Cloud platform resource expansion method and device, storage medium and electronic equipment

Abstract

The application discloses a cloud platform resource expansion method and device, a storage medium and an electronic device. The method comprises the following steps: acquiring actual business demands corresponding to a plurality of sub-business modules in a business system; determining the total amount of resource configuration required by the plurality of sub-business modules in each of a plurality of computer room cloud platforms when the plurality of sub-business modules are subjected to expansion processing, based on the actual business demands corresponding to the plurality of sub-business modules; and determining the number of target physical machines required by the plurality of sub-business modules in each of the plurality of computer room cloud platforms when the plurality of sub-business modules are subjected to expansion processing, according to the total amount of resource configuration required by the plurality of sub-business modules in each of the plurality of computer room cloud platforms and the business node types corresponding to the plurality of sub-business modules. The application solves the technical problem of low expansion efficiency and poor accuracy of the cloud platform resource expansion method in the related art.

Description

Technical Field

[0001] This invention relates to the field of resource expansion, and more specifically, to a method, apparatus, storage medium, and electronic device for expanding cloud platform resources. Background Technology

[0002] With the development of technology, cloud platforms, as the infrastructure for resource supply, have been widely used across various industries. Due to concerns about privacy and data security on cloud platforms, many users choose private clouds (hereinafter referred to as "cloud platforms") to provide basic resources. As business scenarios become more diverse and system scales expand, the capacity and speed at which cloud platforms supply resources also need to match business development. This necessitates that cloud platform builders possess standardized, regulated, reasonable, efficient, and accurate cloud platform expansion planning capabilities.

[0003] The expansion planning process for cloud platforms involves multiple stages, including requirements gathering, requirements transformation, and resource planning. It requires the participation of multiple departments, including business, development, construction, and planning. Factors to consider include business, development, and resource supply. The overall planning, coordination, and workflow are complex, often requiring manual adjustments to the entire plan due to a single change. Furthermore, due to the high complexity of cloud platform expansion planning and the numerous factors and dimensions to be evaluated, the following problems are frequently encountered: First, using Excel for planning is inefficient, lacking a one-stop planning approach, requiring deep involvement from multiple departments, making coordination and collaboration difficult, and consuming time and resources. Second, the input of business requirements during the requirements gathering stage is non-standard, resulting in a long and complex cycle of converting business requirements into technical requirements. Third, the requirements transformation stage lacks a standard requirements transformation model, leading to unreasonable final solutions and mismatches between expansion and actual business needs. Fourth, the resource planning stage lacks a standard expansion planning model, relying heavily on experience and manual intervention. Currently, no effective solutions have been proposed to address these problems. Summary of the Invention

[0004] This invention provides a method, apparatus, storage medium, and electronic device for expanding cloud platform resources, to at least solve the technical problems of low expansion efficiency and poor accuracy in related technologies for expanding cloud platform resources.

[0005] According to one aspect of the present invention, a method for expanding cloud platform resources is provided, comprising: obtaining the actual business requirements corresponding to a plurality of sub-business modules in a business system, wherein the plurality of sub-business modules are obtained by dividing the businesses included in the business system according to project types; based on the actual business requirements corresponding to the plurality of sub-business modules, determining the total amount of resource configuration required by the plurality of sub-business modules in each of a plurality of data center cloud platforms when expanding the plurality of sub-business modules; and determining the target number of physical machines required by the plurality of sub-business modules in each of the data center cloud platforms when expanding the plurality of sub-business modules, based on the total amount of resource configuration required by the plurality of sub-business modules in each of the data center cloud platforms and the business node types corresponding to the plurality of sub-business modules.

[0006] According to another aspect of the present invention, a cloud platform resource expansion device is also provided, comprising: an acquisition module, configured to acquire the actual business requirements corresponding to a plurality of sub-business modules in a business system, wherein the plurality of sub-business modules are obtained by dividing the businesses included in the business system according to project types; a first determination module, configured to determine, based on the actual business requirements corresponding to the plurality of sub-business modules, the total amount of resource configuration required by the plurality of sub-business modules in each of the plurality of data center cloud platforms when performing the expansion process; and a second determination module, configured to determine, based on the total amount of resource configuration required by the plurality of sub-business modules in each of the data center cloud platforms and the business node types corresponding to the plurality of sub-business modules, the number of target physical machines required by the plurality of sub-business modules in each of the data center cloud platforms when performing the expansion process.

[0007] According to another aspect of the present invention, a non-volatile storage medium is also provided, wherein the non-volatile storage medium stores a plurality of instructions, the instructions being adapted to be loaded by a processor and executed any one of the above-described cloud platform resource expansion methods.

[0008] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any of the above-described cloud platform resource expansion methods.

[0009] In this embodiment of the invention, the actual business requirements corresponding to multiple sub-business modules in a business system are obtained. These multiple sub-business modules are obtained by dividing the businesses included in the business system according to project types. Based on the actual business requirements corresponding to these multiple sub-business modules, the total resource configuration required by each of the multiple data center cloud platforms for the expansion of these multiple sub-business modules is determined. According to the total resource configuration required by each of the multiple sub-business modules in each of the multiple data center cloud platforms, and the business node types corresponding to these multiple sub-business modules, the target number of physical machines required by each of the multiple sub-business modules in each of the multiple data center cloud platforms for the expansion of these multiple sub-business modules is determined. This achieves the goal of accurately planning cloud platform resource expansion, thereby improving the timeliness and accuracy of cloud platform resource expansion planning and solving the technical problems of low expansion efficiency and poor accuracy in related technologies. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0011] Figure 1 This is a schematic diagram of a cloud platform resource expansion method according to an embodiment of the present invention;

[0012] Figure 2 This is a schematic diagram of an optional cloud platform resource expansion system according to an embodiment of the present invention;

[0013] Figure 3 This is a flowchart of an optional cloud platform resource expansion method according to an embodiment of the present invention;

[0014] Figure 4 This is a schematic diagram of a cloud platform resource expansion device according to an embodiment of the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0016] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention will be explained below:

[0018] Resource supply: refers to the services provided by the cloud platform, such as computing, storage, and networking.

[0019] Module: A module is a general concept that can be distinguished by function or other purposes. It refers to the components obtained by breaking down a business system according to business units.

[0020] Sub-business module: refers to the components of a module, that is, the smallest business unit in the module.

[0021] Management and control nodes: The management and control nodes of the cloud platform. The control nodes are responsible for controlling the other nodes, including virtual machine creation, migration, network allocation, storage allocation, etc.

