Cloud cost management method and device, equipment and medium

Abstract

The embodiment of the invention provides a cloud cost management method and device, equipment and a medium. Synchronizing resources of the corresponding cloud product owned by the user; according to the data analysis of the cloud product, obtaining target optimization suggestion information; acquiring associated data from a data source corresponding to the cloud product according to associated data types of the resources and the target optimization suggestion information; when the associated data meets the optimization condition of the optimization suggestion information, calculating to obtain the cost difference of the cloud product before and after optimization; according to the method, the optimization suggestions of the resources of different cloud products and the cost difference before and after optimization can be conveniently and quickly provided for the user, so that the user can freely combine different optimization suggestions, and the user can comprehensively and deeply understand the optimization scheme of the resources of the cloud products.

Description

Technical Field

[0001] The present application relates to the field of cloud service technology, and in particular to cloud cost management methods, devices, equipment and media. Background Art

[0002] The Financial Operations (Fi nOps) system is a best practice method for managing cloud costs. It aims to provide users with a direction for reducing costs and increasing efficiency of cloud resources by combining technologies such as cost, usage, asset management, data integration, analysis and prediction, dynamic scheduling and resource recommendation. The current FinOps system mainly provides statistics, analysis and prediction of cloud product costs. As for the optimization analysis of specific resource costs, manual sorting and judgment are often required. However, there are many types of cloud products, each of which usually has a large number of resources. There are many different specialized ways to optimize different products. Manual sorting has the disadvantages of low efficiency and low precision. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a cloud cost management method, device, equipment and medium that can automatically optimize and analyze cloud product resources.

[0004] To achieve the above objectives, a first aspect of an embodiment of the present application proposes a cloud cost management method, including:

[0005] Get data about cloud products;

[0006] Synchronize the resources owned by the user corresponding to the cloud product;

[0007] Analyze the data of the cloud product to obtain target optimization recommendation information;

[0008] According to the data types associated with the resource and the target optimization suggestion information, acquiring associated data from a data source corresponding to the cloud product;

[0009] When the associated data meets the optimization condition of the optimization suggestion information, the cost difference of the cloud product before and after the optimization is calculated.

[0010] In some embodiments, analyzing the data of the cloud product to obtain target optimization suggestion information includes:

[0011] Analyze the data of the cloud product to obtain multiple candidate optimization suggestion information;

[0012] According to the priority of the candidate optimization suggestion information, the candidate optimization suggestion information is analyzed according to the resource to obtain an analysis result;

[0013] The candidate optimization suggestion information for which the analysis result indicates that there is an optimization opportunity is selected as the target optimization suggestion information.

[0014] In some embodiments, analyzing the candidate optimization suggestion information according to the priority of the candidate optimization suggestion information and according to the resource to obtain the analysis result includes:

[0015] When the analysis requirement is local analysis, the candidate optimization suggestion information is analyzed one by one according to the priority of the candidate optimization suggestion information and the resource, until the first candidate optimization suggestion information for which the analysis result indicates that there is an optimization opportunity is detected;

[0016] The first analysis result outputs the analysis result of candidate optimization recommendation information indicating the existence of optimization opportunities.

[0017] In some embodiments, analyzing the candidate optimization suggestion information according to the priority of the candidate optimization suggestion information and according to the resource to obtain the analysis result includes:

[0018] When the analysis requirement is a comprehensive analysis, all of the candidate optimization suggestion information are analyzed according to the priority of the candidate optimization suggestion information and the resources to obtain analysis results of all of the candidate optimization suggestion information.

[0019] In some embodiments, when the associated data meets the optimization condition of the optimization suggestion information, the cost difference of the cloud product before and after the optimization is calculated, including:

[0020] When the associated data meets the optimization condition of the optimization suggestion information, a recommendation scheme is generated according to the recommendation rule;

[0021] The cost difference of the cloud product before and after optimization is calculated based on the recommended solution.

[0022] In some embodiments, calculating the cost difference of the cloud product before and after optimization according to the recommended solution includes:

[0023] The first cost of the cloud product before optimization is calculated based on the resources corresponding to the cloud product before optimization;

[0024] Calculate the second cost of the cloud product after optimization based on the resources corresponding to the recommended solution;

[0025] The cost difference of the cloud product before and after the optimization is obtained according to the first cost and the cost calculation.

