CN116069448B - Sub-service resource scheduling method and system for cloud migration - Google Patents

Sub-service resource scheduling method and system for cloud migration Download PDF

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CN116069448B
CN116069448B CN202310120952.6A CN202310120952A CN116069448B CN 116069448 B CN116069448 B CN 116069448B CN 202310120952 A CN202310120952 A CN 202310120952A CN 116069448 B CN116069448 B CN 116069448B
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CN116069448A (en
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孙明柱
杨俊俊
金霄
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Titanium Shanghai Information Technology Co ltd
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/5044—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering hardware capabilities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/54—Interprogram communication
    • G06F9/546—Message passing systems or structures, e.g. queues
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/4557—Distribution of virtual machine instances; Migration and load balancing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/45595—Network integration; Enabling network access in virtual machine instances
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Abstract

The invention provides a sub-service resource scheduling method and a system for cloud migration, wherein the method comprises the following steps: acquiring first configuration data of an original virtual machine in an original cloud platform, wherein the first configuration data comprises a first dependency relationship between original sub-business resources in the original virtual machine; acquiring second configuration data of a target virtual machine in the target cloud platform, wherein the second configuration data comprises a second dependency relationship between target sub-service resources in the target virtual machine; copying the first dependency relationship in the original cloud platform into the target cloud platform to obtain a differential dependency relationship of the first dependency relationship and the second dependency relationship; and migrating the original sub-business resources in the original cloud platform to the target cloud platform according to the differentiated dependency relationship. According to the sub-business resource scheduling method and system for cloud migration, provided by the invention, the complexity of the sub-business resource scheduling of cloud migration is reduced through the differentiated dependency relationship of the first dependency relationship and the second dependency relationship, and the cloud migration efficiency is greatly improved.

Description

Sub-service resource scheduling method and system for cloud migration
Technical Field
The invention relates to the technical field of cloud computing, in particular to a sub-business resource scheduling method and system for cloud migration.
Background
Cloud migration refers to the meaning of an enterprise's migration from a traditional platform to a cloud platform. Because cloud computing platforms have advantages over traditional application platforms in terms of powerful computing power, storage capacity, diversified services, and cost effectiveness. At present, the cloud migration is mainly from a physical machine to a virtual machine or from a virtual machine to a virtual machine, namely from an original physical machine of a user to the cloud virtual machine or from a cloud environment to another cloud environment. However, cloud migration has the problems of high migration complexity, complex association relation and related sub-business resources.
Therefore, it is necessary to provide a method and a system for scheduling sub-service resources for cloud migration, which can solve the above-mentioned problems.
Disclosure of Invention
Aiming at the problems and the shortcomings of the prior art, the invention provides a sub-service resource scheduling method and a system for cloud migration, which reduce the complexity of the sub-service resource scheduling of the cloud migration and greatly improve the efficiency of the cloud migration through the differentiated dependency relationship of a first dependency relationship and a second dependency relationship.
The invention solves the technical problems by the following technical proposal:
the invention provides a sub-service resource scheduling method for cloud migration, which comprises the following steps:
acquiring first configuration data of an original virtual machine in an original cloud platform, wherein the first configuration data comprises a first dependency relationship between original sub-business resources in the original virtual machine;
acquiring second configuration data of a target virtual machine in a target cloud platform, wherein the second configuration data comprises a second dependency relationship between target sub-business resources in the target virtual machine;
copying the first dependency relationship in the original cloud platform to the target cloud platform to obtain a differential dependency relationship of the first dependency relationship and the second dependency relationship;
and migrating the original sub-business resources in the original cloud platform to the target cloud platform according to the differentiated dependency relationship.