[0022] According to an embodiment of the present invention, a method embodiment for expanding cloud platform resources is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] Figure 1 This is a flowchart of a cloud platform resource expansion method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0024] Step S102: Obtain the actual business requirements corresponding to multiple sub-business modules in the business system. The multiple sub-business modules are obtained by dividing the business included in the business system according to project type.

[0025] It is understandable that the above method divides the business system according to projects. At the project level, the system is broken down into business units to obtain system modules (hereinafter referred to as "modules"); the smallest business unit within a module is determined, which is the aforementioned multiple sub-modules; a collection module MOD{module1,…,module2} consisting of p sub-modules. p}

[0026] In one optional embodiment, the above-mentioned acquisition of the actual business requirements corresponding to the multiple sub-business modules included in the business system includes: acquiring the total business requirements corresponding to the business system; determining the business performance correlation between the multiple sub-business modules and the total business requirements; and determining the actual business requirements corresponding to the multiple sub-business modules based on the total business requirements and the business performance correlation between the multiple sub-business modules and the total business requirements.

[0027] It should be noted that, according to the "barrel theory," the maximum performance a business system can provide depends on the worst-performing sub-business module among the system's critical sub-business modules. To optimize and balance the overall performance of the business system, it is necessary to refine and determine the minimum virtual machine allocation unit that can provide the business performance (crit) for each sub-business module. module By theoretical derivation and collecting historical stress test data, the service performance that the smallest application unit can bear is calculated, and its mapping g(minimum) is obtained. module )→crit module .

[0028] Using the above methods, we first obtain the total business demand (BD) required by the current business system, then determine the business performance relevance between sub-business modules and the overall business system based on the business logic, and initialize the business performance coefficient α for standardized modules. module That is, based on the total business demand (BD) and the business relevance coefficient (α). BD (i.e., business performance relevance), the business requirements collection module calculates the actual business requirements (BD) of this sub-business module. module :BD module =α module ×BD(α BD >0,BD>0).

[0029] Step S104: Based on the actual business needs corresponding to the above-mentioned multiple sub-business modules, determine the total amount of resource configuration required by the above-mentioned multiple sub-business modules in each of the multiple data center cloud platforms when expanding the capacity of the above-mentioned multiple sub-business modules.

[0030] Optionally, the total resource configuration mentioned above may include, but is not limited to, the total virtual machine configuration, the total CPU configuration, the total memory configuration, and the total disk configuration.

[0031] In an optional embodiment, when the total resource configuration includes the total number of virtual machines, the total number of CPUs, the total amount of memory, and the total amount of disk space, the determination of the total resource configuration required by the multiple sub-business modules in each of the multiple data center cloud platforms when performing the expansion process, based on the actual business needs corresponding to the multiple sub-business modules, includes: determining the number of virtual machines required by each of the multiple sub-business modules in each of the multiple data center cloud platforms when performing the expansion process, based on the actual business needs corresponding to the multiple sub-business modules; and determining a preset virtual machine allocation package rule corresponding to the multiple sub-business modules, wherein the preset virtual machine allocation package rule is used to indicate the number of virtual machines required by each of the multiple sub-business modules when performing the expansion process. The CPU, memory, and disk configurations of the aforementioned sub-business modules in a virtual machine on each of the aforementioned data center cloud platforms are determined. Based on the number of virtual machines required by the aforementioned sub-business modules on each of the aforementioned data center cloud platforms, and the aforementioned preset virtual machine allocation package rules, the required CPU, memory, and disk configurations of the aforementioned sub-business modules on each of the aforementioned data center cloud platforms are determined when the aforementioned expansion process is performed. Based on the number of virtual machines, CPU, memory, and disk configurations required by the aforementioned sub-business modules on each of the aforementioned data center cloud platforms, the total number of virtual machines, total number of CPUs, total number of memory, and total number of disks required by the aforementioned sub-business modules on each of the multiple data center cloud platforms are determined.

[0032] By using the above methods, based on the actual business needs corresponding to each of the multiple sub-business modules, the number of virtual machines required for each of the aforementioned data center cloud platforms when expanding the capacity of each sub-business module is determined. According to the preset virtual machine allocation package rules and the number of virtual machines, the required CPU, memory, and disk configurations for each of the aforementioned data center cloud platforms when expanding the capacity of each sub-business module are determined. The required CPU, memory, and disk configurations for each of the aforementioned data center cloud platforms for each sub-business module are summed to obtain the total number of virtual machines, CPUs, memory, and disks required for each data center cloud platform during expansion, thereby improving the accuracy of expansion planning.

[0033] In one optional embodiment, determining the number of virtual machines required by each of the multiple sub-business modules on each data center cloud platform when performing the aforementioned capacity expansion process, based on the actual business needs corresponding to each of the multiple sub-business modules, includes: determining the business performance of the minimum application unit corresponding to each of the multiple sub-business modules, wherein the minimum application unit is used to indicate the minimum number of virtual machines to be applied for corresponding to each of the multiple sub-business modules; determining the number of minimum application units corresponding to each of the multiple sub-business modules based on the actual business needs corresponding to each of the multiple sub-business modules and the business performance of the minimum application units corresponding to each of the multiple sub-business modules; determining the business proportion allocated to each of the multiple sub-business modules on each of the multiple data center cloud platforms; and determining the number of virtual machines required by each of the multiple sub-business modules on each of the multiple data center cloud platforms based on the number of minimum application units corresponding to each of the multiple sub-business modules and the business proportion allocated to each of the multiple sub-business modules on each of the multiple data center cloud platforms.

[0034] It should be noted that, in this embodiment of the invention, virtual machine resource requests are made at the sub-business module level. The smallest unit of virtual machine resource requests made by each sub-business module is the minimum request unit. module This refers to the minimum number of standard virtual machines that can be requested. This minimum request unit serves as the basis for the request of sub-business modules, and the number of virtual machine resources requested by a sub-business module should be a multiple of this minimum request unit. The minimum request unit for virtual machines for a sub-business module can be obtained based on business logic and system rules, and its mapping is: f(module) → minimum module .

[0035] It should be noted that large-scale business systems typically consider multi-site active-active operation and multi-datacenter cloud platform disaster recovery and backup capabilities to ensure stable and reliable operation. In multi-datacenter, multi-cloud scenarios, MOD{module1,…,module2} contains p sub-business modules. p The business system is built with a multi-active architecture across q data center cloud platforms DC{dc1,…,dc}. q Based on business logic, the processing capacity of each sub-business module in different data centers is preset, that is, the ratio of sub-business module m's business in data center n. mn As shown below.

