CN115460082A - Cloud cost optimization method and system based on government affair cloud scene - Google Patents
Cloud cost optimization method and system based on government affair cloud scene Download PDFInfo
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Abstract
The invention relates to the technical field of cloud computing, in particular to a cloud cost optimization method based on a government affair cloud scene, which comprises the following steps of realizing management and configuration of cost budget and resource amount based on an organization mechanism and a department; configuring an optimization strategy according to the type of a service product, and mainly managing a service range, a threshold and an optimization suggestion; the acquisition of resource data is realized, and the output of a resource cost bill is realized by combining with charging configuration; the beneficial effects are that: the cloud cost optimization method and system based on the government affair cloud scene, provided by the invention, realize the fine management of cloud cost by governments and enterprises based on the cloud cost optimization model, realize the overall, objective and clear understanding and evaluation of the rationality and the actual effect of the cloud investment cost while effectively exerting the performance of cloud resources, meet the management requirements of the governments and the enterprises on the cloud cost, and assist the governments and the enterprises to realize digital transformation.
Description
Technical Field
The invention relates to the technical field of cloud computing, in particular to a cloud cost optimization method and system based on a government affair cloud scene.
Background
Cloud computing is one of distributed computing, and means that a huge data computing processing program is decomposed into countless small programs through a network cloud, and then the small programs are processed and analyzed through a system consisting of a plurality of servers to obtain results and are returned to a user.
In the prior art, new problems are caused along with the deepening of the application and the cloud use degree, the cloud outletting is serious after cloud outletting, 30% of cloud outletting is wasted averagely after government affairs and enterprises are clouded according to the latest report display, the cloud cost budget is in an out-of-control state, and the problem that the waste of cloud resource cost is the key point to be considered by government affairs and enterprises is solved.
However, with the continuous expansion of business scale, governments and enterprises have urgent needs for cloud financial management, and it is necessary to make processes and systems of cost visualization, budget quota management and cost optimization management to meet the requirement of fine management of cloud cost in the current business scenario.
Disclosure of Invention
The invention aims to provide a cloud cost optimization method and system based on a government affair cloud scene so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a cloud cost optimization method based on a government affair cloud scene comprises the following steps:
based on organization and department, realizing management configuration of cost budget and resource limit;
configuring an optimization strategy according to the type of a service product, and mainly managing a service range, a threshold and an optimization suggestion;
the acquisition of resource data is realized, and the output of a resource cost bill is realized by combining with charging configuration;
and generating a cloud cost optimization suggestion according to the cost data, the budget quota and the optimization configuration strategy of the resources.
Preferably, the management configuration based on the organization and the department to realize the cost budget and the resource amount comprises the following steps:
configuring cost budgets of all departments based on organizational structures;
setting resource quotas of all departments based on an organization, wherein the configured management range mainly comprises computing resources, storage resources and network resources; the computing resources manage the number of hosts, the number of cpus, the number of memories and the number of gpus; the storage resources manage the storage capacity, the number of hard disks and the number of snapshots; the network resources manage the number of network cards, the number of security groups, the number of public networks ip and the number of load balancing.
Preferably, the management of the service range, the threshold and the optimization suggestion comprises the following steps:
selecting the type of the optimized resources, wherein the type is service products such as a cloud server, a cloud hard disk, a cloud physical host and the like;
setting the type of an optimization strategy, wherein the optimization strategy type comprises idle resource identification, idle resource prediction, resource capacity expansion prediction and over-allocation resource prediction;
and setting an optimization strategy rule, and configuring the optimized service range, configuration items, threshold values and other rule contents.
Preferably, the outputting of the bill for realizing the resource cost comprises the following steps:
acquiring basic data and performance data of resources based on agent, snmp, api and other modes;
according to the charging configuration in the charging configuration management, the summarization and the export of the resource bill data are realized, and the minimum granularity can be accurate to the hour.
Preferably, generating a cloud cost optimization suggestion according to the cost data of the resource, the budget quota and the optimization configuration policy includes the following steps:
data analysis of resource performance and use cost is realized based on the existing cloud cost optimization model;
and outputting related suggestions for cloud cost optimization while ensuring the performance, wherein the related suggestions comprise a resource release suggestion, a resource allocation reduction suggestion and a resource allocation increasing suggestion.
A cloud cost optimization system based on government affair cloud scene is composed of an acquisition module, a management module, an output module and a configuration module;
the acquisition module is used for realizing management configuration of cost budget and resource limit based on an organization mechanism and a department;
the management module is used for configuring an optimization strategy according to the type of a service product and mainly managing a service range, a threshold and an optimization suggestion;
the output module is used for realizing the acquisition of resource data and realizing the output of a resource cost bill by combining with charging configuration;
and the configuration module is used for generating a cloud cost optimization suggestion according to the cost data, the budget quota and the optimization configuration strategy of the resources.