Preferably, mapping the first dependency relationship in the original cloud platform to the target cloud, and obtaining the differentiated dependency relationship of the first dependency relationship and the second dependency relationship includes:
the first dependency relationship comprises a dependency relationship between the original sub-business resources and other original sub-business resources required by the operation of the original sub-business resources, wherein the original sub-business resources comprise a plurality of resource operating system functions, system libraries, drivers, hardware functions, system foundation components and programming framework components and form a first sub-business resource list, the first sub-business resource list comprises names of the original sub-business resources and the first dependency relationship, and the first sub-business resource list and the first dependency relationship form a first firmware package;
the second dependency relationship comprises a dependency relationship between the target sub-service resource and other target sub-service resources required by the operation of the target sub-service resource, wherein the target sub-service resource comprises a plurality of resource operating system functions, a system library, a driver, a hardware function, a system base component and a programming framework component and forms a second sub-service resource list, the second sub-service resource list comprises names of the target sub-service resources and the second dependency relationship, and the second sub-service resource list and the second dependency relationship form a second firmware package;
copying the first firmware package into the target cloud platform, obtaining a differential dependency relationship between the first dependency relationship and the second dependency relationship through a traversal algorithm, and obtaining the dependency relationship between the original sub-business resources and other original sub-business resources required by the operation of the target cloud platform according to the differential dependency relationship.
Preferably, migrating the original sub-service resources in the original cloud platform to the target cloud platform according to the differential dependency relationship further includes:
reconstructing a relational database in the original cloud platform into a database service provided by the target cloud platform service provider;
reconstructing the self-built message middleware in the original cloud platform into a message queue service provided by the target cloud platform service provider;
and reconstructing HAproxy in the original cloud platform into load balancing service provided by the target cloud platform service provider.
Preferably, before migrating the original sub-business resources in the original cloud to the target cloud platform according to the differential dependency relationship, the method further includes:
acquiring average CPU computing power of a target virtual machine in the target cloud platform within a preset time, sequencing the target virtual machines according to the average CPU computing power of the target virtual machine, and preferentially transferring original sub-service resources in the original cloud platform to the target virtual machine with high average CPU computing power.
Preferably, when the average CPU computing power of a plurality of the target virtual machines reaches a first threshold, prompting the target cloud platform to perform node capacity expansion operation.
The invention also provides a sub-service resource scheduling system for cloud migration, which comprises:
the system comprises a first dependency relationship acquisition unit, a second dependency relationship acquisition unit and a second dependency relationship acquisition unit, wherein the first dependency relationship acquisition unit is used for acquiring first configuration data of an original virtual machine in an original cloud platform, and the first configuration data comprises first dependency relationships among original sub-business resources in the original virtual machine;
a second dependency relationship obtaining unit, configured to obtain second configuration data of a target virtual machine in a target cloud platform, where the second configuration data includes a second dependency relationship between target sub-service resources in the target virtual machine;
the differential dependency relationship acquisition unit is used for copying the first dependency relationship in the original cloud platform to the target cloud platform to obtain a differential dependency relationship of the first dependency relationship and the second dependency relationship;
the original sub-business resource migration unit is used for migrating the original sub-business resources in the original cloud platform to the target cloud platform according to the differentiated dependency relationship.
Preferably, mapping the first dependency relationship in the original cloud platform to the target cloud, and obtaining the differentiated dependency relationship of the first dependency relationship and the second dependency relationship includes:
the first dependency relationship comprises a dependency relationship between the original sub-business resources and other original sub-business resources required by the operation of the original sub-business resources, wherein the original sub-business resources comprise a plurality of resource operating system functions, system libraries, drivers, hardware functions, system foundation components and programming framework components and form a first sub-business resource list, the first sub-business resource list comprises names of the original sub-business resources and the first dependency relationship, and the first sub-business resource list and the first dependency relationship form a first firmware package;
the second dependency relationship comprises a dependency relationship between the target sub-service resource and other target sub-service resources required by the operation of the target sub-service resource, wherein the target sub-service resource comprises a plurality of resource operating system functions, a system library, a driver, a hardware function, a system base component and a programming framework component and forms a second sub-service resource list, the second sub-service resource list comprises names of the target sub-service resources and the second dependency relationship, and the second sub-service resource list and the second dependency relationship form a second firmware package;
copying the first firmware package into the target cloud platform, obtaining a differential dependency relationship between the first dependency relationship and the second dependency relationship through a traversal algorithm, and obtaining the dependency relationship between the original sub-business resources and other original sub-business resources required by the operation of the target cloud platform according to the differential dependency relationship.