[0026] In some embodiments, the associated data is stored in a cache of a cloud server.

[0027] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes a cloud cost management device, including:

[0028] Data input module, used to obtain data of cloud products;

[0029] A resource synchronization module, used to synchronize the resources owned by the user corresponding to the cloud product;

[0030] A solution analysis module is used to analyze the data of the cloud product to obtain target optimization suggestion information;

[0031] A related data acquisition module, configured to acquire related data from a data source corresponding to the cloud product according to the type of related data between the resource and the target optimization suggestion information;

[0032] The suggestion analysis module is used to calculate the cost difference of the cloud product before and after optimization when the associated data meets the optimization conditions of the optimization suggestion information.

[0033] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the cloud cost management method described in the first aspect of the embodiment of the present application is implemented.

[0034] To achieve the above-mentioned purpose, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the cloud cost management method described in the first aspect of the embodiments of the present application.

[0035] The present application has the following beneficial effects: by synchronizing the resources of the corresponding cloud products owned by the user; obtaining target optimization suggestion information based on data analysis of the cloud products; obtaining related data from the data source corresponding to the cloud product based on the type of data associated with the resources and the target optimization suggestion information; when the related data meets the optimization conditions of the optimization suggestion information, calculating the cost difference of the cloud product before and after optimization; and providing users with optimization suggestions for resources of different cloud products and the cost difference before and after optimization in a convenient and fast manner, allowing users to freely combine different optimization suggestions, so that users can fully and deeply understand the optimization solutions for the resources of cloud products. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a step diagram of a cloud cost management method provided by an embodiment of the present application;

[0037] Figure 2 It is a sub-step diagram of step S300 provided in an embodiment of the present application;

[0038] Figure 3is a step diagram of step S321 provided in an embodiment of the present application;

[0039] Figure 4 is a step diagram of step S322 provided in an embodiment of the present application;

[0040] Figure 5 is a step diagram of step S500 provided in an embodiment of the present application;

[0041] Figure 6 is a step diagram of step S520 provided in an embodiment of the present application;

[0042] Figure 7 is a structural diagram of a cloud cost management device provided in an embodiment of the present application;

[0043] Figure 8 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0047] The financial operation system is a system of best practices for managing cloud costs. It aims to provide users with a direction for reducing costs and increasing efficiency of cloud resources by combining technologies such as cost, usage, asset management, data integration, analysis and prediction, dynamic scheduling and resource recommendation. The current FinOps system mainly provides statistics, analysis and prediction of cloud product costs. As for the optimization analysis of specific resource costs, manual sorting and judgment are often required. However, there are many types of cloud products, and each product usually has a large number of resources. There are many different specialized ways to optimize different products. Manual sorting has the disadvantages of low efficiency and low precision.

[0048] In order to solve the above problems, the embodiments of the present application provide a cloud cost management method, apparatus, device and medium, which can automatically perform optimization analysis and sorting of cloud product resources.

[0049] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0050] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0051] The data storage method, data retrieval method and related devices provided in the embodiments of the present application relate to the field of artificial intelligence technology. The data storage method and data retrieval method provided in the embodiments of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements data storage methods and data retrieval methods, etc., but is not limited to the above forms.

[0052] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0053] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant regulations. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or jump to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0054] The cloud cost management method, apparatus, device and medium provided in the embodiments of the present application are specifically described by the following embodiments, and firstly, the cloud cost management method in the embodiments of the present application is described.

[0055] The embodiment of the present application provides a cloud cost management method, which is applied to a financial operation system.

[0056] The FinOps system is a system of best practices for managing cloud costs, designed to help organizations better understand and control cloud costs. The FinOps system defines a series of cloud financial management rules and best practices. The goal of the FinOps system is to help organizations better manage cloud computing costs and improve efficiency and flexibility through collaborative work between engineering, finance, technology, and business teams, based on data-driven cost decisions.

