disk _ num, and Snapshot _ num of each storage pool is to take the data acquired at the current time as the latest value, and the average value of the Perf _ IOPS and the Perf _ throughput is taken as the latest value by averaging.
In the implementation process, the information (such as multiple items of use data) of the storage pool stored in the database is updated at regular time, so that the storage pool for creating the cloud hard disk is the optimal storage pool, the resources of the storage pool can be effectively utilized, the resources of each storage pool can be maximally utilized, and the quality of the cloud hard disk cannot be influenced due to too concentrated distribution.
Step S103, acquiring a plurality of items of use data of each storage pool.
As an embodiment, step S103 includes: a plurality of usage data for each of the storage pools is obtained from a database. Wherein the database has stored therein a plurality of items of usage data for each storage pool.
Optionally, the database is built with a plurality of tables, each table storing data for one storage pool.
Of course, in actual use, the multiple usage data of the multiple storage pools may be stored in one table of the database.
For example, as shown in table one, assume that there are three storage pools, which are storage pool one, storage pool two, and storage pool three, respectively, and the multiple items of usage data for storage pool one, storage pool two, and storage pool three are as follows:
| properties | Storage pool 1 | Storage pool two | Storage pool three |
| Remain_space | 4323.34GB | 547.32GB | 2878.67GB |
| Used_space | 7852.66GB | 9242.45GB | 6242.21GB |
| Disk_num | 55 | 65 | 45 |
| Snapshot_num | 102 | 132 | 92 |
| Perf_IOPS | 252350 | 295230 | 204780 |
| Perf_throughput | 8525MB/s | 10565MB/s | 7547MB/s |
Watch 1
Step S104, determining a composite score for each of the storage pools according to the plurality of items of usage data for each of the storage pools.
Optionally, the high or low of the composite score is used to determine the matching degree of the storage pool for creating the cloud hard disk, for example, the higher the composite score is, the storage pool is indicated as the optimal storage pool for creating the cloud hard disk.
As an embodiment, step S104 includes: determining a ranking of each of the plurality of usage data for each of the storage pools relative to corresponding items of data in storage pools other than the storage pool; obtaining a weight corresponding to each item of the usage data of each storage pool; determining a composite score for each of the storage pools based on the ranking and the weight.
Continuing with the above example, the Remain _ space in pool one, pool two, and pool three are compared to obtain a ranking for the Remain _ space in each pool; comparing the Used _ space in the first storage pool, the second storage pool and the third storage pool to obtain the ranking of the Used _ space in each storage pool; comparing Disk _ num in the first storage pool, the second storage pool and the third storage pool to obtain the ranking of Disk _ num in each storage pool; comparing the Snapshot _ num in the first storage pool, the second storage pool and the third storage pool to obtain the ranking of the Snapshot _ num in each storage pool; comparing the Perf _ IOPS in the first storage pool, the second storage pool and the third storage pool to obtain the ranking of the Perf _ IOPS in each storage pool; comparing the Perf _ throughput in the first storage pool, the second storage pool and the third storage pool to obtain the ranking of the Perf _ throughput in each storage pool, and obtaining the results as shown in table two:
watch two
Optionally, the obtaining a weight corresponding to each item of usage data of each storage pool includes: acquiring an importance score and an importance level of each item of usage data; obtaining a change degree coefficient corresponding to each item of use data according to the importance score and the importance level; and determining the weight corresponding to each item of the use data according to the coefficient of the degree of change.
Alternatively, the importance score may be scored on a 10-point scale, i.e., the importance score for each item of usage data is 1-10 points.
Of course, in actual use, the importance score may be a percentage score, and is not particularly limited herein.
Alternatively, the importance score may be scored by the user, by reference to historical data, or a score based on relative importance.
For example, as shown in table three, each item uses the score of the data:
watch III
Optionally, the importance levels include four levels of very important, general, and not important.