Preferably, in the acquisition module, the cost budget of each department is configured based on an organization; setting resource quotas of all departments based on an organization mechanism, wherein the configured management range mainly comprises computing resources, storage resources and network resources; the computing resources manage the number of hosts, the number of cpus, the number of memories and the number of gpus; the storage resources manage the storage capacity, the number of hard disks and the number of snapshots; the network resources manage the number of network cards, the number of security groups, the number of public networks ip and the number of load balancing.
Preferably, in the management module, the type of the optimized resource is selected, and the type is service products such as a cloud server, a cloud hard disk, a cloud physical host and the like; setting the type of an optimization strategy, wherein the optimization strategy type comprises idle resource identification, idle resource prediction, resource capacity expansion prediction and over-allocation resource prediction; and setting an optimization strategy rule, and configuring the optimized service range, configuration items, threshold values and other rule contents.
Preferably, in the output module, the acquisition of resource basic data and performance data is realized based on agent, snmp, api and other modes; according to the charging configuration in the charging configuration management, the gathering and the exporting of the resource bill data are realized, and the minimum granularity can be accurate to hours.
Preferably, in the configuration module, data analysis of resource performance and use cost is realized based on an existing cloud cost optimization model; and outputting related suggestions for cloud cost optimization while ensuring the performance, wherein the related suggestions comprise a resource release suggestion, a resource allocation reduction suggestion and a resource allocation increasing suggestion.
Compared with the prior art, the invention has the beneficial effects that:
the cloud cost optimization method and system based on the government affair cloud scene, provided by the invention, realize the fine management of cloud cost by governments and enterprises based on the cloud cost optimization model, realize the overall, objective and clear understanding and evaluation of the rationality and the actual effect of the cloud investment cost while effectively exerting the performance of cloud resources, meet the management requirements of the governments and the enterprises on the cloud cost, and assist the governments and the enterprises to realize digital transformation.
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Fig. 1 is a schematic diagram of a cloud cost optimization process.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clear and fully described, embodiments of the present invention are further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are illustrative of some, but not all, embodiments of the invention and are not to be construed as limiting the scope of the invention, as those skilled in the art will recognize and appreciate that many other embodiments can be made without inventive faculty.
In the description of the present invention, it should be noted that the terms "center", "middle", "upper", "lower", "left", "right", "inner", "outer", "top", "bottom", "side", "vertical", "horizontal", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention. Furthermore, the terms "a," "an," "first," "second," "third," "fourth," "fifth," and "sixth" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in a specific case to those of ordinary skill in the art.
For the purposes of simplicity and explanation, the principles of the embodiments are described by referring mainly to examples. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments. It will be apparent, however, to one of ordinary skill in the art that the embodiments may be practiced without limitation to these specific details. In some instances, well-known methods and structures have not been described in detail so as not to unnecessarily obscure the embodiments. In addition, all embodiments may be used in combination with each other.
Example one
Referring to fig. 1, the present invention provides a technical solution: a cloud cost optimization method based on a government affair cloud scene comprises the following steps:
the management configuration of cost budget and resource limit is realized based on organizational structure and department; the method comprises the following steps:
configuring cost budgets of all departments based on organizational structures;
setting resource quotas of all departments based on an organization, wherein the configured management range mainly comprises computing resources, storage resources and network resources; the computing resources manage the number of hosts, the number of cpus, the number of memories and the number of gpus; the storage resources manage the storage capacity, the number of hard disks and the number of snapshots; the network resources manage the number of network cards, the number of security groups, the number of public network ip and the number of load balancing.
Configuring an optimization strategy according to the type of a service product, and mainly managing a service range, a threshold and an optimization suggestion; the method comprises the following steps:
selecting the type of the optimized resources, wherein the type is service products such as a cloud server, a cloud hard disk, a cloud physical host and the like;
setting the type of an optimization strategy, wherein the optimization strategy type comprises idle resource identification, idle resource prediction, resource capacity expansion prediction and over-allocation resource prediction;
and setting an optimization strategy rule, and configuring the optimized service range, configuration items, threshold values and other rule contents.