Preferably, migrating the original sub-service resources in the original cloud platform to the target cloud platform according to the differential dependency relationship further includes:
reconstructing a relational database in the original cloud platform into a database service provided by the target cloud platform service provider;
reconstructing the self-built message middleware in the original cloud platform into a message queue service provided by the target cloud platform service provider;
and reconstructing HAproxy in the original cloud platform into load balancing service provided by the target cloud platform service provider.
Preferably, before migrating the original sub-business resources in the original cloud to the target cloud platform according to the differential dependency relationship, the method further includes:
acquiring average CPU computing power of a target virtual machine in the target cloud platform within a preset time, sequencing the target virtual machines according to the average CPU computing power of the target virtual machine, and preferentially transferring original sub-service resources in the original cloud platform to the target virtual machine with high average CPU computing power.
Preferably, when the average CPU computing power of a plurality of the target virtual machines reaches a first threshold, prompting the target cloud platform to perform node capacity expansion operation.
Compared with the prior art, the technical scheme of the embodiment of the invention has the following beneficial effects:
according to the sub-service resource scheduling method and system for cloud migration, first configuration data of an original virtual machine in an original cloud platform are obtained, and the first configuration data comprise first dependency relations among original sub-service resources in the original virtual machine; acquiring second configuration data of a target virtual machine in a target cloud platform, wherein the second configuration data comprises a second dependency relationship between target sub-business resources in the target virtual machine; copying the first dependency relationship in the original cloud platform to the target cloud platform to obtain a differential dependency relationship of the first dependency relationship and the second dependency relationship; migrating the original sub-business resources in the original cloud platform to the target cloud platform according to the differential dependency relationship, and reducing the complexity of scheduling the sub-business resources of cloud migration through the differential dependency relationship of the first dependency relationship and the second dependency relationship, thereby greatly improving the cloud migration efficiency;
further, reconstructing a relational database in the original cloud platform into a database service provided by the target cloud platform service provider; reconstructing the self-built message middleware in the original cloud platform into a message queue service provided by the target cloud platform service provider; the HAproxy in the original cloud platform is reconstructed into the load balancing service provided by the target cloud platform service provider, and the application program is subjected to simple cloud optimization, so that the management cost is reduced and the efficiency is improved;
further, acquiring average CPU computing power of a target virtual machine in the target cloud platform within a preset time, sequencing the target virtual machines according to the average CPU computing power of the target virtual machine, and preferentially transferring original sub-service resources in the original cloud platform to the target virtual machine with high average CPU computing power, so that higher operation efficiency can be maintained after the original sub-service resources in the original cloud platform are transferred to the target cloud platform;
further, when the average CPU computing power of a plurality of target virtual machines reaches a first threshold, prompting the target cloud platform to perform node capacity expansion operation, so that the problem that the nodes of the target cloud platform are insufficient when the original sub-business resources in the original cloud platform are migrated to the target cloud platform is avoided.
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Fig. 1 is a schematic flow chart of a sub-service resource scheduling method for cloud migration according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a sub-service resource scheduling system for cloud migration according to an embodiment of the present 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 be within the scope of the invention.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Based on the problems existing in the prior art, the method and the system for scheduling the sub-service resources for cloud migration are provided, and the complexity of scheduling the sub-service resources for cloud migration is reduced through the differentiated dependency relationship of the first dependency relationship and the second dependency relationship, so that the efficiency of cloud migration is greatly improved.