Optionally, according to the 20/80 principle, the values of both the important and the unimportant are 20%, and the weight range corresponding to each level is defined as: very important (i.e., corresponding to 9-10 of the importance scores), important (i.e., corresponding to 6-8 of the importance scores), general (i.e., corresponding to 3-5 of the importance scores), and not important (i.e., corresponding to 1-2 of the importance scores). Therefore, from the importance scores, the importance rating of each item of usage data can be determined, as shown in table four:
| properties | Importance rating |
| Remain_space | In general |
| Used_space | In general |
| Disk_num | In general |
| Snapshot_num | In general |
| Perf_IOPS | Of importance |
| Perf_throughput | Of importance |
Watch four
Optionally, the obtaining a weight corresponding to each item of usage data of each storage pool includes: determining a degree of change coefficient for said each usage data for each said storage pool; and determining the weight corresponding to each item of the use data according to the change degree coefficient.
In the implementation process, the corresponding weight is determined by obtaining the change degree coefficient of each item of the use data, so that the determined weight is more accurate.
Optionally, the determining a coefficient of degree of change of said each usage data for each said storage pool comprises: determining a difference between a maximum value and a minimum value of usage data for each of the same items in the plurality of storage pools; determining an average of usage data for each identical item in the plurality of storage pools; and using the quotient of the difference value and the average value as the coefficient of the degree of change of each item of the usage data of each storage pool.
Optionally, the coefficient of degree of change satisfies:
wherein, ckCharacterizing the coefficient of variation, said vkCharacterizing an aggregate belonging to the kth usage data in each of the storage pools; max (v)k) For determining the maximum value in the set; min (v)k) For determining a minimum value in said set; average (v)k) For determining the average of said set.
Continuing with the above example, the maximum value and the minimum value of the remaining _ space in pool one, pool two, and pool three are subtracted, i.e., max (v)k)-min(vk) 4323.34-547.32 (GB) 3776.02(GB), and average (v)k) The coefficient of variation C1 ═ 3776.02/2583.11 ═ 1.46 for (4323.34+547.32+2878.67)/3 ═ 2583.11(GB), where 1.46 is the result obtained by keeping two decimal points. In order to avoid repeated description, according to the above calculation method, the first storage pool, the second storage pool and the storage pool are respectively storedAnd calculating Used _ space, Disk _ num, Snapshot _ num, Perf _ IOPS and Perf _ throughput in the pool III to obtain a result shown in a table V:
| properties | Coefficient of variation |
| Remain_space | 1.46 |
| Used_space | 0.39 |
| Disk_num | 0.36 |
| Snapshot_num | 0.37 |
| Perf_IOPS | 0.36 |
| Perf_throughput | 0.34 |
Watch five
Optionally, determining a weight corresponding to each item of usage data according to the coefficient of degree of change includes: when the importance level is very important, if the coefficient of the degree of change corresponding to a certain item of usage data is greater than a first preset threshold, the weight corresponding to the item of usage data is determined to be 10, and if the coefficient of the degree of change is less than or equal to the first preset threshold and is greater than or equal to 0, the weight corresponding to the item of usage data is determined to be 9;
when the importance level is important, if the coefficient of the degree of change corresponding to a certain item of usage data is greater than a second preset threshold value and is less than or equal to 1, determining the weight corresponding to the item of usage data as 8; if the change degree coefficient is smaller than or equal to a second preset threshold and larger than a third preset threshold, determining the weight corresponding to the use data to be 7; if the coefficient of the degree of change is less than or equal to a third preset threshold and is greater than or equal to 0, determining the weight corresponding to the item of usage data to be 6; if the coefficient of the degree of change is greater than 1, determining the weight corresponding to the item of use data as 9;
when the importance level is general, if the coefficient of the degree of change corresponding to a certain item of usage data is greater than a second preset threshold value and is less than or equal to 1, determining the weight corresponding to the item of usage data to be 5; if the change degree coefficient is smaller than or equal to a second preset threshold and larger than a third preset threshold, determining the weight corresponding to the use data to be 4; if the coefficient of the degree of change is less than or equal to a third preset threshold and is greater than or equal to 0, determining the weight corresponding to the item of usage data to be 3; if the coefficient of the degree of change is greater than 1, determining the weight corresponding to the item of use data as 6;
when the importance level is unimportant, if the coefficient of the degree of change corresponding to a certain item of usage data is greater than a first preset threshold and is less than or equal to 1, determining the weight corresponding to the item of usage data as 2, and if the coefficient of the degree of change is less than or equal to the first preset threshold and is greater than or equal to 0, determining the weight corresponding to the item of usage data as 1; if the coefficient of the degree of change is greater than 1, the weight corresponding to the item of usage data is determined to be 3.