The acquisition of resource data is realized, and the output of a resource cost bill is realized by combining with charging configuration; the method comprises the following steps:
acquiring basic data and performance data of resources based on agent, snmp, api and other modes;
according to the charging configuration in the charging configuration management, the summarization and the export of the resource bill data are realized, and the minimum granularity can be accurate to the hour
Generating a cloud cost optimization suggestion according to the cost data, the budget quota and the optimization configuration strategy of the resources; the method comprises the following steps:
data analysis of resource performance and use cost is realized based on the existing cloud cost optimization model;
and outputting related suggestions for cloud cost optimization while ensuring the performance, wherein the related suggestions comprise a resource release suggestion, a resource allocation reduction suggestion and a resource allocation increasing suggestion.
Example two
A cloud cost optimization method based on a government affair cloud scene is implemented according to the following steps:
step 1: the budget cost and the resource quota information are acquired based on organization departments and projects. Different annual and monthly budgets can be configured for multi-level organizations and projects, the configured range can be managed with values or without limitation, but the budget of the upper level is always larger than or equal to the sum of all budgets of the related lower levels;
step 2: judging whether the resource is opened and operated, if the used cost of the resource is more than or equal to the budget cost of the project or the organization to which the resource belongs, stopping the resource instance, if the number of the resource to be opened exceeds the resource quota number of the project or the organization to which the resource belongs, the resource opening service cannot be carried out, and if the use requirement of the resource meets the requirements of the department, the budget and the quota of the project, the resource opening service is carried out;
and 3, step 3: charging the resources, and performing charging management on the resources based on charging configuration, wherein the charging mode comprises time dimension, usage amount and contract, and the time dimension mainly comprises charging according to hours, days, months and years; charging according to the use amount, namely according to the resource data actually used by the resources; charging according to the contract, namely according to the specified resource number, money and time;
and 4, step 4: displaying a resource cost bill, and visually displaying and analyzing the cost bill according to an organization, a project and a cloud service provider to realize bill allocation of the organization, the project and the service product types;
and 5: configuration information of the optimization strategy is obtained, management configuration can be carried out on the optimization strategy of the service product, and standard specifications are provided for establishment of an optimization model and output of optimization suggestions;
step 6: and outputting the optimization suggestion, and realizing the output of the resource optimization suggestion based on the cost optimization strategy model and the AI cost and performance analysis model.
And 7: and performing resource adjustment operation according to the optimization suggestion.
Step 7.1: and (3) optimizing and proposing to perform allocation increasing treatment, expanding the capacity, allocating the resources and the like, and entering step 2 after the adjustment is finished to analyze the adjusted resources and cost.
And 7.2: and (4) optimizing and proposing to reduce allocation, releasing and reducing allocation of resources and the like, and entering step 2 after adjustment is finished to analyze the adjusted resources and cost.
EXAMPLE III
A cloud cost optimization system based on government affair cloud scene is composed of an acquisition module, a management module, an output module and a configuration module;
the acquisition module is used for realizing management configuration of cost budget and resource limit based on organizational structures and departments; configuring cost budgets of all departments based on organizational structures; setting resource quotas of all departments based on an organization mechanism, wherein the configured management range mainly comprises computing resources, storage resources and network resources; the computing resources manage the number of hosts, the number of cpus, the number of memories and the number of gpus; the storage resources manage the storage capacity, the number of hard disks and the number of snapshots; the network resources manage the number of network cards, the number of security groups, the number of public networks ip and the number of load balancing.
The management module is used for configuring an optimization strategy according to the type of a service product and mainly managing a service range, a threshold and an optimization suggestion; selecting the type of the optimized resources, wherein the type is service products such as a cloud server, a cloud hard disk, a cloud physical host and the like; setting the type of an optimization strategy, wherein the optimization strategy type comprises idle resource identification, idle resource prediction, resource capacity expansion prediction and over-allocation resource prediction; and setting an optimization strategy rule, and configuring the optimized service range, configuration items, threshold values and other rule contents.
The output module is used for realizing the acquisition of resource data and realizing the output of a resource cost bill by combining with charging configuration; acquiring basic data and performance data of resources based on agent, snmp, api and other modes; according to the charging configuration in the charging configuration management, the summarization and the export of the resource bill data are realized, and the minimum granularity can be accurate to the hour.
The configuration module is used for generating a cloud cost optimization suggestion according to the cost data, the budget quota and the optimization configuration strategy of the resources; data analysis of resource performance and use cost is realized based on the existing cloud cost optimization model; and outputting related suggestions for cloud cost optimization while ensuring the performance, wherein the related suggestions comprise a resource release suggestion, a resource allocation reduction suggestion and a resource allocation increasing suggestion.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A cloud cost optimization method based on government affair cloud scenes is characterized by comprising the following steps: the cloud cost optimization method based on the government affair cloud scene comprises the following steps:
the management configuration of cost budget and resource limit is realized based on organizational structure and department;
configuring an optimization strategy according to the type of a service product, and mainly managing a service range, a threshold value threshold and an optimization suggestion;
the acquisition of resource data is realized, and the output of a resource cost bill is realized by combining with charging configuration;
and generating a cloud cost optimization suggestion according to the cost data, the budget quota and the optimization configuration strategy of the resources.