[0036]

[0037] Optionally, calculations can be performed based on meeting business performance requirements. Taking one of multiple sub-business modules as an example, the actual business requirements (BD) of the sub-business module are calculated. moduleand the service performance crit of the smallest application unit module The number of minimum application units (vmunits) required is calculated. module :

[0038]

[0039] Number of virtual machines required (vmnum) module :

[0040] vmnum module =vmunit module ×minimum module

[0041] Among them, minimum module This represents the smallest application unit, i.e., the minimum number of virtual machines required for a sub-business module.

[0042] The above formula can be used to calculate the number of virtual machines that each sub-business module should request, which should meet the capacity allocation of the business system in each data center. This is based on the cloud platform service ratio of each sub-business module in the data center (DC). module dc The number of virtual machines (VMnum) required for the sub-business module on the cloud platform in the data center (DC) is determined as follows: module dc :

[0043]

[0044] in,

[0045] Therefore, the number of virtual machines applied for by each module on each data center cloud platform can be represented as follows:

[0046]

[0047] Where p represents the number of multiple sub-business modules, and q represents the number of multiple data center cloud platforms.

[0048] Optionally, based on the needs of the business system, embodiments of the present invention construct virtual machine allocation package rules with logical central processing unit (CPU), memory, and system disk as allocation indicators.

[0049] According to the virtual machine allocation package rules, the virtual machine allocation package rules template corresponding to the sub-business module is as follows: module for:

[0050] template module ={cpu module mem moduledisk module}

[0051] Among them, CPU module Mem represents the CPU configuration size corresponding to the sub-business module. module This indicates the memory configuration amount (disk) corresponding to the sub-business module. module This represents the disk configuration quantity corresponding to the sub-business module. The matrix representation of the number of virtual machines, CPU configuration quantity, memory configuration quantity, and disk configuration quantity required by the corresponding multiple sub-business modules in each of the above data center cloud platforms is as follows.

[0052]

[0053]

[0054]

[0055] The total number of virtual machines required for each sub-business module in each data center cloud platform can be obtained through standard packages and the required number of virtual machines.

[0056] Based on the CPU configuration rules for sub-service modules in each data center cloud platform (DC) according to the virtual machine allocation package rules, the CPU configuration for expansion applications of sub-service modules in each data center cloud platform DC is as follows:

[0057] CPU module dc =cpu module ×VMnum module dc

[0058] The matrix representation of the total number of central processing units required by the corresponding multiple sub-business modules in each of the multiple data center cloud platforms is as follows.

[0059]

[0060] Based on the memory configuration rules for sub-service modules in each data center cloud platform (DC) according to the virtual machine allocation package rules, the memory configuration for expansion requests of sub-service modules in each data center cloud platform DC is as follows:

[0061] MEM module dc =mem module ×VMnum module dc

[0062] The matrix representation of the total memory required by the corresponding multiple sub-business modules in each of the multiple data center cloud platforms is as follows.

[0063]

[0064] Based on the disk configuration rules of the sub-service module in each data center cloud platform (DC) according to the virtual machine allocation package rules, the disk configuration for the expansion application of the sub-service module in each data center cloud platform DC is as follows:

[0065] DISK module dc =disk module ×VMnum module dc

[0066] The matrix representation of the total disk space required by the corresponding multiple sub-business modules in each of the multiple data center cloud platforms is as follows.

[0067]

[0068] The total resource configuration required by each sub-business module in each data center is as follows.

[0069]

[0070] The total resource configuration required for each sub-business module in the data center (DC) is as follows.

[0071]

[0072] Step S106: Based on the total resource configuration required by the above-mentioned multiple sub-business modules in each of the above-mentioned data center cloud platforms, and the business node types corresponding to the above-mentioned multiple sub-business modules, determine the target number of physical machines required by the above-mentioned multiple sub-business modules in each of the above-mentioned data center cloud platforms when performing the above-mentioned expansion processing on the above-mentioned multiple sub-business modules.

[0073] By using the above methods, based on the business node types (such as compute nodes, storage nodes, and management nodes) corresponding to multiple sub-business modules, the corresponding number of physical machines for each sub-business module is determined (i.e., different business node types correspond to different physical machine allocation packages), making the expansion planning of each sub-business module more accurate and better meeting the expansion needs of each sub-business module.

[0074] In an optional embodiment, where the plurality of sub-business modules include a first sub-business module whose business node type is a compute node, a second sub-business module whose business node type is a storage node, a third sub-business module whose business node type is a control node, and a fourth sub-business module whose business node type is other nodes, determining the target number of physical machines required by the plurality of sub-business modules in each of the data center cloud platforms when performing the aforementioned expansion processing includes: determining the number of first physical machines required by the first sub-business module in each of the data center cloud platforms when performing the aforementioned expansion processing; determining the number of second physical machines required by the second sub-business module in each of the data center cloud platforms when performing the aforementioned expansion processing; and determining the number of target physical machines required by the aforementioned sub-business modules in each of the data center cloud platforms. When the third sub-business module performs the above-mentioned expansion process, the number of third physical machines required by the third sub-business module in each of the above-mentioned data center cloud platforms is determined; when the fourth sub-business module performs the above-mentioned expansion process, the number of fourth physical machines required by the fourth sub-business module in each of the above-mentioned data center cloud platforms is determined; based on the number of first physical machines required by the first sub-business module in each of the above-mentioned data center cloud platforms, the number of second physical machines required by the second sub-business module in each of the above-mentioned data center cloud platforms, the number of third physical machines required by the third sub-business module in each of the above-mentioned data center cloud platforms, the number of third physical machines, the number of first physical machines, and the number of fourth physical machines required by the fourth sub-business module in each of the above-mentioned data center cloud platforms, the target number of physical machines required by the above-mentioned multiple sub-business modules in each of the above-mentioned data center cloud platforms is obtained.

[0075] Optionally, the physical machine allocation type can be matched according to the business node type. Based on the overall cloud platform architecture, product characteristics, and product requirements, the physical machine allocation type corresponding to each business node type on the cloud platform is determined. That is, different business node types correspond to different physical machine allocation methods, and also to different material resource allocation methods. This embodiment of the invention constructs a compute node using a compute server package (pack). com The storage nodes use a storage server package. block The control nodes use a general-purpose server package. ctl The physical resource package collection (PACK) for the data center cloud platform is as follows.

