A resource allocation management method and system, electronic equipment, storage medium

Abstract

The application provides a resource allocation management method and system, electronic equipment and a storage medium, and the method comprises the following steps: acquiring actual resource utilization rates of various types of resource pools of a cloud management platform; combining deployment rules of the various types of resource pools and the actual resource utilization rates, training a minimum cabinet installation model of a matching ratio between a plurality of atomic models of resource pool cabinet deployment; forming an actual machine room positioning plan according to the types of the atomic models and the matching ratio between the plurality of atomic models; according to the atomic models of the resource pool deployment and the matching ratio, the standardization degree is greatly improved, and meanwhile, the elasticity characteristics of cloud computing can be retained; the formed positioning plan can fully utilize the current machine room space, the utilization rates of various types of resources related to cloud computing are high, the matching ratios of various resources are balanced, and there is no waste of a certain resource pool; meanwhile, the minimum cabinet installation model is set, container type batch construction and installation are supported, unified construction standards and effects are maintained, and the construction and fault troubleshooting efficiency is greatly improved.

Description

Technical Field

[0001] This invention belongs to the field of financial cloud technology, and more specifically, it relates to a resource allocation and management method and system, electronic device, and storage medium. Background Technology

[0002] In the construction and implementation of financial cloud, while distributed deployment brings technological advantages such as elasticity, agility, and sharing, it also consumes data center space resources rapidly. Under the backdrop of new infrastructure development, it is necessary to resolve the contradiction between increasingly strained data center environments and the high availability design of products.

[0003] During the implementation of resource construction, it is necessary to continuously train and optimize the resource construction model to maximize resource utilization within limited data center space. After balancing elasticity and standardization, an atomic model for financial cloud construction is formed, balancing the allocation of various resources while fully utilizing the data center environment. During construction and expansion, a containerized construction model is established to maintain standardized construction plans, improve construction speed, and ensure construction quality.

[0004] The existing technical solution involves estimating the types and quantities of resources to be expanded during construction and expansion, based on the resource needs provided by tenants and the development trend of existing tenant resource requests. Then, based on the high availability requirements and design of each resource (e.g., whether it needs to be deployed across racks, in a separate rack, or mixed with other resources), and combined with the data center environment to be deployed, a comprehensive data center environment deployment plan is formed. During each implementation, a complete data center deployment plan needs to be planned based on different resources and different data center environments for implementation personnel to execute.

[0005] However, under the current technical solution, the data for resource expansion comes from tenant demand and estimates, which cannot reflect the actual resource consumption in the cloud, thus resulting in idle resources. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a resource allocation management method and system, electronic device, and storage medium, which can significantly improve the degree of standardization based on the atomic model and its allocation of resource pools, while retaining the elasticity of cloud computing.

[0007] The first aspect of this application discloses a resource allocation management method, including:

[0008] Obtain the actual resource utilization rate of various resource pools on the cloud management platform;

[0009] By combining the deployment rules of various resource pools and the actual resource utilization rate, a minimum rack installation model for the deployment of multiple resource pool racks is trained; wherein, the minimum rack installation model includes the ratio between multiple atomic models;

[0010] Based on the types of atomic models and the ratios between multiple atomic models, a practical computer room layout plan is formed.

[0011] Optionally, in the above resource allocation and management method, obtaining the actual resource utilization rate of various types of resource pools on the cloud management platform includes:

[0012] In the production operation environment, the usage capacity of computing, storage, network, database, and platform management resource pools is collected to form best practice analysis data.

[0013] Optionally, in the above resource allocation management method, before training the minimum rack installation model for deploying multiple resource pool racks by combining the deployment rules of various types of resource pools and the actual resource utilization rate, the method further includes:

[0014] Enter deployment prototypes for various types of resource pools; among them, the deployment prototypes for resource pools include the number of racks, machine types, number of servers and their limits, cross-rack deployment requirements, and whether to use a dedicated rack.