Optionally, the first preset threshold is 0.5.
Optionally, the second preset threshold is 0.8.
Optionally, the third preset threshold is 0.4.
Optionally, the setting of the first preset threshold, the second preset threshold, and the third preset threshold may be set according to a user requirement, and is not specifically limited herein.
For example, if the coefficient of the degree of change corresponding to the remaining _ space is 1.46 and the importance level is very important, the weight corresponding to the remaining _ space is 10. On the contrary, if the variation coefficient corresponding to the remaining _ space is 0.46 and the importance level is very important, the weight corresponding to the remaining _ space is 9.
Note that, the weight value in the present application is calculated in the tenth system. Of course, if converted to a percent system, the weight is 100. Here, the number of the carbon atoms is not particularly limited.
For example, combining table four and table five, the weight of each usage data in storage pool one, storage pool two, and storage pool three may be obtained according to the above calculation manner, specifically, as shown in table six:
| properties | Preset weight |
| Remain_space | 6 |
| Used_space | 3 |
| Disk_num | 3 |
| Snapshot_num | 3 |
| Perf_IOPS | 6 |
| Perf_throughput | 6 |
Watch six
As can be seen from the above table, in pool one, pool two, and pool three, the weight of remaining _ space is 6, the weight of Used _ space is 3, the weight of Disk _ num is 3, the weight of Snapshot _ num is 3, the weight of Perf _ IOPS is 6, and the weight of Perf _ throughput is 6.
Optionally, the determining, according to the coefficient of degree of change, a weight corresponding to each item of usage data includes: determining an importance level of said each item of usage data; and determining the weight corresponding to each item of the use data according to the degree of change coefficient corresponding to each item of the use data and the importance level.
Optionally, said determining a composite score for each of said storage pools according to said ranking and said weight comprises: multiplying the reciprocal of the ranking corresponding to each item of usage data by the weight corresponding to each item of usage data to obtain a plurality of products; and accumulating the products to obtain a comprehensive score of each storage pool.
Optionally, the composite score satisfies:
wherein s is used to characterize the composite score for each of the storage pools and the creation information; the n characterizes the number of the plurality of items of usage data, and the k characterizes the kth item of usage data of the plurality of items of usage data; said rkCharacterizing a ranking corresponding to the kth item of usage data; said wkCharacterizing the weight to which the k-th item of usage data corresponds. For example, since there are 6 items of usage data in the first storage pool, n is 6, the value of k is 1 to 6, and according to the sequence from top to bottom in the table, it can be seen that when k is 1, the corresponding 1 st item of usage data is remaining _ space; when k is 2, the corresponding item 2 use data is Used _ space; when k is 3, the corresponding item 3 usage data is Disk _ num; when k is 4The corresponding item 4 usage data is Snapshot _ num; when k is 5, the corresponding 5 th item of usage data is Perf _ IOPS; when k is 6, the corresponding item 6 usage data is Perf _ throughput.
It should be noted that, in the present application, the arrangement order of the usage data in each storage pool is the same. For example, if k is 1 in the first storage pool, the corresponding 1 st item of usage data is remaining _ space, and if k is 1 in the second storage pool and the third storage pool, the corresponding 1 st item of usage data is still remaining _ space. It should be understood that the same is true for other data in the first storage pool, the second storage pool and the third storage pool, and the description is omitted here.
In the implementation process, the multi-dimensional storage pool data is included in the calculation, so that the influence of partial dominant data of the storage pool on the result can be effectively reduced, and all data can play a role; and also by
The data difference of each storage pool is converted into the relative ranking difference of the data size, so that the influence of the data difference on the result is reduced, and the calculation result can reflect the real situation; and passing through a preset weight value wkThe impact of various usage data in the storage pool on the results may be adjusted to suit different scenarios.