[0076] PACK = {pack com ,pack block ,pack ctl pack etc}

[0077] For example, based on the needs of the business system, packages can be constructed using virtual machine configuration, CPU configuration, memory configuration, and disk configuration as allocation indicators. Nodes of type `type` select a physical server package of type `pack`. type The available configurations are as follows:

[0078] pack type ={virtual type CPU type mem type disk type}

[0079] By using the above method, based on the business node types (such as compute nodes, storage nodes, management nodes, and other nodes) corresponding to multiple sub-business modules, the corresponding number of physical machines for each sub-business module is determined (i.e., different business node types correspond to different physical machine allocation packages). Then, the calculated numbers of physical machines for each of the multiple sub-business modules are summed to obtain the final target number of physical machines required by each of the aforementioned sub-business modules in each data center cloud platform. The resulting expansion plans for each sub-business module are more accurate and better meet the expansion needs of each sub-business module.

[0080] In an optional embodiment, when determining the first sub-service module's required number of first physical machines on each data center cloud platform during the aforementioned capacity expansion process, the determination includes: determining the fourth physical machine number required by the first sub-service module on each data center cloud platform based on the total number of virtual machines required by the first sub-service module on each data center cloud platform and the virtual machine ratio corresponding to each data center cloud platform; determining the fifth physical machine number required by the first sub-service module on each data center cloud platform based on the total number of CPUs required by the first sub-service module on each data center cloud platform and the number of available CPU threads on each data center cloud platform; determining the sixth physical machine number required by the first sub-service module on each data center cloud platform based on the total memory required by the first sub-service module on each data center cloud platform and the memory level and memory capacity corresponding to each data center cloud platform; and taking the maximum value among the fourth, fifth, and sixth physical machine numbers required by the first sub-service module on each data center cloud platform as the first physical machine number required by the first sub-service module on each data center cloud platform.

[0081] Optionally, based on the determination of the number of physical machines for the aforementioned computing nodes and the number of virtual machines required for the expansion of the computing business system, the total resource configuration required for each first sub-business module of the data center cloud platform (dc) is as follows:

[0082] Demand dc =[VMnum ALL dc CPU ALL dc MEM ALL dc DISK ALL dc ]

[0083] The required computing node resources are calculated from different dimensions based on various configuration metrics (i.e., total number of virtual machines, total number of CPUs, and total memory). The number of physical machines required for computing nodes is calculated separately for the total number of virtual machines, total number of CPUs, and total memory.

[0084] The number of physical machines is calculated based on the total number of virtual machines. The maximum number of virtual machines each server can provide, i.e., the server's virtualization ratio, is [virtualization ratio]. com Based on the total number of virtual machines (VMnum) required by each first sub-business module in the data center (DC). ALL dc Compared to virtual com Therefore, the number of fourth physical machines required in the data center (DC) is:

[0085] The number of physical machines is calculated based on the total number of central processing units (CPUs). The cloud platform not only provides computing resources for business systems but also needs to provide CPU and memory resources for its own virtualization software, data acquisition and control systems, etc. Excluding its own resource consumption, the cloud platform's computing nodes can provide resources (β) to the business systems, with the available CPU threads being [cputhreads]. com =β×cpu com ×thread com ×core. Based on the total number of virtual machine CPUs required by each first sub-business module in the data center cloud platform (DC). ALL dc and the number of available CPU cores (cputhread) type Therefore, the number of fifth physical machines required for the data center cloud platform (DC) is:

[0086] The number of physical machines was calculated based on the total memory. Considering its own resource consumption, the available memory capacity is [memory]. com =μmem com Considering the resilience and disaster recovery capabilities of the business system, memory redundancy is preset, with a memory waterline of level [level number missing]. memBased on the total virtual machine memory (MEM) required by each first sub-business module in the data center (DC). ALL dc Available memory capacity is memory com and memory water level mem Therefore, the number of sixth physical machines required in the data center (DC) is:

[0087] Based on the above calculation method, and by comprehensively comparing the number of physical machines required for computing nodes calculated according to the total number of virtual machines, total number of central processing units, and total memory, the maximum number of physical machines, BMnum, is selected. compute dc The first number of physical machines (BMnum) required for computing nodes in the data center cloud platform (dc) compute dc .

[0088] BMnum compute dc =max(bmnum) vm dc ,bmnum cpu dc ,bmnum mem dc )

[0089] In one optional embodiment, when determining the number of second physical machines required by the second sub-service module on each of the data center cloud platforms during the aforementioned expansion process, the steps include: obtaining the memory water level corresponding to each data center cloud platform and the preset number of storage replicas; and determining the number of second physical machines required by the second sub-service module on each of the data center cloud platforms based on the total disk volume required by the second sub-service module on each of the data center cloud platforms, the memory water level corresponding to each of the data center cloud platforms, and the preset number of storage replicas.

[0090] Based on the number of virtual machines required for the expansion of the computing business system, the total resource configuration required for each second sub-business module in the data center (DC) is as follows:

[0091] Demand n =[VMnum ALL dc CPU ALL dc MEM ALL dc DISK ALL dc ]

[0092] Considering the cloud platform's multi-replica backup mechanism, the number of storage replicas is set to ∈. Considering the elasticity and disaster recovery capabilities of the business system, storage redundancy is preset, and the storage waterline is level. disk Based on the total number of virtual machine disks required by each second sub-business module in the data center (DC). ALL dc Storage replica count and disk waterline level diskBased on storage requirements, the number of second physical machines (BMnum) needed in the data center (DC) can be calculated. block :

[0093]

[0094] Therefore, the block storage resource pool requires BMnum. block dc Taiwan physical machine.

[0095] In an optional embodiment, when determining the number of third physical machines required by the third sub-business module in each of the data center cloud platforms during the aforementioned expansion process, the process includes: determining whether there are any new business requirements in the third sub-business module; and if there are new business requirements in the third sub-business module, and the number of new services corresponding to the new business requirements is greater than the preset upper limit of the number of control nodes corresponding to the control node, determining the number of third physical machines required by the third sub-business module in each of the data center cloud platforms based on the number of new services.