2. The cloud cost optimization method based on the government affairs cloud scene according to claim 1, wherein: the management configuration of cost budget and resource limit based on an organization and a department comprises the following steps:
configuring cost budgets of all departments based on an organization;
setting resource quotas of all departments based on an organization, wherein the configured management range mainly comprises computing resources, storage resources and network resources; the computing resources manage the number of hosts, the number of cpus, the number of memories and the number of gpus; the storage resources manage the storage capacity, the number of hard disks and the number of snapshots; the network resources manage the number of network cards, the number of security groups, the number of public networks ip and the number of load balancing.
3. The cloud cost optimization method based on the government affair cloud scene according to claim 2, wherein: the management of the service range, the threshold and the optimization suggestion comprises the following steps:
selecting the type of the optimized resources, wherein the type is service products such as a cloud server, a cloud hard disk, a cloud physical host and the like;
setting the type of an optimization strategy, wherein the optimization strategy type comprises idle resource identification, idle resource prediction, resource capacity expansion prediction and over-allocation resource prediction;
and setting an optimization strategy rule, and configuring the optimized service range, configuration items, threshold values and other rule contents.
4. The cloud cost optimization method based on the government affairs cloud scene according to claim 3, wherein: the method for realizing the output of the resource cost bill comprises the following steps:
acquiring basic data and performance data of resources based on agent, snmp, api and other modes;
according to the charging configuration in the charging configuration management, the summarization and the export of the resource bill data are realized, and the minimum granularity can be accurate to the hour.
5. The cloud cost optimization method based on the government affairs cloud scene according to claim 4, wherein: according to the cost data, the budget quota and the optimization configuration strategy of the resource, the method for generating the cloud cost optimization suggestion comprises the following steps:
data analysis of resource performance and use cost is realized based on the existing cloud cost optimization model;
and outputting related suggestions for cloud cost optimization while ensuring the performance, wherein the related suggestions comprise a resource release suggestion, a resource allocation reduction suggestion and a resource allocation increasing suggestion.
6. A cloud cost optimization system based on government cloud scenarios according to any one of claims 1 to 5, wherein: the system consists of an acquisition module, a management module, an output module and a configuration module;
the acquisition module is used for realizing management configuration of cost budget and resource limit based on an organization mechanism and a department;
the management module is used for configuring an optimization strategy according to the type of a service product and mainly managing a service range, a threshold and an optimization suggestion;
the output module is used for realizing the acquisition of resource data and realizing the output of a resource cost bill by combining with charging configuration;
and the configuration module is used for generating a cloud cost optimization suggestion according to the cost data of the resources, the budget quota and the optimization configuration strategy.
7. The cloud cost optimization method and system based on the government affair cloud scene according to claim 6, wherein: in the acquisition module, the cost budget of each department is configured based on an organization mechanism; setting resource quotas of all departments based on an organization mechanism, wherein the configured management range mainly comprises computing resources, storage resources and network resources; the computing resources manage the number of hosts, the number of cpus, the number of memories and the number of gpus; the storage resources manage the storage capacity, the number of hard disks and the number of snapshots; the network resources manage the number of network cards, the number of security groups, the number of public network ip and the number of load balancing.
8. The cloud cost optimization method and system based on the government affairs cloud scene according to claim 7, wherein: in the management module, the type of the optimized resource is selected, and the type is service products such as a cloud server, a cloud hard disk, a cloud physical host and the like; setting the type of an optimization strategy, wherein the optimization strategy type comprises idle resource identification, idle resource prediction, resource capacity expansion prediction and over-allocation resource prediction; and setting an optimization strategy rule, and configuring the optimized service range, configuration items, threshold values and other rule contents.
9. The cloud cost optimization method and system based on the government affair cloud scene according to claim 8, wherein: in the output module, the acquisition of resource basic data and performance data is realized based on agent, snmp, api and other modes; according to the charging configuration in the charging configuration management, the summarization and the export of the resource bill data are realized, and the minimum granularity can be accurate to the hour.
10. The cloud cost optimization method and system based on the government affair cloud scene according to claim 9, wherein: in the configuration module, data analysis of resource performance and use cost is realized based on the existing cloud cost optimization model; and outputting related suggestions for cloud cost optimization while ensuring the performance, wherein the related suggestions comprise a resource release suggestion, a resource allocation reduction suggestion and a resource allocation increasing suggestion.
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