[0015] Optionally, in the above resource allocation management method, the step of training a minimum rack installation model for deploying multiple resource pool racks by combining the deployment rules of various types of resource pools and the actual resource utilization rate includes:

[0016] Calculate the capacity allocation ratio for each type of resource pool based on the actual resource utilization rate of each type of resource pool;

[0017] Based on the usage capacity allocation of each type of resource pool, combined with the racks for computing, storage, and database, as well as the number of physical machines in the resource pool and deployment rules, the allocation between atomic models is calculated;

[0018] Based on the ratio between the atomic models, the minimum rack installation model is obtained.

[0019] Optionally, in the above resource allocation management method, before forming the actual data center location plan based on the types of atomic models and the ratios between multiple atomic models, the method further includes:

[0020] Enter the information about the data center to be deployed; the information about the data center to be deployed includes: the number of data center modules and the number of server racks.

[0021] Optionally, in the above resource allocation management method, the step of forming an actual data center location plan based on the types of atomic models and the ratio between multiple atomic models includes:

[0022] Based on the types of atomic models and the ratios between multiple atomic models, as well as the amount of construction resources for this project, an overall data center layout is determined, resulting in the actual data center layout plan.

[0023] Optionally, in the above resource allocation management method, after obtaining the actual resource utilization rates of various types of resource pools on the cloud management platform, the method further includes:

[0024] The actual resource utilization rate of various resource pools on the storage cloud management platform.

[0025] The second aspect of this application discloses a resource allocation management system, comprising:

[0026] The resource utilization rate acquisition module is used to obtain the actual resource utilization rate of various types of resource pools on the cloud management platform;

[0027] The atomic model allocation calculation module is used to train atomic models for deployment in multiple resource pool racks by combining the deployment rules of various types of resource pools; and to calculate the allocation ratio between multiple atomic models based on the actual resource utilization rate.

[0028] The placement planning output module is used to generate actual computer room placement plans based on the types of atomic models and the ratios between multiple atomic models.

[0029] A third aspect of this application discloses an electronic device, comprising:

[0030] One or more processors;

[0031] A storage device on which one or more programs are stored;

[0032] When one or more programs are executed by one or more processors, the one or more processors implement the resource allocation management method as described in any one of the first aspects of this application.

[0033] The fourth aspect of this application discloses a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the resource allocation management method as described in any one of the first aspects of this application.

[0034] As can be seen from the above technical solution, the resource allocation and management method provided by the present invention includes: obtaining the actual resource utilization rate of various types of resource pools in the cloud management platform; training a minimum rack installation model for the deployment of multiple resource pool racks by combining the deployment rules and actual resource utilization rates of various types of resource pools; wherein, the minimum rack installation model includes the ratio between multiple atomic models; forming an actual data center placement plan based on the types of atomic models and the ratio between multiple atomic models; significantly improving the standardization level based on the atomic models and their ratios of resource pool deployment, while retaining the elasticity of cloud computing; in addition, the resulting placement plan can make full use of the current data center space, achieve high utilization rates of various types of cloud computing resources, balance the ratio of various resources, and avoid waste in any particular resource pool; at the same time, setting a minimum rack installation model supports containerized batch construction and installation, maintains unified construction standards and effects, and significantly improves the efficiency of construction and troubleshooting. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of a resource allocation and management method provided in an embodiment of the present invention;

[0037] Figure 2 This is a flowchart of another resource allocation and management method provided in an embodiment of the present invention;

[0038] Figure 3 This is a flowchart of another resource allocation and management method provided in an embodiment of the present invention;

[0039] Figure 4 This is a flowchart of another resource allocation and management method provided in an embodiment of the present invention;

[0040] Figure 5 This is a schematic diagram of a resource allocation and management system provided in an embodiment of the present invention;

[0041] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0044] Terminology Explanation:

[0045] Financial cloud: Based on the cloud computing model, financial institutions deploy their financial business on the cloud.