Fig. 1 is a flow chart of a sub-service resource scheduling method for cloud migration according to an embodiment of the present invention, and as shown in fig. 1, the present invention provides a sub-service resource scheduling method for cloud migration, where the method includes:
step S101: acquiring first configuration data of an original virtual machine in an original cloud platform, wherein the first configuration data comprises a first dependency relationship between original sub-business resources in the original virtual machine;
step S102: acquiring second configuration data of a target virtual machine in a target cloud platform, wherein the second configuration data comprises a second dependency relationship between target sub-business resources in the target virtual machine;
step S103: copying the first dependency relationship in the original cloud platform to the target cloud platform to obtain a differential dependency relationship of the first dependency relationship and the second dependency relationship;
step S104: and migrating the original sub-business resources in the original cloud platform to the target cloud platform according to the differentiated dependency relationship.
The complexity of sub-business resource scheduling of cloud migration is reduced through the differentiated dependency relationship of the first dependency relationship and the second dependency relationship, and the cloud migration efficiency is greatly improved.
In a specific implementation, mapping the first dependency relationship in the original cloud platform to the target cloud, and obtaining the differential dependency relationship of the first dependency relationship and the second dependency relationship includes:
the first dependency relationship comprises a dependency relationship between the original sub-business resources and other original sub-business resources required by the operation of the original sub-business resources, wherein the original sub-business resources comprise a plurality of resource operating system functions, system libraries, drivers, hardware functions, system foundation components and programming framework components and form a first sub-business resource list, the first sub-business resource list comprises names of the original sub-business resources and the first dependency relationship, and the first sub-business resource list and the first dependency relationship form a first firmware package;
the second dependency relationship comprises a dependency relationship between the target sub-service resource and other target sub-service resources required by the operation of the target sub-service resource, wherein the target sub-service resource comprises a plurality of resource operating system functions, a system library, a driver, a hardware function, a system base component and a programming framework component and forms a second sub-service resource list, the second sub-service resource list comprises names of the target sub-service resources and the second dependency relationship, and the second sub-service resource list and the second dependency relationship form a second firmware package;
copying the first firmware package into the target cloud platform, obtaining a differential dependency relationship between the first dependency relationship and the second dependency relationship through a traversal algorithm, and obtaining the dependency relationship between the original sub-business resources and other original sub-business resources required by the operation of the target cloud platform according to the differential dependency relationship.
Specifically, the target sub-service resource may further include a data source, a data file, a data storage, and a data backup.
In a specific implementation, migrating the original sub-service resources in the original cloud platform to the target cloud platform according to the differential dependency relationship further includes:
reconstructing a relational database in the original cloud platform into a database service provided by the target cloud platform service provider;
reconstructing the self-built message middleware in the original cloud platform into a message queue service provided by the target cloud platform service provider;
and reconstructing HAproxy in the original cloud platform into load balancing service provided by the target cloud platform service provider. HAProxy provides high availability, load balancing, and TCP and HTTP based application proxy.
By doing a simple cloud optimization for the application, management costs are reduced and efficiency is improved.
In a specific implementation, before migrating the original sub-service resources in the original cloud to the target cloud platform according to the differential dependency relationship, the method further includes:
the method comprises the steps of obtaining average CPU computing power of a target virtual machine in the target cloud platform within preset time, sorting the target virtual machines according to the average CPU computing power of the target virtual machine, and preferentially transferring original sub-service resources in the original cloud platform to the target virtual machine with high average CPU computing power, so that higher computing efficiency can be maintained after the original sub-service resources in the original cloud platform are transferred to the target cloud platform.
Specifically, the average CPU calculation power and time are fitted by a nonlinear regression curve model, which is specifically calculated by the following formula:
y＝m 0 +m 1 x+m 2 x 2
wherein y is the average CPU calculation power, m 0 、m 1 、m 2 Is a model coefficient, and x is time.
The nonlinear regression is converted into linear regression, and the specific conversion rule is as follows:
z 1 ＝x
z 2 ＝x 2
y＝m 0 +m 1 z 1 +m 2 z 2
constructing an estimated value model of the least square regression parameters to determine the values of the parameters, wherein the estimated value model is specifically calculated by the following formula:
Figure BDA0004079968050000101
wherein m is 0 、m i 、m 2 For model coefficients, i is an integer.
In a specific implementation, when the average CPU computing power of a plurality of target virtual machines reaches a first threshold, prompting the target cloud platform to perform node capacity expansion operation.