[0096] Optionally, regarding the capacity of the planning and control nodes, the cloud platform's own management capabilities are primarily undertaken by control nodes. These nodes support various cloud platform infrastructure services, cloud management platforms, virtual private network services, load balancing services, SDN services, and other products. The resource usage of control nodes can be rationally configured according to the cloud platform plan. The number of physical nodes to be added to the control nodes should consider the following factors: whether there is a new product requirement. For new product requirements, new control nodes are needed; whether the product expansion quantity is still below the minimum control node quantity. For products that do not meet the control node expansion standard, the control node quantity remains at the minimum and no expansion is needed; whether the product expansion quantity is above the maximum control node quantity. For products exceeding the control node expansion capacity, corresponding clusters need to be added. Considering the above factors, in the data center cloud platform (dc) expansion plan, the required number of physical machines (BMnum) should be applied for based on the cloud product application. product dc The number of third physical machines (bmnum) required for the control node on the data center cloud platform (dc) can be determined. product ctl dc .

[0097] h product (BMnum product dc →bmnum product ctl dc

[0098] Therefore, the physical machine requirement for the management node is BMnum. ctldc .

[0099]

[0100] Optionally, if the data center cloud platform (DC) still includes other cloud products, i.e., the fourth sub-business modules corresponding to other nodes, the number of fourth physical machines (BMnum) required by other nodes in the data center cloud platform (DC) will be determined based on the characteristics of the cloud platform products and the actual situation. etc dc .

[0101]

[0102] Optionally, by summing the number of various nodes, the total number of physical machines required for the data center cloud platform (DC) can be obtained as BMnum. ALLdc .

[0103] BMnum ALLdc =BMnum compute dc +BMnum block dc +BMnum ctl dc +BMnum etc dc

[0104] In an optional embodiment, after determining the target number of physical machines required by the multiple sub-business modules in each data center cloud platform based on the total resource configuration required by the multiple sub-business modules in each data center cloud platform and the business node type corresponding to each of the multiple sub-business modules, the method further includes: performing the expansion process on the multiple sub-business modules based on the target number of physical machines required by the multiple sub-business modules in each data center cloud platform to obtain expanded multiple sub-business modules; obtaining first stress test data corresponding to the expanded multiple sub-business modules; and determining the new business performance corresponding to the expanded multiple sub-business modules based on the first stress test data.

[0105] Optionally, after the cloud platform resource expansion is completed and the target number of physical machines required for each of the aforementioned sub-business modules in each data center cloud platform is determined, load testing needs to be performed on the expanded business system. A new round of load testing data is obtained, and the business performance criteria for the smallest application unit corresponding to each of the sub-business modules is updated based on the latest load testing data. module This will provide a more accurate and valuable basis for subsequent expansion plans.

[0106] In an optional embodiment, after determining the target number of physical machines required by the multiple sub-business modules in each data center cloud platform based on the total resource configuration required by the multiple sub-business modules in each data center cloud platform and the business node type corresponding to each of the multiple sub-business modules, the method further includes: determining a preset evaluation coefficient; and determining a seventh number of physical machines required by the multiple sub-business modules in each data center cloud platform based on the target number of physical machines required by the multiple sub-business modules in each data center cloud platform and the preset evaluation coefficient.

[0107] Using the above methods and based on the evaluation coefficient θ, the target number of physical machines calculated for the data center cloud platform (DC) is adjusted. This ensures that the number of physical machines required for the expansion of the data center cloud platform (DC) has a certain margin, better able to cope with various unforeseen circumstances. The physical machine requirement BMnum for each type of node in the data center cloud platform (DC) is as follows: ′ typedc :

[0108] ′

[0109] BMnum typedc =θBMnum typedc

[0110] Total physical machine requirement (BMNUM) for data center cloud platform (dc) dc BMNUM dc =θBMnum ALLdc Total physical machine requirement (BMNUM) for each data center's cloud platform. ALL As shown in the following formula.

[0111] BMNUM ALL =∑ dc θBMnum ALLdc =θ∑ dc BMnum ALLdc

[0112] Through the above steps S102 to S106, the goal of accurately planning the expansion of cloud platform resources can be achieved, thereby improving the timeliness and accuracy of cloud platform resource expansion planning, and solving the technical problems of low expansion efficiency and poor accuracy in cloud platform resource expansion methods in related technologies.

[0113] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method. Figure 2 This is a schematic diagram of an optional cloud platform resource expansion system according to an embodiment of the present invention. Figure 3 This is a flowchart of an optional cloud platform resource expansion method according to an embodiment of the present invention, such as... Figure 2 As shown, the system includes: a standardization module, a business requirements collection module, a resource requirements planning module, a resource planning evaluation module, a resource status acquisition module, and a system parameter optimization module.

[0114] The aforementioned standardized module is used for the standardization and normalization of business system logic, data center service allocation, service performance coefficients, virtual machine allocation packages, and physical machine provision packages within the system. Before performing expansion planning calculations, the relevant data such as business system logic, data center service allocation, virtual machine allocation packages, and physical machine provision packages need to be initialized in the standardized module.

[0115] The aforementioned business requirement collection module is responsible for collecting the total business requirements for the expansion of the data center cloud platform and formatting these requirements. Based on the relevance of the sub-business modules to the overall business system's performance, the actual business requirements of each sub-business module are determined.

[0116] The aforementioned resource requirement planning module, used in the expansion planning system, is responsible for constructing the requirement transformation model and the expansion planning model. Through calculations using these models, business requirements are transformed into physical resource requirements for the cloud platform. Specifically, the requirement transformation model achieves steps 1)-4), and the expansion planning model achieves step 4). Specifically: 1) Calculate the number of virtual machines required for the expansion of each sub-business module; 2) Calculate the total number of virtual machines required for the overall expansion of the business system (i.e., the number of virtual machines required by each sub-business module on the cloud platform in each data center); 3) Calculate the number of physical machines required for the expansion of various nodes on the cloud platform; 4) Calculate the total number of physical machines required for the overall expansion of the business system's cloud platform (i.e., the target number of physical machines required by each sub-business module on the cloud platform in each data center). This resource requirement planning module provides a multi-dimensional view of the expansion details and overall picture. Presented from the perspective of data center cloud platforms, this resource demand planning module can calculate the total number of physical machines required for the expansion of each data center cloud platform; presented from the perspective of nodes, the total number of physical machines required for the expansion of each node of each cloud platform; presented from the perspective of modules, the total number of physical machines required for the expansion of each sub-business module across all data center cloud platforms; and presented from the perspective of a combination of modules and data centers, the number of physical machines required for the expansion of each sub-business module in each data center, etc.