[0046] Resources: The resource pools used by the financial cloud include platform-managed resource pools, computing resource pools, storage resource pools, network resource pools, and database resource pools.

[0047] Resource allocation management platform: A unified resource allocation management platform that forms an atomic model and allocation ratio of resource pools and generates a data center placement plan.

[0048] Atomic Model: Based on best practices, the minimum model for deploying various types of resource pools, with a fixed allocation ratio for each resource in the model.

[0049] This application provides a resource allocation management method to address the problem in the prior art where resource expansion data is derived from tenant needs and estimates, which cannot reflect the actual resource consumption in the cloud, thus causing resource idleness.

[0050] See Figure 1 The resource allocation and management method includes:

[0051] S101. Obtain the actual resource utilization rate of various resource pools on the cloud management platform.

[0052] It should be noted that the cloud management platform has various resource pools, including computing resource pool, storage resource pool, platform resource pool, network resource pool, database resource pool, etc. Of course, the cloud management platform may also include other resource pools, which will not be elaborated here, depending on the actual situation, and all are within the scope of protection of this application.

[0053] In other words, this step can be to obtain the actual resource utilization rates of resource pools such as computing resource pool, storage resource pool, platform resource pool, network resource pool, and database resource pool.

[0054] It should be noted that the best practice is to collect data on the actual use of various types of resource pools in the current financial system (such as a cloud platform management system), such as the actual usage rate of various types of resource pools in the cloud platform management system.

[0055] Specifically, the acquisition method could be that a system applying this resource allocation and management method, such as a resource allocation and management system, obtains the actual resource utilization rate of various types of resource pools by calling the data supply interface of the cloud management platform.

[0056] This data supply interface provides various resource utilization data for the cloud management platform.

[0057] The cloud management platform provides information on the daily operation and resource utilization of various types of resource pools, and can provide actual resource utilization data through a data supply interface.

[0058] Of course, other methods can also be used to obtain the actual resource utilization rate of various resource pools of the cloud management platform, which will not be elaborated here. It depends on the actual situation and all are within the scope of protection of this application.

[0059] S102. Combine the deployment rules of various resource pools and the actual resource utilization rate to train the minimum rack installation model for rack deployment of multiple resource pools.

[0060] The minimum cabinet installation model includes the proportions between multiple atomic models.

[0061] Based on the actual resource utilization rate obtained in step S101, the actual allocation ratio of various resource pools can be determined.

[0062] By combining the actual allocation ratios of various resource pools with the high availability requirements of resource deployment and the models of servers and switches, an atomic model of resource deployment and the allocation ratios between each atomic model are formed, namely the minimum rack installation model.

[0063] In other words, the minimum rack installation model includes multiple atomic models that need to be deployed, as well as the ratio between each atomic model.

[0064] like Figure 5As shown, the atomic model can be: computational storage model 1, computational storage model 2, network model 1, network model 2, database model 1, database model 2, etc. The ratio between computational storage model 1, computational storage model 2, network model 1, network model 2, database model 1, and database model 2 can be N1:N2:N3:N4:N5:N6.

[0065] S103. Based on the types of atomic models and the ratio between multiple atomic models, a practical computer room layout plan is formed.

[0066] In other words, based on the minimum rack installation model as the most basic installation unit, the required number of minimum rack installation models are installed, which means installing the required number of individual atomic models.

[0067] Specifically, a minimum rack installation model includes atomic models such as compute storage model 1, compute storage model 2, network model 1, network model 2, database model 1, and database model 2. The ratio between compute storage model 1, compute storage model 2, network model 1, network model 2, database model 1, and database model 2 can be N1:N2:N3:N4:N5:N6. Among them, compute storage model 1, compute storage model 2, network model 1, network model 2, database model 1, and database model 2 are the types of atomic models; N1:N2:N3:N4:N5:N6 is the ratio between each atomic model.