Specifically, monitoring the average CPU computing power in a plurality of containers in the target virtual machine, and prompting the target cloud platform to perform node capacity expansion operation when the average CPU computing power in the plurality of containers in the target virtual machine reaches a first threshold value so as to calculate whether the plurality of cloud clusters need to perform node capacity expansion. Therefore, the problem that the nodes of the target cloud platform are insufficient when the original sub-service resources in the original cloud platform are migrated to the target cloud platform is avoided, and the first threshold can be set according to experience by a person skilled in the art, and the description is omitted here.
Fig. 2 is a schematic block diagram of a sub-service resource scheduling system for cloud migration according to an embodiment of the present invention, and referring now to fig. 2, a sub-service resource scheduling system for cloud migration includes:
a first dependency relationship obtaining unit 21, configured to obtain first configuration data of an original virtual machine in an original cloud platform, where the first configuration data includes a first dependency relationship between original sub-service resources in the original virtual machine;
a second dependency relationship obtaining unit 22, configured to obtain second configuration data of a target virtual machine in a target cloud platform, where the second configuration data includes a second dependency relationship between target sub-service resources in the target virtual machine;
a differential dependency relationship obtaining unit 23, configured to copy the first dependency relationship in the original cloud platform to the target cloud platform, so as to obtain a differential dependency relationship between the first dependency relationship and the second dependency relationship;
and the original sub-business resource migration unit 24 is used for migrating the original sub-business resources in the original cloud platform to the target cloud platform according to the differentiated dependent relationship.
In a specific implementation, mapping the first dependency relationship in the original cloud platform to the target cloud, and obtaining the differential dependency relationship of the first dependency relationship and the second dependency relationship includes:
the first dependency relationship comprises a dependency relationship between the original sub-business resources and other original sub-business resources required by the operation of the original sub-business resources, wherein the original sub-business resources comprise a plurality of resource operating system functions, system libraries, drivers, hardware functions, system foundation components and programming framework components and form a first sub-business resource list, the first sub-business resource list comprises names of the original sub-business resources and the first dependency relationship, and the first sub-business resource list and the first dependency relationship form a first firmware package;
the second dependency relationship comprises a dependency relationship between the target sub-service resource and other target sub-service resources required by the operation of the target sub-service resource, wherein the target sub-service resource comprises a plurality of resource operating system functions, a system library, a driver, a hardware function, a system base component and a programming framework component and forms a second sub-service resource list, the second sub-service resource list comprises names of the target sub-service resources and the second dependency relationship, and the second sub-service resource list and the second dependency relationship form a second firmware package;
copying the first firmware package into the target cloud platform, obtaining a differential dependency relationship between the first dependency relationship and the second dependency relationship through a traversal algorithm, and obtaining the dependency relationship between the original sub-business resources and other original sub-business resources required by the operation of the target cloud platform according to the differential dependency relationship.
In a specific implementation, migrating the original sub-service resources in the original cloud platform to the target cloud platform according to the differential dependency relationship further includes:
reconstructing a relational database in the original cloud platform into a database service provided by the target cloud platform service provider;
reconstructing the self-built message middleware in the original cloud platform into a message queue service provided by the target cloud platform service provider;
and reconstructing HAproxy in the original cloud platform into load balancing service provided by the target cloud platform service provider.
In a specific implementation, before migrating the original sub-service resources in the original cloud to the target cloud platform according to the differential dependency relationship, the method further includes:
acquiring average CPU computing power of a target virtual machine in the target cloud platform within a preset time, sequencing the target virtual machines according to the average CPU computing power of the target virtual machine, and preferentially transferring original sub-service resources in the original cloud platform to the target virtual machine with high average CPU computing power.