[0117] The resource planning and evaluation module described above is used to adjust the initial expansion plan. During the provision of basic resources, the data center cloud platform may experience failures or losses. However, due to its inherent high availability mechanism, this usually does not affect the normal operation of services. To ensure the continuous and reliable operation of the data center cloud platform, timely repair of faulty machines is necessary; therefore, backup machines and spare parts need to be considered and prepared in advance. Simultaneously, considering the overall planning of the data center cloud platform and its future elasticity, equipment redundancy needs to be provisioned in advance to facilitate rapid adjustments. The resource planning module sets evaluation coefficients to adjust the expansion quantity, obtaining the final expansion plan.

[0118] The aforementioned resource status acquisition module is used to collect information on the usage and status of virtual and physical resources in each sub-business module of the data center cloud platform, and provides this data to the system parameter optimization module.

[0119] The aforementioned system parameter optimization module is used to optimize the service performance parameters carried by the smallest application unit. The resource status acquisition module obtains the actual resource usage status and feeds it back to the system parameter optimization module for subsequent scheme planning and tuning.

[0120] Figure 3 As shown, the method includes: Step S1, initializing and standardizing the business system modules, data center business allocation, business performance coefficients (i.e., business performance correlation), virtual machine allocation packages, and physical machine provision packages through the standardization module, establishing baselines and corresponding standards; Step S2, obtaining the total business requirements for the expansion of the business system through the business requirement collection module, and sending the actual business requirements of each sub-business module to the resource requirement planning module, which will serve as the basis for expansion planning; Step S3, converting the business requirements into technical requirements through the resource requirement planning module, calculating the number of virtual machines required for the expansion of the business system, and then converting it into the number of physical machines required for the cloud platform; Step S4, adjusting the number of physical machines required for the expansion of the cloud platform through the resource planning evaluation module according to the overall business plan; Step S5, after the expansion construction is completed, collecting the performance indicators of each physical machine during the stress test through the resource status collection module, and updating and optimizing the service capacity of the minimum application unit of the sub-business module through the system parameter optimization module based on the latest stress test indicators.

[0121] It should be noted that this invention adheres to the principle of "overall planning and collaborative construction," realizing the transmission between top-level design and specific planning, and the transformation of business requirements into technical solutions. Utilizing this method and system, through key steps such as standardized business system modules, data center service allocation, business performance coefficients, virtual machine allocation packages, and physical machine provision packages, standardized output of expansion planning schemes is achieved, improving the timeliness and accuracy of expansion planning. By constructing a requirement transformation model and an expansion planning model, the automatic transformation from business capability requirements to virtual resource requirements, and then to physical resource requirements is realized. The system's one-stop requirement transformation greatly improves the efficiency of planning. By controlling the overall expansion planning and establishing a feedback mechanism between the expansion implementation plan and system parameters, the calculated physical resource quantity is adjusted overall to obtain the final planning scheme, making the expansion planning more aligned with actual technical solutions, improving planning accuracy, and reducing implementation difficulty. This invention makes the expansion planning process more standardized, efficient, and comprehensive. The expansion planning scheme obtained by this system provides a reasonable basis for cloud platform construction, better matches the actual business needs and actual operation requirements, and balances the overall construction cost and resource utilization ratio of the cloud platform in the best state.

[0122] The embodiments of the present invention can achieve at least the following technical effects: taking business needs as the starting point, it realizes a one-stop solution from business needs to capacity expansion planning; it constructs standardized processes and rules, making capacity expansion planning standardized, scientific and rational; it builds a demand transformation model and a capacity expansion planning model, realizing loose coupling of components in the capacity expansion planning system; the capacity expansion planning system performs real-time calculations at high speed, without waiting for manual calculations or relying on experience; and it realizes global measurement and multi-dimensional presentation of details of the capacity expansion planning scheme.

[0123] This embodiment also provides a cloud platform resource expansion device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementations, or a combination of software and hardware, are also possible and contemplated.

[0124] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described cloud platform resource expansion method is also provided. Figure 4 This is a schematic diagram of the structure of a cloud platform resource expansion device according to an embodiment of the present invention, as shown below. Figure 4 As shown, the aforementioned cloud platform resource expansion device includes: an acquisition module 400, a first determination module 402, and a second determination module 404, wherein:

[0125] The aforementioned acquisition module 400 is used to acquire the actual business requirements corresponding to multiple sub-business modules in the business system. These multiple sub-business modules are obtained by dividing the businesses included in the business system according to project type.

[0126] The first determining module 402 is connected to the obtaining module 400 and is used to determine the total amount of resource configuration required by the multiple sub-business modules in each of the multiple data center cloud platforms when the multiple sub-business modules are expanded, based on the actual business needs corresponding to the multiple sub-business modules respectively.

[0127] The second determining module 404, connected to the first determining module 402, is used to determine the target number of physical machines required by the multiple sub-business modules in each of the data center cloud platforms when performing the expansion process, based on the total resource configuration required by the multiple sub-business modules in each of the data center cloud platforms and the business node types corresponding to the multiple sub-business modules.

[0128] In this embodiment of the invention, by setting up the acquisition module 400, the actual business requirements corresponding to multiple sub-business modules in the business system are acquired. These multiple sub-business modules are obtained by dividing the businesses included in the business system according to project type. The first determining module 402, connected to the acquisition module 400, is used to determine, based on the actual business requirements corresponding to the multiple sub-business modules, the total resource configuration required by each of the multiple sub-business modules in each of the multiple data center cloud platforms when performing the expansion process. The second determining module 404, connected to the first determining module 402, is used to determine, based on the total resource configuration required by the multiple sub-business modules in each of the multiple data center cloud platforms and the business node type corresponding to each of the multiple sub-business modules, the target number of physical machines required by the multiple sub-business modules in each of the multiple data center cloud platforms when performing the expansion process. This achieves the goal of accurately planning cloud platform resource expansion, thereby improving the timeliness and accuracy of cloud platform resource expansion planning and solving the technical problems of low expansion efficiency and poor accuracy in related technologies for cloud platform resource expansion methods.

[0129] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0130] It should be noted that the aforementioned acquisition module 400, first determination module 402, and second determination module 404 correspond to steps S102 to S106 in the embodiments. The instances and application scenarios implemented by the above modules and their corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run in a computer terminal.

[0131] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0132] The aforementioned cloud platform resource expansion device may also include a processor and a memory. The aforementioned acquisition module 400, first determination module 402, second determination module 404, etc., are all stored in the memory as program modules, and the processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0133] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0134] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device where the non-volatile storage medium is located to execute any of the cloud platform resource expansion methods.