[0057] Reference Figure 1 , the cloud cost management method includes the following steps:

[0058] Step S100, obtaining cloud product data;

[0059] Step S200, synchronizing the resources of the corresponding cloud products owned by the user;

[0060] Step S300, analyzing the data of the cloud product to obtain target optimization suggestion information;

[0061] Step S400, acquiring associated data from a data source corresponding to a cloud product according to the associated data type of the resource and target optimization suggestion information;

[0062] Step S500: When the associated data meets the optimization conditions of the optimization suggestion information, the cost difference of the cloud product before and after the optimization is calculated.

[0063] In step S100 of some embodiments, data of cloud products are read from the database, including general data such as the type, specifications, and pricing of cloud products, and may also include computing resources (such as CPU and memory usage), storage resources (such as disk usage, I / O operations), network resources (such as data transmission volume), etc. These data can be obtained through the API or management console of the cloud service provider.

[0064] Cloud products include cloud hosts, MySQL databases, etc.

[0065] In step S200 of some embodiments, resources of the corresponding cloud product owned by the user are synchronized according to the type of the cloud product.

[0066] For example, for cloud host products, the resources of the cloud host owned by users mainly include: computing resources, storage resources, network resources, images and templates, monitoring and logs, and security settings. Among them, computing resources include the number of CPU cores, memory size, etc. Storage resources: involve hard disk type, capacity, and snapshots and backups of data storage. Network resources include virtual networks, IP addresses, bandwidth allocation, and network security groups. Images and templates include images of operating systems and pre-installed applications for rapid deployment of cloud host instances. Monitoring and logs include monitoring data and log files of resource usage for performance analysis and troubleshooting. Security settings include key pairs, access control lists, and security groups.

[0067] For MySQL database products, the MySQL database resources owned by users mainly include: database instance, storage and backup, network access, performance monitoring and security configuration. Among them, the database instance includes the configuration and version information of the database server. Storage and backup include database storage capacity, backup strategy and backup files. Network access includes database access rights, ports and network configuration. Performance monitoring includes database performance indicators such as query response time, number of connections and resource utilization. Security configuration includes user permissions, roles and encryption settings.

[0068] Reference Figure 2In step S300 of some embodiments, analyzing the data of the cloud product to obtain target optimization suggestion information includes the following steps:

[0069] Step S310, analyzing the data of the cloud product to obtain multiple candidate optimization suggestion information;

[0070] Step S320, analyzing the candidate optimization suggestion information according to the priority of the candidate optimization suggestion information and according to the resources to obtain an analysis result;

[0071] Step S330 : selecting candidate optimization suggestion information for which the analysis result indicates there is an optimization opportunity as target optimization suggestion information.

[0072] It is understandable that the optimization suggestion information is based on a certain dimension to analyze the product, determine whether the product can be optimized, and come up with an optimization plan and product configuration, price and other data before and after optimization. For the same cloud product, multiple optimization suggestion information can be selected to combine into one plan. A user can make multiple plans, and one plan can be associated with one cloud product and multiple optimization suggestion information.

[0073] For step S310, the service cost of the overall cloud product is allocated according to usage; the performance indicators of cloud resources are analyzed, such as CPU and memory utilization, disk I / O performance, network latency, etc., to identify performance bottlenecks and over-configured resources; historical data and trend analysis are used to predict future resource requirements and costs; based on the above analysis, specific candidate optimization recommendation information is generated; these recommendation information may include adjusting resource configuration, shutting down unused resources, adopting more efficient service options, implementing load balancing strategies, etc.

[0074] For step S320, the candidate optimization suggestion information is analyzed according to the priority of the candidate optimization suggestion information and the resources to obtain the analysis result, including step S321 and step S322.

[0075] Reference Figure 3 , step S321, when the analysis requirement is local analysis, the candidate optimization suggestion information is analyzed one by one according to the priority of the candidate optimization suggestion information and the resources until the first analysis result indicating the candidate optimization suggestion information with optimization opportunity is detected; the analysis result of the first analysis result indicating the candidate optimization suggestion information with optimization opportunity is output.

[0076] Reference Figure 4 , step S322, when the analysis requirement is a comprehensive analysis, all candidate optimization suggestion information is analyzed according to the priority of the candidate optimization suggestion information and the resources to obtain the analysis results of all candidate optimization suggestion information.