[0068] Develop a practical data center layout plan, determine the installation model for N minimum server racks, and determine the value of N.

[0069] Of course, the minimum rack installation model may include other models, which will not be elaborated here. They are all within the scope of protection of this application, depending on the actual situation.

[0070] It should be noted that existing technologies, in order to fully utilize the limited data center environment, sometimes break the deployment principles of some resources during deployment, resulting in a loss of high availability, increased complexity of daily operation and maintenance, and potential risks in production operations. Secondly, each time new data center space is involved in the construction process, a new site planning is required. Furthermore, because the data center conditions and the types and quantities of resources vary, the results of each planning will differ, making it difficult to guarantee consistent construction plans and standards, requiring manpower to review data center site placement. Differences in individual understanding of deployment principles lead to a lack of standardization in construction processes, resulting in a high error rate due to strong subjective biases.

[0071] In this embodiment, the actual resource utilization rates of various resource pools on the cloud management platform are obtained. Combining the deployment rules and actual resource utilization rates of various resource pools, a minimum rack installation model for deploying multiple resource pool racks is trained. This minimum rack installation model includes the ratios between multiple atomic models. Based on the types of atomic models and the ratios between them, an actual data center placement plan is formed. By using the atomic models and their ratios for resource pool deployment, the standardization level is significantly improved while retaining the elasticity of cloud computing. Furthermore, the resulting placement plan fully utilizes the current data center space, achieves high utilization rates for various cloud computing-related resources, balances the allocation of various resources, and avoids waste in any particular resource pool. Simultaneously, the minimum rack installation model supports containerized batch construction and installation, maintains unified construction standards and effects, and significantly improves the efficiency of construction and troubleshooting.

[0072] In practical applications, see Figure 2 Step S101: Obtain the actual resource utilization rate of various resource pools on the cloud management platform, including:

[0073] S201. Collect the usage capacity of computing, storage, network, database, and platform management resource pools in the production operation environment to form best practice analysis data.

[0074] In other words, the resource allocation and management system collects the usage capacity of resource pools such as computing, storage, network, database, and platform management in the production operation environment to form best practice analysis data.

[0075] It should be noted that collecting data on the actual use of various resource pools in current financial systems (such as cloud platform management systems), such as the actual utilization rate of various resource pools in cloud platform management systems, can serve as best practice.

[0076] In practical applications, see Figure 3 Before step S102, which combines the deployment rules of various resource pools and the actual resource utilization rate to train the minimum rack installation model for deploying multiple resource pool racks, the following steps are also included:

[0077] S301. Input deployment prototypes for various types of resource pools.

[0078] The resource pool deployment prototype includes the number of racks, machine types, number and upper limit of servers, cross-rack deployment requirements, and whether to use a dedicated rack.

[0079] Specifically, in the resource allocation management system, deployment prototypes for various types of resource pools are entered, including the number of racks, machine types, number and upper limit of servers in the resource pool, cross-rack deployment requirements, and whether to use a dedicated rack.

[0080] Of course, the deployment prototypes of these various resource pools may also include other information, which will not be elaborated here, depending on the actual situation, and are all within the scope of protection of this application.

[0081] In practical applications, see Figure 4 Step S102: Combining the deployment rules of various resource pools and the actual resource utilization rate, train the minimum rack installation model for multiple resource pool rack deployments, including:

[0082] S401. Calculate the capacity allocation ratio for each type of resource pool based on the actual resource utilization rate of each type of resource pool.

[0083] Specifically, the actual resource utilization rate of various types of resource pools can be calculated as a ratio to obtain the utilization capacity allocation of each type of resource pool.

[0084] Of course, other methods can also be used to determine the usage capacity ratio of each type of resource pool, which will not be elaborated here. It depends on the actual situation and is all within the scope of protection of this application.

[0085] S402. Based on the capacity allocation of each type of resource pool, combined with the racks for computing, storage and database, as well as the number of physical machines in the resource pool and deployment rules, calculate the allocation between atomic models.