[0135] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0136] Optionally, during program execution, the device containing the non-volatile storage medium may be controlled to perform the following functions: Obtain the actual business requirements corresponding to multiple sub-business modules in the business system, wherein the multiple sub-business modules are obtained by dividing the businesses included in the business system according to project type; based on the actual business requirements corresponding to the multiple sub-business modules, determine the total resource configuration required by the multiple sub-business modules in each of the multiple data center cloud platforms when performing the expansion process; and based on the total resource configuration required by the multiple sub-business modules in each of the multiple data center cloud platforms and the business node types corresponding to the multiple sub-business modules, determine the target number of physical machines required by the multiple sub-business modules in each of the multiple data center cloud platforms when performing the expansion process.

[0137] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the cloud platform resource expansion methods described above.

[0138] According to an embodiment of this application, an embodiment of a computer program product is also provided, which, when executed on a data processing device, is adapted to execute a program that initializes the expansion method steps of cloud platform resources having any of the above-described steps.

[0139] Optionally, when the aforementioned computer program product is executed on a data processing device, it is suitable to execute an initialization program with the following method steps: obtaining the actual business requirements corresponding to multiple sub-business modules in the business system, wherein the multiple sub-business modules are obtained by dividing the businesses included in the business system according to project types; based on the actual business requirements corresponding to the multiple sub-business modules, determining the total resource configuration required by the multiple sub-business modules in each of the multiple data center cloud platforms when performing the expansion processing; and based on the total resource configuration required by the multiple sub-business modules in each of the multiple data center cloud platforms and the business node types corresponding to the multiple sub-business modules, determining the target number of physical machines required by the multiple sub-business modules in each of the multiple data center cloud platforms when performing the expansion processing.

[0140] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: obtaining the actual business requirements corresponding to multiple sub-business modules in a business system, wherein the multiple sub-business modules are obtained by dividing the businesses included in the business system according to project type; based on the actual business requirements corresponding to the multiple sub-business modules, determining the total resource configuration required by the multiple sub-business modules in each of the multiple data center cloud platforms when performing the expansion process; and determining the target number of physical machines required by the multiple sub-business modules in each data center cloud platform when performing the expansion process, based on the total resource configuration required by the multiple sub-business modules in each data center cloud platform and the business node type corresponding to each of the multiple sub-business modules.

[0141] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0142] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0144] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0145] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0146] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0147] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for expanding cloud platform resources, characterized in that, include: The actual business requirements corresponding to multiple sub-business modules in the business system are obtained. The multiple sub-business modules are obtained by dividing the business included in the business system according to project type. The actual business requirements are calculated based on the total business requirements corresponding to the business system and the business performance correlation between the multiple sub-business modules and the total business requirements. Based on the actual business needs corresponding to the multiple sub-business modules, the total resource configuration required by the multiple sub-business modules in each of the multiple data center cloud platforms is determined when the multiple sub-business modules are expanded. The total resource configuration includes the sum of the total number of virtual machines, the total number of central processing units, the total amount of memory, and the total amount of disk space. Based on the total resource configuration required by the multiple sub-business modules in each data center cloud platform, and the business node type corresponding to each of the multiple sub-business modules, determine the target number of physical machines required by the multiple sub-business modules in each data center cloud platform when performing the expansion process; Where the total resource configuration includes the total number of virtual machines, total number of CPUs, total number of memory, and total number of disks, the determination of the total resource configuration required by the multiple sub-business modules in each of the multiple data center cloud platforms when expanding the capacity of the multiple sub-business modules, based on the actual business needs corresponding to each of the multiple sub-business modules, includes: Based on the actual business needs corresponding to the multiple sub-business modules, determine the number of virtual machines required by each of the multiple sub-business modules on each data center cloud platform when performing the expansion process. Determine the preset virtual machine allocation package rules corresponding to the plurality of sub-business modules, wherein the preset virtual machine allocation package rules are used to indicate the CPU, memory and disk configuration of the plurality of sub-business modules in a virtual machine of each data center cloud platform when the expansion process is performed on the plurality of sub-business modules; Based on the number of virtual machines required by the multiple sub-business modules in each data center cloud platform, and the preset virtual machine allocation package rules, the CPU configuration, memory configuration, and disk configuration required by the multiple sub-business modules in each data center cloud platform are determined when the expansion process is performed on the multiple sub-business modules. Based on the number of virtual machines, CPU configuration, memory configuration, and disk configuration required by the multiple sub-business modules in each data center cloud platform, the total number of virtual machines, CPUs, memory, and disks required by the multiple sub-business modules in each of the multiple data center cloud platforms are determined. Wherein, determining the number of virtual machines required by each of the multiple sub-business modules on each data center cloud platform when performing the capacity expansion process based on the actual business needs corresponding to the multiple sub-business modules includes: The service performance of the minimum application unit corresponding to each of the plurality of sub-service modules is determined, wherein the minimum application unit is used to indicate the minimum number of virtual machines applied for each of the plurality of sub-service modules; Based on the actual business requirements corresponding to each of the multiple sub-business modules, and the business performance of the minimum application unit corresponding to each of the multiple sub-business modules, the number of minimum application units corresponding to each of the multiple sub-business modules is determined. Determine the business proportion allocated to each of the multiple sub-business modules on each data center cloud platform; Based on the number of minimum application units corresponding to the multiple sub-business modules and the business proportion allocated to each data center cloud platform for each of the multiple sub-business modules, the number of virtual machines required by each of the multiple sub-business modules in each data center cloud platform is determined.

2. The method according to claim 1, characterized in that, The plurality of sub-business modules include a first sub-business module whose business node type is a compute node, a second sub-business module whose business node type is a storage node, a third sub-business module whose business node type is a management node, and a fourth sub-business module whose business node type is other types of nodes. When determining the number of target physical machines required for each of the plurality of sub-business modules to perform the expansion process, the number of physical machines required for each sub-business module on each data center cloud platform includes: When performing the expansion process on the first sub-service module, determine the number of first physical machines required by the first sub-service module in each data center cloud platform. When performing the expansion process on the second sub-business module, determine the number of second physical machines required by the second sub-business module in each data center cloud platform; When performing the expansion process on the third sub-business module, determine the number of third physical machines required by the third sub-business module in each data center cloud platform; When performing the expansion process on the fourth sub-business module, determine the number of fourth physical machines required by the fourth sub-business module in each data center cloud platform; Based on the first number of physical machines required by the first sub-business module in each data center cloud platform, the second number of physical machines required by the second sub-business module in each data center cloud platform, the third number of physical machines required by the third sub-business module in each data center cloud platform, the third number of physical machines and the first number of physical machines, and the fourth number of physical machines required by the fourth sub-business module in each data center cloud platform, the target number of physical machines required by the plurality of sub-business modules in each data center cloud platform is obtained.