[0077] Exemplarily, there are four candidate optimization suggestion information, which are sorted into candidate optimization suggestion information 1, candidate optimization suggestion information 2, candidate optimization suggestion information 3 and candidate optimization suggestion information 4 according to their priorities.

[0078] When the analysis requirement is local analysis, the candidate optimization suggestion information is analyzed one by one according to the priority of the candidate optimization suggestion information and the resources. Candidate optimization suggestion information 1 is analyzed first. When the analysis result of candidate optimization suggestion information 1 indicates that there is no optimization opportunity, candidate optimization suggestion information 2 is analyzed. When the analysis result of candidate optimization suggestion information 2 indicates that there is no optimization opportunity, candidate optimization suggestion information 3 is analyzed. When the analysis result of candidate optimization suggestion information 3 indicates that there is an optimization opportunity, candidate optimization suggestion information 3 is the first candidate optimization suggestion information whose analysis result indicates that there is an optimization opportunity, and the analysis result of candidate optimization suggestion information 3 is output. Subsequent analysis of the candidate optimization suggestion information is stopped, that is, there is no need to analyze candidate optimization suggestion information 4.

[0079] When the analysis requirement is a comprehensive analysis, the candidate optimization suggestion information is analyzed one by one according to the priority of the candidate optimization suggestion information and the resources. First, the candidate optimization suggestion information 1 is analyzed to obtain the analysis result of the candidate optimization suggestion information 1; then the candidate optimization suggestion information 2 is analyzed to obtain the analysis result of the candidate optimization suggestion information 2; then the candidate optimization suggestion information 3 is analyzed to obtain the analysis result of the candidate optimization suggestion information 3; finally, the candidate optimization suggestion information 4 is analyzed to obtain the analysis result of the candidate optimization suggestion information 4. The analysis results of all the candidate optimization suggestion information are output, that is, the analysis results of the candidate optimization suggestion information 1, the analysis results of the candidate optimization suggestion information 2, the analysis results of the candidate optimization suggestion information 3, and the analysis results of the candidate optimization suggestion information 4.

[0080] For step S330, when the analysis requirement is a local analysis, only the analysis result of the candidate optimization suggestion information whose first analysis result indicates that there is an optimization opportunity will be output, then only one analysis result of the candidate optimization suggestion information indicates that there is an optimization opportunity, or, the analysis results of zero candidate optimization suggestion information indicate that there is an optimization opportunity. When the analysis requirement is a comprehensive analysis, all candidate optimization suggestion information will be analyzed and the analysis results of all candidate optimization suggestion information will be output, then one or more analysis results indicate that there is an optimization opportunity, or, the analysis results of zero candidate optimization suggestion information indicate that there is an optimization opportunity. The candidate optimization suggestion information whose analysis result indicates that there is an optimization opportunity is selected as the target optimization suggestion information.

[0081] For example, when the analysis requirement is local analysis, and only the analysis result of candidate optimization suggestion information 3 indicates that there is an optimization opportunity, candidate optimization suggestion information 3 is selected as target optimization suggestion information.

[0082] When the analysis requirement is local analysis, and the analysis results of candidate optimization suggestion information 3 and candidate optimization suggestion information 4 indicate that there is an optimization opportunity, candidate optimization suggestion information 3 and candidate optimization suggestion information 4 are selected as target optimization suggestion information.

[0083] In step S400 of some embodiments, the associated data is obtained from a data source corresponding to the cloud product according to the associated data type of the resource and target optimization suggestion information.

[0084] During the solution analysis phase, based on the target optimization suggestion information, the associated data types required by the target optimization suggestion information are parsed and aggregated. Based on the associated data types of the resources and target optimization suggestion information, the associated data is obtained from the data source corresponding to the cloud product and stored in the server cache.

[0085] In the suggestion analysis phase, the associated data corresponding to the current resource is read from the server's cache according to the associated data type of the target optimization suggestion information.

[0086] The source of the associated data is the data source corresponding to the cloud product. The data source corresponding to the cloud product includes the attributes and usage of current resources, historical data, market and pricing data, business and operational indicators, external data sources, configuration files, logs and monitoring data, and other related resources.