[0086] S403. Based on the ratio between the atomic models, the minimum cabinet installation model is obtained.

[0087] This minimum rack installation model includes rack placement models for various resource pools that are mixed or deployed individually, as well as the quantity ratios between various atomic models.

[0088] The process of determining the minimum rack installation model involves calculating the allocation ratio of each resource based on its usage. For example, in a financial system, the actual allocation ratio of computing, storage, and database resources is 10:2:5. Based on this actual usage ratio, combined with the number of racks and physical machines in the resource pool for computing, storage, and database, and the deployment algorithm, the allocation ratio between atomic models is calculated. The minimum rack installation model is then obtained based on this allocation ratio.

[0089] Specifically, firstly, based on actual resource utilization, the capacity allocation of various resource pools is calculated, for example, 50G storage / C. Secondly, combined with the deployment rules of the resource pools, atomic models for the deployment of multiple resource pool racks are obtained. For example, atomic model one deploys 96 compute servers and 48 storage servers in every 3 racks; atomic model two deploys 32 gateway servers in every 2 racks. Finally, based on resource usage, the allocation ratio between multiple atomic models is calculated to balance the capacity of compute, storage, network, and database resource pools.

[0090] In practical applications, before step S103, which involves forming the actual data center location plan based on the types of atomic models and the ratio between multiple atomic models, the following steps are also included: entering the information of the data center to be deployed.

[0091] The details of the data center to be deployed include: the number of data center modules and the number of server racks.

[0092] Specifically, the resource allocation administrator enters information about the data centers to be deployed into the resource allocation management system, including the number of data center modules and server racks. Based on the available resources for this construction task, the overall data center placement is determined, guiding the construction implementation.

[0093] In practical applications, step S103, based on the types of atomic models and the ratios between multiple atomic models, forms the actual computer room layout plan, including:

[0094] Based on the types of atomic models and the ratios between multiple atomic models, as well as the amount of construction resources for this project, an overall data center layout is determined, resulting in the actual data center layout plan.

[0095] Based on the types and predetermined proportions of atomic models, a detailed layout plan for the complete computer room is formed.

[0096] In other words, based on the situation of the data center to be deployed, such as the total amount of resources and data center and rack conditions, the system automatically calculates how many of each atomic model are needed and finally determines their placement.

[0097] This application identifies and optimizes the atomic model and related proportions for resource construction, forming a highly standardized data center resource allocation plan.

[0098] In practical applications, after obtaining the actual resource utilization rates of various resource pools on the cloud management platform, the following is also included:

[0099] The actual resource utilization rate of various resource pools on the storage cloud management platform.

[0100] In other words, by calling the data supply interface of the cloud management platform, the actual resource utilization rate of various types of resource pools is obtained and stored for use in subsequent steps.

[0101] It should be noted that the current resource allocation scheme involves placing servers and switches based on tenant needs and the availability of the data center. This allocation method consumes significant manpower for data center placement planning when the data center environment is strained or the project is large-scale. Furthermore, the lack of standardized placement methods affects deployment plans and subsequent maintenance, reducing implementation effectiveness.

[0102] In this embodiment, a resource management and allocation system is created to apply resource management and allocation methods. This system forms an atomic model and its allocation ratio for resource pool deployment, significantly improving standardization while retaining the elasticity of cloud computing. The placement plan formed by the resource management and allocation system can make full use of the current data center space, achieve high utilization rates of various types of cloud computing resources, balance the allocation ratio of various resources, and avoid waste in any particular resource pool. The atomic model and allocation ratio formed by the resource management and allocation system support containerized batch construction and installation, maintain unified construction standards and effects, and significantly improve the efficiency of construction and troubleshooting.

[0103] Another embodiment of this application provides a resource allocation management system.