For example, combining tables one through six, the combined score S1 +6 × 1/1+3 × 1/2+3 × 1/2+3 × 1/2+6 × 1/2+6 × 1/2 of storage pool one is 16.5; the total score S2 ═ 6 × 1/3+3 × 1/3+3 × 1/3+3 × 1/3+6 × 1/1+6 × 1/1 ═ 17 in the storage pool two; the total score S3 ═ 6 × 1/2+3 × 1/1+3 × 1/1+3 × 1/1+6 × 1/3+6 × 1/3 ═ 16 for storage pool three.
And step S105, creating the cloud hard disk in the storage pool with the maximum comprehensive score according to the creation information.
As an embodiment, step S105 includes: comparing all the comprehensive scores to obtain the comprehensive score with the maximum result; and creating the cloud hard disk in the storage pool corresponding to the comprehensive score with the maximum result according to the creating information.
Continuing with the above example, it can be seen that the result of the highest score among the first storage pool, the second storage pool, and the third storage pool is 17, which corresponds to the second storage pool, so that a cloud disk matching the creation information is created on the second storage pool. For example, if the creation information is to create a 40GB cloud disk, a 40GB cloud disk is created on the storage pool two.
In a possible embodiment, the method further comprises: and returning a creation result.
Optionally, the creation result includes description information for describing whether the cloud hard disk was successfully created, that is, the description information includes a successful creation of the cloud hard disk or a failed creation of the cloud hard disk.
Optionally, the creation result further includes detailed description information for describing a reason for giving unsuccessful creation and a suggestion when the cloud hard disk is not created.
The cloud hard disk creation method in the present application is described above with reference to fig. 1, and the following describes, by way of example and not limitation, the cloud hard disk creation method in the present application in detail with reference to fig. 2. The method shown in fig. 2 comprises:
s201, acquiring the storage pool use data from the storage pool every 5 minutes.
Optionally, the data collection service obtains storage pool usage data from the storage pool every 5 minutes of timing.
And S202, returning corresponding data according to the HTTP request of the data acquisition service.
Optionally, each storage pool returns corresponding data according to an HTTP request of the data collection service. Namely, the data acquisition service sends an HTTP request to each storage pool every 5 minutes, so that each storage pool returns corresponding data according to the HTTP request of the data acquisition service.
And S203, transmitting the returned data to the data processing service through the HTTP.
Optionally, the data collection service transmits the returned data to the data processing service via the HTTP protocol.
And S204, receiving data transmitted by the data acquisition service.
Optionally, the data processing service receives data transmitted by the data acquisition service.
And S205, processing the data and writing the data into the data storage device.
Optionally, the data processing service processes the data and writes the data into the data storage device after receiving the data transmitted by the data acquisition service.
S206, storing the data transmitted by the data processing service.
Optionally, the data storage device stores the data transmitted by the data processing service.
Alternatively, the data storage device may be a MySQL database.
S207, the user initiates an HTTP request for creating the cloud hard disk.
Alternatively, the user may initiate an HTTP request to create a cloud hard disk through an operation interface of the cloud platform, for example, initiate the HTTP request through a keyboard or a mouse.
And S208, after receiving the request of the user, the cloud platform transmits the created cloud hard disk information to the resource scheduling service through the HTTP.
And S209, screening and taking out data from the data storage device according to the created cloud hard disk information.
Optionally, the resource scheduling service screens data from the data storage device according to the created cloud hard disk information and extracts a storage pool matched with the created cloud hard disk information.
And S210, returning data according to the condition.
Optionally, the data storage device returns various items of usage data of the storage pool matched with the created cloud hard disk information.
And S211, calculating the data by adopting a weighted calculation formula to obtain the score of the storage pool.
Optionally, the resource scheduling service calculates the usage data using a weighted calculation formula to obtain the storage pool score.
And S212, returning the storage pool information with the highest score to the cloud platform.
Optionally, the resource scheduling service returns the storage pool information with the highest score to the cloud platform.
And S213, creating the cloud hard disk on the storage pool according to the returned storage pool information.
Optionally, the cloud platform creates a cloud hard disk on the storage pool according to the returned storage pool information.
And S214, returning the result to the user after the creation is completed.
Optionally, the cloud platform returns the result to the user after the creation is completed.
S215, the user obtains a cloud hard disk creating result.
It should be noted that the resource scheduling service, the data processing service, and the data acquisition service in the present application may be understood as a virtual module/unit, i.e. a software module/unit, in the electronic device.