[0087] Among them, the attributes and usage of current resources include resource configuration, performance indicators, usage rate, cost data, etc. Historical data includes past usage patterns of resources, cost history and performance records, which can provide a basis for trend analysis. Market and pricing data include cloud service providers' pricing models, discount information, market trends, etc. Business and operational indicators include business requirements, service level agreements, budget constraints, etc. related to resources. External data sources include industry benchmarks, best practices, regulatory requirements, etc. Configuration files include user-defined configurations, preference settings, policy rules, etc. Log and monitoring data comes from system and application log files, output of monitoring tools, etc. Other related resources include data from other systems or services that interact with or depend on the current resources, etc.

[0088] Reference Figure 5 In step S500 of some embodiments, when the associated data meets the optimization condition of the optimization suggestion information, the cost difference of the cloud product before and after the optimization is calculated, including the following steps:

[0089] Step S510, when the associated data meets the optimization conditions of the optimization suggestion information, a recommendation solution is generated according to the recommendation rules;

[0090] Step S520: Calculate the cost difference of the cloud product before and after optimization based on the recommended solution.

[0091] Reference Figure 6 , wherein the cost difference of the cloud product before and after optimization is calculated according to the recommended solution, including the following steps:

[0092] Step S521, calculating the first cost of the cloud product before optimization according to the resources corresponding to the cloud product before optimization;

[0093] Step S522, calculating the second cost of the cloud product after optimization according to the resources corresponding to the recommended solution;

[0094] Step S523, calculating the cost difference of the cloud product before and after the optimization based on the first cost and the cost.

[0095] Exemplarily, for a cloud product of a cloud host, one optimization suggestion information is named utilization analysis, and the optimization condition of utilization analysis is to determine whether optimization can be achieved by downgrading based on the CPU and memory utilization of the cloud host, and the recommended rule is to reduce the CPU or memory, and to the specification closest to the current configuration. Another optimization suggestion information is named billing mode analysis, and the optimization condition of billing mode analysis is to determine whether optimization can be achieved by adjusting the billing mode based on the running time and billing mode of the cloud host, and the recommended rule is another billing mode. Yet another optimization suggestion information is named backup cleanup analysis, and the optimization condition of backup cleanup analysis is to determine whether optimization can be achieved by cleaning up backups based on the backup time, quantity, and strategy of the cloud host, and the recommended rule is to clean up backups before a certain point in time.

[0096] For cloud products of MySQL database, one optimization suggestion is named utilization analysis. The optimization condition of utilization analysis is to judge whether optimization can be achieved by downgrading according to the CPU and memory utilization of MySQL. The recommended rule is the specification that is closest to the current configuration and has both CPU and memory reduced. Another optimization suggestion is named exclusive analysis. The optimization condition of exclusive analysis is to judge whether optimization can be achieved by converting the attribute according to the exclusive attribute and utilization of the database. The recommended rule is another exclusive attribute. Another optimization suggestion is named instance type analysis. The optimization condition of instance type analysis is to judge whether optimization can be achieved by downgrading according to the environment, instance type, and role of MySQL. The recommended rule is the specification corresponding to the specific downgrading.

[0097] In summary, the main process of the cloud cost management method is: synchronize the data of cloud products; load the plan of optimization suggestion information, group and sort the plans according to users; traverse the plan group and analyze the plan of optimization suggestion information; save the analysis results of the optimization suggestion information, summarize and generate a report, and notify the user.

[0098] The solution analysis process is as follows: Synchronize product resources: First, synchronize all resources associated with the solution. This step is to ensure the comprehensiveness of the analysis. Get optimization suggestions: Collect optimization suggestions for all solutions and parse the required associated data types. Synchronize associated data: According to resource information and the data types required by the optimization suggestions, synchronize data from various data sources and put them into the cache for fast access. Sort optimization suggestions: Prioritize the optimization suggestions according to the preset standards. Traverse resource analysis: Analyze each resource in the order of optimization suggestions to determine whether there are optimization opportunities. Analysis result judgment: If the analysis result shows that the current resource configuration does not meet the optimization suggestion, continue to analyze other optimization suggestions in sequence. Return partial analysis results: If the analysis results meet the optimization suggestions and the solution configuration does not require a full analysis, return this part of the analysis results. Return full analysis results: If the solution is configured with full analysis, even if some analysis results already meet the optimization requirements, all suggestions should continue to be analyzed, and finally all analysis results that meet the optimization requirements are returned.