[0104] See Figure 5 The resource allocation management system includes:

[0105] The resource utilization rate acquisition module 101 is used to obtain the actual resource utilization rate of various resource pools in the cloud management platform.

[0106] Collect resource allocation data in the production environment; specifically, it can obtain operational data of current best practices to support subsequent resource allocation analysis.

[0107] The atomic model allocation calculation module 102 is used to train atomic models for the deployment of multiple resource pool racks by combining the deployment rules of various types of resource pools; and to calculate the allocation ratio between multiple atomic models based on the actual resource utilization rate.

[0108] The atomic model allocation calculation module 102 determines the quantity allocation between atomic models. Specifically, through the analysis of existing operational data and the established deployment strategy set by the resource allocation administrator, it generates atomic models for data center deployment and provides the allocation ratio between each atomic model.

[0109] Specifically, firstly, based on the actual resource utilization rate collected by the resource utilization rate collection module 101, the capacity allocation of various types of resource pools is calculated, for example, 50G storage / C. Secondly, combined with the deployment rules of the resource pools, atomic models for the deployment of multiple resource pool racks are trained. For example, atomic model one deploys 96 compute servers and 48 storage servers in every 3 racks; atomic model two deploys 32 gateway servers in every 2 racks. Finally, based on the resource usage, the allocation ratio between multiple atomic models is calculated to balance the capacity of compute, storage, network, and database resource pools.

[0110] The placement planning output module 103 is used to generate an actual computer room placement plan based on the types of atomic models and the ratio between multiple atomic models.

[0111] The placement planning output module 103 is responsible for calculating the number of deployable atomic models for a specific construction task based on the actual situation of the data center and the number of resource pools deployed, thus forming the actual data center placement plan.

[0112] In other words, based on the best practices of financial cloud in production environments, a resource allocation management platform is built to provide resource allocation administrators with resource allocation management tools.

[0113] For details on the specific working process and principles of each of the above modules, please refer to the resource allocation and management method provided in the above embodiments. They will not be repeated here, but can be determined according to the actual situation, and are all within the protection scope of this application.

[0114] In this embodiment, the resource utilization rate acquisition module 101 acquires the actual resource utilization rates of various resource pools on the cloud management platform; the atomic model allocation calculation module 102 trains atomic models for the deployment of multiple resource pool racks based on the deployment rules of various resource pools; and calculates the allocation ratio between multiple atomic models based on the actual resource utilization rate; the placement planning output module 103 forms an actual data center placement plan based on the type of atomic model and the allocation ratio between multiple atomic models; thus, based on the atomic models and their allocation ratios of the resource pool deployment, the standardization level is greatly improved, while the elasticity of cloud computing is preserved; in addition, the resulting placement plan can make full use of the current data center space, with high utilization rates of various cloud computing-related resources, balanced allocation of various resources, and no waste of any particular resource pool; at the same time, a minimum rack installation model is set to support containerized batch construction and installation, maintain unified construction standards and effects, and greatly improve the efficiency of construction and troubleshooting.

[0115] Another embodiment of this application provides a storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, it implements a resource allocation management method as described in any of the above embodiments.

[0116] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0117] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0118] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0119] Another embodiment of the present invention provides an electronic device, such as... Figure 6 As shown, it includes:

[0120] One or more processors 201.

[0121] Storage device 202, on which one or more programs are stored.

[0122] When one or more programs are executed by one or more processors 201, the one or more processors 201 implement the resource allocation management method as described in any of the above embodiments.

[0123] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts.

[0124] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

[0125] While several specific implementation details are included in the foregoing discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0126] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0127] It should be noted that the resource allocation management method and system, electronic device, and storage medium provided by this invention can be used in the fields of artificial intelligence, blockchain, distributed systems, cloud computing, big data, the Internet of Things, mobile internet, network security, chips, virtual reality, augmented reality, holography, quantum computing, quantum communication, quantum measurement, digital twins, or finance. The above are merely examples and do not limit the application areas of the resource allocation management method and system, electronic device, and storage medium provided by this invention.