Of course, in actual use, the resource scheduling service, the data processing service, and the data collecting service may also be deployed on different servers respectively. For example, the resource scheduling service is deployed on server a, forming a resource scheduling server. Here, the number of the carbon atoms is not particularly limited.
The cloud hard disk creation method in the present application is described above with reference to fig. 1 and fig. 2, and the following describes, by way of example and not limitation, the cloud hard disk creation method in the present application in detail with reference to fig. 3.
The method shown in fig. 3 comprises:
in step S301, various information of the storage pool is input.
Optionally, the data collection service collects usage data for each storage pool. For example, usage data of each of storage pools 1 to m is collected. m is an integer greater than 1.
Step S302, input information is transmitted.
Optionally, the data collection service transmits the data to the data processing service after collecting each item of usage data.
In step S303, data is written.
Optionally, after receiving the data, the data processing service writes the usage data in each storage pool into the data storage device.
Step S304, inputting the information of creating the cloud hard disk.
Optionally, the user inputs creation information for creating the cloud hard disk based on the cloud platform.
Step S305 calls data.
Optionally, after monitoring the creation information transmitted by the cloud platform, the resource scheduling service queries a storage pool matched with the creation information from the data storage device.
And step S306, returning the optimal storage pool information.
Optionally, the resource scheduling service determines the storage pool with the highest comprehensive score according to the obtained multiple items of usage data of each storage pool, takes the storage pool as the optimal storage pool, and returns the optimal storage pool to the cloud platform.
According to the cloud hard disk creation method provided by the embodiment of the application, the creation information of the cloud hard disk is acquired; determining a plurality of storage pools that match the creation information; obtaining a plurality of items of usage data for each of the storage pools; determining a composite score for each of the storage pools from the plurality of usage data for each of the storage pools; and creating the cloud hard disk in the storage pool with the maximum comprehensive score according to the creation information. Therefore, the information of each dimension of the storage pool is acquired, the comprehensive score of each storage pool is calculated, the real use condition of each storage pool can be truly and effectively reflected, the optimal storage pool is selected to be used for creating the cloud hard disk, the resources of each storage pool can be guaranteed to be utilized to the maximum extent, and the quality of the cloud hard disk cannot be influenced due to the fact that the cloud hard disk is distributed too intensively. In addition, the preset weight is calculated in a mode of subjective and objective combination, one-sidedness of subjectively determined weight is avoided, and the method and the device are suitable for different service scenes. And the storage pool is selected by the mode, the subsequent newly expanded storage pool can be effectively scheduled, and the expandability of the storage system is ensured.
Based on the same inventive concept, as shown in fig. 4, in the embodiment of the present application, there is also provided a cloud hard disk creation apparatus corresponding to the cloud hard disk creation method shown in fig. 1, it should be understood that the apparatus 400 corresponds to the method embodiment shown in fig. 1, and can perform various steps related to the method embodiment, and specific functions of the apparatus 400 may be referred to the description above, and detailed descriptions are appropriately omitted here to avoid repetition. Specifically, the apparatus 400 includes:
a first obtaining unit 410, configured to obtain creation information of a cloud hard disk;
a first processing unit 420 for determining a plurality of storage pools matching the creation information;
a second obtaining unit 430, configured to obtain multiple items of usage data of each of the storage pools;
a second processing unit 440 for determining a composite score for each of the storage pools based on the plurality of usage data for each of the storage pools;
a creating unit 450, configured to create the cloud hard disk in the storage pool with the largest comprehensive score according to the creation information.
Optionally, the second processing unit 430 is further configured to determine a ranking of each item of usage data in the plurality of items of usage data of each of the storage pools relative to corresponding items of data in storage pools other than the storage pool; obtaining a weight corresponding to each item of the usage data of each storage pool; determining a composite score for each of the storage pools based on the ranking and the weight.
Optionally, the composite score satisfies:
wherein s is used to characterize the composite score for each of the storage pools and the creation information; the n characterizes the number of the plurality of items of usage data, and the k characterizes the kth item of usage data of the plurality of items of usage data; said rkCharacterizing a ranking corresponding to the kth item of usage data; said wkCharacterizing the weight to which the k-th item of usage data corresponds.