3. The method according to claim 2, characterized in that, When determining the number of physical machines required by the first sub-service module for each data center cloud platform during the expansion process, the number of physical machines required by the first sub-service module includes: Based on the total number of virtual machines required by the first sub-business module in each data center cloud platform, and the virtual machine ratio corresponding to each data center cloud platform, determine the number of fourth physical machines required by the first sub-business module in each data center cloud platform. Based on the total number of central processing units required by the first sub-business module in each data center cloud platform, and the number of available central processing unit thread cores corresponding to each data center cloud platform, determine the number of fifth physical machines required by the first sub-business module in each data center cloud platform. Based on the total amount of memory required by the first sub-business module in each data center cloud platform, and the memory water level and memory capacity corresponding to each data center cloud platform, determine the number of sixth physical machines required by the first sub-business module in each data center cloud platform. The maximum value among the number of fourth, fifth, and sixth physical machines required by the first sub-service module in each data center cloud platform shall be taken as the first physical machine number required by the first sub-service module in each data center cloud platform.

4. The method according to claim 3, characterized in that, When determining the number of second physical machines required by the second sub-service module for each data center cloud platform during the expansion process, the number of second physical machines required by the second sub-service module includes: Obtain the memory water level corresponding to each data center cloud platform, as well as the preset number of storage replicas; Based on the total disk volume required by the second sub-business module in each data center cloud platform, the memory level corresponding to each data center cloud platform, and the preset number of storage replicas, the number of second physical machines required by the second sub-business module in each data center cloud platform is determined.

5. The method according to claim 2, characterized in that, When determining the number of third physical machines required by the third sub-service module for each data center cloud platform during the expansion process, the number of third physical machines required by the third sub-service module includes: Determine whether there are any new business requirements in the third sub-business module; If the new business requirement exists in the third sub-business module, and the number of new businesses corresponding to the new business requirement is greater than the preset upper limit of the number of control nodes corresponding to the control node, the number of third physical machines required by the third sub-business module in each data center cloud platform is determined based on the number of new businesses.

6. The method according to claim 1, characterized in that, When determining the target number of physical machines required by the multiple sub-business modules on each data center cloud platform based on the total resource configuration required by the multiple sub-business modules on each data center cloud platform and the business node type corresponding to each of the multiple sub-business modules, the method further includes, after determining the target number of physical machines required by the multiple sub-business modules on each data center cloud platform: Based on the target number of physical machines required by the multiple sub-business modules in each data center cloud platform, the expansion process is performed on the multiple sub-business modules to obtain the expanded multiple sub-business modules. Obtain the first stress test data corresponding to the multiple sub-service modules after the expansion; Based on the first stress test data, the new service performance corresponding to the multiple sub-service modules after the expansion is determined.

7. The method according to any one of claims 1 to 6, characterized in that, When determining the target number of physical machines required by the multiple sub-business modules on each data center cloud platform based on the total resource configuration required by the multiple sub-business modules on each data center cloud platform, and the business node type corresponding to each of the multiple sub-business modules, the method further includes, after determining the target number of physical machines required by the multiple sub-business modules on each data center cloud platform: Determine the preset evaluation coefficients; Based on the target number of physical machines required by the multiple sub-business modules in each data center cloud platform, and the preset evaluation coefficient, the number of seventh physical machines required by the multiple sub-business modules in each data center cloud platform is determined.

8. A cloud platform resource expansion device, characterized in that, include: The acquisition module is used to acquire the actual business requirements corresponding to multiple sub-business modules in the business system. The multiple sub-business modules are obtained by dividing the businesses included in the business system according to project type. The actual business requirements are calculated based on the total business requirements corresponding to the business system and the business performance correlation between the multiple sub-business modules and the total business requirements. The first determining module is used to determine, based on the actual business needs corresponding to the multiple sub-business modules, the total amount of resource configuration required by the multiple sub-business modules in each of the multiple data center cloud platforms when expanding the capacity of the multiple sub-business modules, wherein the total amount of resource configuration includes the sum of the total number of virtual machines, the total number of central processing units, the total amount of memory, and the total amount of disk space. The second determining module is used to determine the target number of physical machines required by the multiple sub-business modules in each data center cloud platform when performing the expansion process, based on the total resource configuration required by the multiple sub-business modules in each data center cloud platform and the business node type corresponding to the multiple sub-business modules respectively. The first determining module is further configured to: determine, based on the actual business needs corresponding to the plurality of sub-business modules, the number of virtual machines required by each of the plurality of sub-business modules on each data center cloud platform when performing the expansion process; determine a preset virtual machine allocation package rule corresponding to the plurality of sub-business modules, wherein the preset virtual machine allocation package rule is used to indicate the CPU, memory, and disk configuration of each of the plurality of sub-business modules in a virtual machine on each data center cloud platform when performing the expansion process; determine, based on the number of virtual machines required by the plurality of sub-business modules on each data center cloud platform and the preset virtual machine allocation package rule, the CPU configuration, memory configuration, and disk configuration required by each of the plurality of sub-business modules on each data center cloud platform when performing the expansion process; and determine, based on the number of virtual machines, CPU configuration, memory configuration, and disk configuration required by the plurality of sub-business modules on each data center cloud platform, the total number of virtual machines, CPUs, memory, and disks required by the plurality of sub-business modules on each data center cloud platform. The first determining module is further configured to determine the service performance of the minimum application unit corresponding to each of the plurality of sub-service modules, wherein the minimum application unit is used to indicate the minimum number of virtual machines to be applied for by each of the plurality of sub-service modules; determine the number of minimum application units corresponding to each of the plurality of sub-service modules based on the actual service requirements corresponding to each of the plurality of sub-service modules and the service performance of the minimum application units corresponding to each of the plurality of sub-service modules; determine the service ratio allocated to each of the plurality of sub-service modules on each data center cloud platform; and determine the number of virtual machines required by each of the plurality of sub-service modules on each data center cloud platform based on the number of minimum application units corresponding to each of the plurality of sub-service modules and the service ratio allocated to each of the plurality of sub-service modules on each data center cloud platform.

9. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by the cloud platform resource expansion method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the cloud platform resource expansion method according to any one of claims 1 to 7.

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