[0099] The recommended analysis process is as follows: Get related data: Get the related data corresponding to the current resource from the cache according to the data type required by the optimization recommendation. Determine the optimization conditions: Determine whether the current resource meets the optimization conditions according to the optimization recommendation rules. Generate a recommended solution: If the resource meets the optimization conditions, generate a recommended solution according to the recommendation rules, and calculate the cost of the current resource, the cost of the recommended solution, and the cost difference. Return analysis results: Return analysis results including cost analysis and recommended solutions. If the resource does not meet the optimization conditions, return the result that does not meet the optimization.

[0100] The two processes are interrelated. The solution analysis process determines which optimization recommendations should be considered, while the recommendation analysis process conducts a detailed analysis of each optimization recommendation to determine whether it can be implemented and the effect after implementation. This hierarchical analysis approach helps ensure that the FinOps solution can comprehensively evaluate all possible optimization opportunities while providing specific, data-driven recommendations.

[0101] This cloud cost management method can conveniently and quickly provide users with optimization suggestions for resources of different cloud products and the cost difference before and after optimization, allowing users to freely combine different optimization suggestions and enable users to fully and deeply understand the optimization solutions for cloud product resources.

[0102] An embodiment of the present application provides a cloud cost management device.

[0103] Reference Figure 7 The cloud cost management device includes: a data input module 100, a resource synchronization module 200, a solution analysis module 300, a related data acquisition module 400 and a suggestion analysis module 500.

[0104] Among them, the data input module 100 is used to obtain the data of cloud products; the resource synchronization module 200 is used to synchronize the resources of the corresponding cloud products owned by the user; the solution analysis module 300 is used to analyze the data of cloud products to obtain target optimization suggestion information; the associated data acquisition module 400 is used to obtain associated data from the data source corresponding to the cloud product according to the associated data type of the resource and target optimization suggestion information; the suggestion analysis module 500 is used to calculate the cost difference of the cloud product before and after optimization when the associated data meets the optimization conditions of the optimization suggestion information.

[0105] The data input module also includes a business module 110, which is used to interact with users and provide functions such as query and configuration. The data input module also includes a product module 120, which is used to synchronize general data such as product type, specification, and pricing.

[0106] The resource synchronization module 200 also includes an adaptation module 210, which is used to connect with external systems. The external systems include a configuration management database (CMDB), a monitoring system, a cloud vendor, and a user system.

[0107] The cloud cost management device further includes a notification module 600, which is used to notify the user of the analysis results including the cost difference of the cloud product before and after the optimization.

[0108] The user interacts with the cloud cost management device through the business module 110 of the data input module 100, and inputs information related to functions such as query and configuration. The product module 120 of the data input module 100 reads the data of cloud products from the database, including general data such as the type, specification, and pricing of cloud products, and may also include computing resources (such as CPU and memory usage), storage resources (such as disk usage, I / O operations), network resources (such as data transmission volume), etc. These data can be obtained through the API or management console of the cloud service provider.

[0109] Cloud products include cloud hosts, MySQL databases, etc.

[0110] The resource synchronization module 200 synchronizes the resources of the corresponding cloud products owned by the user according to the type of the cloud products.

[0111] For example, for cloud host products, the resources of the cloud host owned by users mainly include: computing resources, storage resources, network resources, images and templates, monitoring and logs, and security settings. Among them, computing resources include the number of CPU cores, memory size, etc. Storage resources: involve hard disk type, capacity, and snapshots and backups of data storage. Network resources include virtual networks, IP addresses, bandwidth allocation, and network security groups. Images and templates include images of operating systems and pre-installed applications for rapid deployment of cloud host instances. Monitoring and logs include monitoring data and log files of resource usage for performance analysis and troubleshooting. Security settings include key pairs, access control lists, and security groups.