Optionally, the obtaining a weight corresponding to each item of usage data of each storage pool includes: determining a degree of change coefficient for said each usage data for each said storage pool; and determining the weight corresponding to each item of the use data according to the change degree coefficient.
Optionally, the determining a coefficient of degree of change of said each usage data for each said storage pool comprises: determining a difference between a maximum value and a minimum value of usage data for each of the same items in the plurality of storage pools; determining an average of usage data for each identical item in the plurality of storage pools; and using the quotient of the difference value and the average value as the coefficient of the degree of change of each item of the usage data of each storage pool.
Optionally, the coefficient of degree of change satisfies:
wherein, ckCharacterizing the coefficient of variation, said vkCharacterizing an aggregate belonging to the kth usage data in each of the storage pools; max (v)k) For determining the maximum value in the set; min (v)k) For determining a minimum value in said set; average (v)k) For determining the average of said set.
Optionally, the determining, according to the coefficient of degree of change, a weight corresponding to each item of usage data includes: determining an importance level of said each item of usage data; and determining the weight corresponding to each item of the use data according to the degree of change coefficient corresponding to each item of the use data and the importance level.
Optionally, the plurality of items of usage data include first usage data characterizing remaining storage space of the storage pool, second usage data characterizing used storage space of the storage pool, third usage data characterizing a number of cloud disks created by the storage pool, fourth usage data characterizing a number of snapshots created by the storage pool, fifth usage data characterizing a current number of read and write operations per second of the storage pool, and sixth usage data characterizing a throughput of the storage pool.
Based on the same inventive concept, the present application further provides an electronic device, and fig. 5 is a block diagram of a structure of the electronic device 500 in the embodiment of the present application, as shown in fig. 5. Electronic device 500 may include a processor 510, a communication interface 520, a memory 530, and at least one communication bus 540. Wherein the communication bus 540 is used for realizing direct connection communication of these components. The communication interface 520 of the device in the embodiment of the present application is used for performing signaling or data communication with other node devices. Processor 510 may be an integrated circuit chip having signal processing capabilities.
The Processor 510 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor 510 may be any conventional processor or the like.
The Memory 530 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 530 stores computer readable instructions that, when executed by the processor 510, enable the electronic device 500 to perform the steps associated with the method embodiment of fig. 1 described above.
Alternatively, the electronic device 500 may be a cloud server or a computer. If the electronic device 500 is a cloud server or a computer, a cloud platform is operated thereon. The cloud platform is used for providing cloud computing-based services for users, and the services are used when the users create cloud resources.
Optionally, the electronic device 500 may further run a resource scheduling service, a data processing service, and a data collecting service.
Optionally, the resource scheduling service, the data processing service, and the data collecting service may be executed by the processor 510, and are not limited in this respect.
Optionally, the electronic device 500 may further include a memory controller, an input-output unit, and a display unit.
The memory 530, the memory controller, the processor 510, the input/output unit, the audio unit, and the display unit are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these elements may be electrically coupled to each other via one or more communication buses 540. The processor 510 is used to execute executable modules stored in the memory 530, such as software functional modules or computer programs included in the apparatus 400. Also, the apparatus 400 is configured to perform the following method: acquiring creating information of a cloud hard disk; determining a plurality of storage pools that match the creation information; obtaining a plurality of items of usage data for each of the storage pools; determining a composite score for each of the storage pools from the plurality of usage data for each of the storage pools; and creating the cloud hard disk in the storage pool with the maximum comprehensive score according to the creation information.
The input and output unit is used for providing input data for a user to realize the interaction of the user and the server (or the local terminal). The input/output unit may be, but is not limited to, a mouse, a keyboard, and the like.
The display unit provides an interactive interface (e.g. a user interface, such as an operation interface of a cloud platform) between the electronic device 500 and the user or is used for displaying the created result to the user for reference. In this embodiment, the display unit may be a liquid crystal display or a touch display. In the case of a touch display, the display can be a capacitive touch screen or a resistive touch screen, which supports single-point and multi-point touch operations. The support of single-point and multi-point touch operations means that the touch display can sense touch operations simultaneously generated from one or more positions on the touch display, and the sensed touch operations are sent to the processor for calculation and processing.