In a specific implementation, when the average CPU computing power of a plurality of target virtual machines reaches a first threshold, prompting the target cloud platform to perform node capacity expansion operation
In summary, the method and the system for scheduling sub-service resources for cloud migration provided by the embodiments of the present invention acquire first configuration data of an original virtual machine in an original cloud platform, where the first configuration data includes a first dependency relationship between original sub-service resources in the original virtual machine; acquiring second configuration data of a target virtual machine in a target cloud platform, wherein the second configuration data comprises a second dependency relationship between target sub-business resources in the target virtual machine; copying the first dependency relationship in the original cloud platform to the target cloud platform to obtain a differential dependency relationship of the first dependency relationship and the second dependency relationship; migrating the original sub-business resources in the original cloud platform to the target cloud platform according to the differential dependency relationship, and reducing the complexity of scheduling the sub-business resources of cloud migration through the differential dependency relationship of the first dependency relationship and the second dependency relationship, thereby greatly improving the cloud migration efficiency;
further, reconstructing a relational database in the original cloud platform into a database service provided by the target cloud platform service provider; reconstructing the self-built message middleware in the original cloud platform into a message queue service provided by the target cloud platform service provider; the HAproxy in the original cloud platform is reconstructed into the load balancing service provided by the target cloud platform service provider, and the application program is subjected to simple cloud optimization, so that the management cost is reduced and the efficiency is improved;
further, acquiring average CPU computing power of a target virtual machine in the target cloud platform within a preset time, sequencing the target virtual machines according to the average CPU computing power of the target virtual machine, and preferentially transferring original sub-service resources in the original cloud platform to the target virtual machine with high average CPU computing power, so that higher operation efficiency can be maintained after the original sub-service resources in the original cloud platform are transferred to the target cloud platform;
further, when the average CPU computing power of a plurality of target virtual machines reaches a first threshold, prompting the target cloud platform to perform node capacity expansion operation, so that the problem that the nodes of the target cloud platform are insufficient when the original sub-business resources in the original cloud platform are migrated to the target cloud platform is avoided.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; 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 or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.

Claims (8)

1. A sub-business resource scheduling method for cloud migration, the method comprising:
acquiring first configuration data of an original virtual machine in an original cloud platform, wherein the first configuration data comprises a first dependency relationship between original sub-business resources in the original virtual machine;
acquiring second configuration data of a target virtual machine in a target cloud platform, wherein the second configuration data comprises a second dependency relationship between target sub-business resources in the target virtual machine;
copying the first dependency relationship in the original cloud platform to the target cloud platform to obtain a differential dependency relationship of the first dependency relationship and the second dependency relationship;
the first dependency relationship comprises a dependency relationship between the original sub-business resources and other original sub-business resources required by the operation of the original sub-business resources, wherein the original sub-business resources comprise a plurality of resource operating system functions, system libraries, drivers, hardware functions, system foundation components and programming framework components and form a first sub-business resource list, the first sub-business resource list comprises names of the original sub-business resources and the first dependency relationship, and the first sub-business resource list and the first dependency relationship form a first firmware package;
the second dependency relationship comprises a dependency relationship between the target sub-service resource and other target sub-service resources required by the operation of the target sub-service resource, wherein the target sub-service resource comprises a plurality of resource operating system functions, a system library, a driver, a hardware function, a system base component and a programming framework component and forms a second sub-service resource list, the second sub-service resource list comprises names of the target sub-service resources and the second dependency relationship, and the second sub-service resource list and the second dependency relationship form a second firmware package;
copying the first firmware package into the target cloud platform, obtaining a differential dependency relationship between the first dependency relationship and the second dependency relationship through a traversal algorithm, and obtaining the dependency relationship between the original sub-business resources and other original sub-business resources required by the operation of the target cloud platform according to the differential dependency relationship;
and migrating the original sub-business resources in the original cloud platform to the target cloud platform according to the differentiated dependency relationship.
2. The method for scheduling sub-business resources for cloud migration according to claim 1, wherein migrating the original sub-business resources in the original cloud platform into the target cloud platform according to the differentiated dependency relationship further comprises:
reconstructing a relational database in the original cloud platform into a database service provided by the target cloud platform service provider;
reconstructing the self-built message middleware in the original cloud platform into a message queue service provided by the target cloud platform service provider;
and reconstructing HAproxy in the original cloud platform into load balancing service provided by the target cloud platform service provider.