[0128] The features described in the various embodiments of this specification can be substituted for or combined with each other. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0129] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0130] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A resource allocation and management method, characterized in that, include: Obtain the actual resource utilization rate of various resource pools on the cloud management platform; By combining the deployment rules of various resource pools and the actual resource utilization rate, a minimum rack installation model for the deployment of multiple resource pool racks is trained; wherein, the minimum rack installation model includes the ratio between multiple atomic models; Based on the types of atomic models and the ratio between multiple atomic models, a practical computer room layout plan is formed; The step of training a minimum rack installation model for deploying multiple resource pool racks by combining the deployment rules of various resource pools and the actual resource utilization rate includes: Calculate the capacity allocation ratio for each type of resource pool based on the actual resource utilization rate of each type of resource pool; Based on the usage capacity allocation of each type of resource pool, combined with the racks for computing, storage, and database, as well as the number of physical machines in the resource pool and deployment rules, the allocation between atomic models is calculated; Based on the ratio between the atomic models, the minimum rack installation model is obtained.

2. The resource allocation and management method according to claim 1, characterized in that, The actual resource utilization rates of various resource pools on the cloud management platform are obtained, including: In the production operation environment, the usage capacity of computing, storage, network, database, and platform management resource pools is collected to form best practice analysis data.

3. The resource allocation and management method according to claim 1, characterized in that, Before training the minimum rack installation model for deploying multiple resource pool racks by combining the deployment rules of various resource pools and the actual resource utilization rate, the following steps are also included: Enter deployment prototypes for various types of resource pools; among them, the deployment prototypes for resource pools include the number of racks, machine types, number of servers and their limits, cross-rack deployment requirements, and whether to use a dedicated rack.

4. The resource allocation and management method according to claim 1, characterized in that, Before formulating the actual data center layout plan based on the types of atomic models and the ratios between multiple atomic models, the following steps are also included: Enter the information about the data center to be deployed; the information about the data center to be deployed includes: the number of data center modules and the number of server racks.

5. The resource allocation and management method according to claim 1, characterized in that, The actual data center layout plan is formed based on the types of atomic models and the ratios between multiple atomic models, including: Based on the types of atomic models and the ratios between multiple atomic models, as well as the amount of construction resources for this project, an overall data center layout is determined, resulting in the actual data center layout plan.

6. The resource allocation and management method according to claim 1, characterized in that, After obtaining the actual resource utilization rates of various resource pools on the cloud management platform, the following is also included: The actual resource utilization rate of various resource pools on the storage cloud management platform.

7. A resource allocation and management system, characterized in that, include: The resource utilization rate acquisition module is used to obtain the actual resource utilization rate of various types of resource pools on the cloud management platform; The atomic model allocation calculation module is used to train atomic models for deployment in multiple resource pool racks by combining the deployment rules of various types of resource pools; and to calculate the allocation ratio between multiple atomic models based on the actual resource utilization rate. The placement planning output module is used to generate an actual computer room placement plan based on the types of atomic models and the ratio between multiple atomic models. The step of training a minimum rack installation model for deploying multiple resource pool racks by combining the deployment rules of various resource pools and the actual resource utilization rate includes: Calculate the capacity allocation ratio for each type of resource pool based on the actual resource utilization rate of each type of resource pool; Based on the usage capacity allocation of each type of resource pool, combined with the racks for computing, storage, and database, as well as the number of physical machines in the resource pool and deployment rules, the allocation between atomic models is calculated; Based on the ratio between the atomic models, the minimum rack installation model is obtained.

8. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When one or more programs are executed by one or more processors, the one or more processors implement the resource allocation management method as described in any one of claims 1-6.

9. A storage medium, characterized in that, It stores a computer program, wherein the computer program, when executed by a processor, implements the resource allocation management method as described in any one of claims 1-6.

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