It will be appreciated that the configuration shown in FIG. 5 is merely illustrative and that the electronic device 500 may include more or fewer components than shown in FIG. 5 or may have a different configuration than shown in FIG. 5. The components shown in fig. 5 may be implemented in hardware, software, or a combination thereof.
The embodiment of the present application further provides a storage medium, where the storage medium stores instructions, and when the instructions are run on a computer, when the computer program is executed by a processor, the method in the method embodiment is implemented, and in order to avoid repetition, details are not repeated here.
The present application also provides a computer program product which, when run on a computer, causes the computer to perform the method of the method embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be 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.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (11)
1. A cloud hard disk creation method is characterized by comprising the following steps:
acquiring creating information of a cloud hard disk;
determining a plurality of storage pools that match the creation information;
obtaining a plurality of items of usage data for each of the storage pools;
determining a composite score for each of the storage pools from the plurality of usage data for each of the storage pools;
and creating the cloud hard disk in the storage pool with the maximum comprehensive score according to the creation information.
2. The method of claim 1, wherein determining a composite score for each of the storage pools from the plurality of usage data for each of the storage pools comprises:
determining a ranking of each of the plurality of usage data for each of the storage pools relative to corresponding items of data in storage pools other than the storage pool;
obtaining a weight corresponding to each item of the usage data of each storage pool;
determining a composite score for each of the storage pools based on the ranking and the weight.
3. The method of claim 2, wherein the composite score satisfies:
wherein s is used to characterize the composite score for each of the storage pools and the creation information; the n characterizes the number of the plurality of items of usage data, and the k characterizes the kth item of usage data of the plurality of items of usage data; said rkCharacterizing a ranking corresponding to the kth item of usage data; said wkCharacterizing the weight to which the k-th item of usage data corresponds.
4. The method of claim 2 or 3, wherein said obtaining a weight corresponding to said each usage data for each of said storage pools comprises:
determining a degree of change coefficient for said each usage data for each said storage pool;
and determining the weight corresponding to each item of the use data according to the change degree coefficient.
5. The method of claim 4, wherein said determining a coefficient of degree of change for said each usage data for each said storage pool comprises:
determining a difference between a maximum value and a minimum value of usage data for each of the same items in the plurality of storage pools;
determining an average of usage data for each identical item in the plurality of storage pools;
and using the quotient of the difference value and the average value as the coefficient of the degree of change of each item of the usage data of each storage pool.
6. The method of claim 5, wherein the coefficient of variation satisfies:
wherein, ckCharacterizing the coefficient of variation, said vkCharacterizing items belonging to the k-th entry in each of the storage poolsA collection of data; max (v)k) For determining the maximum value in the set; min (v)k) For determining a minimum value in said set; average (v)k) For determining the average of said set.
7. The method according to claim 4, wherein the determining the weight corresponding to each item of usage data according to the coefficient of variation comprises:
determining an importance level of said each item of usage data;
and determining the weight corresponding to each item of the use data according to the degree of change coefficient corresponding to each item of the use data and the importance level.
8. The method of any of claims 1-3, wherein the plurality of usage data includes first usage data characterizing remaining storage space of the storage pool, second usage data characterizing used storage space of the storage pool, third usage data characterizing a number of cloud disks that the storage pool has created, fourth usage data characterizing a number of snapshots that the storage pool has created, fifth usage data characterizing a current number of read and write operations per second of the storage pool, and sixth usage data characterizing a throughput of the storage pool.
9. An apparatus for creating a cloud hard disk, the apparatus comprising:
the first acquisition unit is used for acquiring the creation information of the cloud hard disk;
a first processing unit for determining a plurality of storage pools matching the creation information;
a second obtaining unit, configured to obtain multiple items of usage data of each of the storage pools;
a second processing unit for determining a composite score for each of the storage pools based on the plurality of items of usage data for each of the storage pools;
and the creating unit is used for creating the cloud hard disk in the storage pool with the maximum comprehensive score according to the creating information.
10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the cloud disk creation method according to any one of claims 1 to 8 when executing the computer program.
11. A storage medium having stored therein instructions that, when executed on a computer, cause the computer to execute the cloud hard disk creation method according to any one of claims 1 to 8.
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