3. The method for scheduling sub-service resources for cloud migration according to claim 1, wherein before migrating the original sub-service resources in the original cloud to the target cloud platform according to the differentiated dependency relationship, further comprises:
acquiring average CPU computing power of a target virtual machine in the target cloud platform within a preset time, sequencing the target virtual machines according to the average CPU computing power of the target virtual machine, and preferentially transferring original sub-service resources in the original cloud platform to the target virtual machine with high average CPU computing power.
4. The sub-service resource scheduling method for cloud migration according to claim 3, wherein when the average CPU computing power of a plurality of the target virtual machines reaches a first threshold, the target cloud platform is prompted to perform node capacity expansion operation.
5. A sub-business resource scheduling system for cloud migration, the system comprising:
the system comprises a first dependency relationship acquisition unit, a second dependency relationship acquisition unit and a second dependency relationship acquisition unit, wherein the first dependency relationship acquisition unit is used for acquiring first configuration data of an original virtual machine in an original cloud platform, and the first configuration data comprises first dependency relationships among original sub-business resources in the original virtual machine;
a second dependency relationship obtaining unit, configured to obtain second configuration data of a target virtual machine in a target cloud platform, where the second configuration data includes a second dependency relationship between target sub-service resources in the target virtual machine;
the differential dependency relationship acquisition unit is used for copying the first dependency relationship in the original cloud platform to the target cloud platform to obtain a differential dependency relationship of the first dependency relationship and the second dependency relationship;
the first dependency relationship comprises a dependency relationship between the original sub-business resources and other original sub-business resources required by the operation of the original sub-business resources, wherein the original sub-business resources comprise a plurality of resource operating system functions, system libraries, drivers, hardware functions, system foundation components and programming framework components and form a first sub-business resource list, the first sub-business resource list comprises names of the original sub-business resources and the first dependency relationship, and the first sub-business resource list and the first dependency relationship form a first firmware package;
the second dependency relationship comprises a dependency relationship between the target sub-service resource and other target sub-service resources required by the operation of the target sub-service resource, wherein the target sub-service resource comprises a plurality of resource operating system functions, a system library, a driver, a hardware function, a system base component and a programming framework component and forms a second sub-service resource list, the second sub-service resource list comprises names of the target sub-service resources and the second dependency relationship, and the second sub-service resource list and the second dependency relationship form a second firmware package;
copying the first firmware package into the target cloud platform, obtaining a differential dependency relationship between the first dependency relationship and the second dependency relationship through a traversal algorithm, and obtaining the dependency relationship between the original sub-business resources and other original sub-business resources required by the operation of the target cloud platform according to the differential dependency relationship;
the original sub-business resource migration unit is used for migrating the original sub-business resources in the original cloud platform to the target cloud platform according to the differentiated dependency relationship.
6. The sub-business resource scheduling system for cloud migration of claim 5, wherein migrating the original sub-business resources in the original cloud platform into the target cloud platform according to the differentiated dependency relationship further comprises:
reconstructing a relational database in the original cloud platform into a database service provided by the target cloud platform service provider;
reconstructing the self-built message middleware in the original cloud platform into a message queue service provided by the target cloud platform service provider;
and reconstructing HAproxy in the original cloud platform into load balancing service provided by the target cloud platform service provider.
7. The sub-business resource scheduling system for cloud migration of claim 5, further comprising, prior to migrating the original sub-business resources in the original cloud into the target cloud platform according to the differentiated dependency relationship:
acquiring average CPU computing power of a target virtual machine in the target cloud platform within a preset time, sequencing the target virtual machines according to the average CPU computing power of the target virtual machine, and preferentially transferring original sub-service resources in the original cloud platform to the target virtual machine with high average CPU computing power.
8. The sub-business resource scheduling system for cloud migration of claim 7, wherein the target cloud platform is prompted to perform node capacity expansion operations when an average CPU computing power of a plurality of the target virtual machines reaches a first threshold.
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