[0112] For MySQL database products, the MySQL database resources owned by users mainly include: database instance, storage and backup, network access, performance monitoring and security configuration. Among them, the database instance includes the configuration and version information of the database server. Storage and backup include database storage capacity, backup strategy and backup files. Network access includes database access rights, ports and network configuration. Performance monitoring includes database performance indicators such as query response time, number of connections and resource utilization. Security configuration includes user permissions, roles and encryption settings.

[0113] The solution analysis module 300 analyzes the data of the cloud product to obtain target optimization suggestion information. Specifically, the cloud product data is analyzed to obtain multiple candidate optimization suggestion information; the candidate optimization suggestion information is analyzed according to the priority of the candidate optimization suggestion information and the resources to obtain the analysis result; the candidate optimization suggestion information for which the analysis result indicates that there is an optimization opportunity is selected as the target optimization suggestion information.

[0114] It is understandable that the optimization suggestion information is based on a certain dimension to analyze the product, determine whether the product can be optimized, and come up with an optimization plan and product configuration, price and other data before and after optimization. For the same cloud product, multiple optimization suggestion information can be selected to combine into one plan. A user can make multiple plans, and one plan can be associated with one cloud product and multiple optimization suggestion information.

[0115] Allocate the overall cloud product service cost based on usage; analyze cloud resource performance indicators, such as CPU and memory utilization, disk I / O performance, network latency, etc., to identify performance bottlenecks and over-configured resources; use historical data and trend analysis to predict future resource requirements and costs; based on the above analysis, generate specific candidate optimization recommendation information; these recommendations may include adjusting resource configuration, shutting down unused resources, adopting more efficient service options, implementing load balancing strategies, etc.

[0116] According to the priority of the candidate optimization suggestion information, the candidate optimization suggestion information is analyzed according to the resources to obtain the analysis results. Specifically, when the analysis requirement is local analysis, according to the priority of the candidate optimization suggestion information, the candidate optimization suggestion information is analyzed one by one according to the resources until the first analysis result indicates that the candidate optimization suggestion information has an optimization opportunity is detected; the analysis result of the candidate optimization suggestion information whose first analysis result indicates that the optimization opportunity exists is output; when the analysis requirement is comprehensive analysis, according to the priority of the candidate optimization suggestion information, all the candidate optimization suggestion information is analyzed according to the resources to obtain the analysis results of all the candidate optimization suggestion information.

[0117] Exemplarily, there are four candidate optimization suggestion information, which are sorted into candidate optimization suggestion information 1, candidate optimization suggestion information 2, candidate optimization suggestion information 3 and candidate optimization suggestion information 4 according to their priorities.

[0118] When the analysis requirement is local analysis, the candidate optimization suggestion information is analyzed one by one according to the priority of the candidate optimization suggestion information and the resources. Candidate optimization suggestion information 1 is analyzed first. When the analysis result of candidate optimization suggestion information 1 indicates that there is no optimization opportunity, candidate optimization suggestion information 2 is analyzed. When the analysis result of candidate optimization suggestion information 2 indicates that there is no optimization opportunity, candidate optimization suggestion information 3 is analyzed. When the analysis result of candidate optimization suggestion information 3 indicates that there is an optimization opportunity, candidate optimization suggestion information 3 is the first candidate optimization suggestion information whose analysis result indicates that there is an optimization opportunity, and the analysis result of candidate optimization suggestion information 3 is output. Subsequent analysis of the candidate optimization suggestion information is stopped, that is, there is no need to analyze candidate optimization suggestion information 4.

[0119] When the analysis requirement is a comprehensive analysis, the candidate optimization suggestion information is analyzed one by one according to the priority of the candidate optimization suggestion information and the resources. First, the candidate optimization suggestion information 1 is analyzed to obtain the analysis result of the candidate optimization suggestion information 1; then the candidate optimization suggestion information 2 is analyzed to obtain the analysis result of the candidate optimization suggestion information 2; then the candidate optimization suggestion information 3 is analyzed to obtain the analysis result of the candidate optimization suggestion information 3; finally, the candidate optimization suggestion information 4 is analyzed to obtain the analysis result of the candidate optimization suggestion information 4. The analysis results of all the candidate optimization suggestion information are output, that is, the analysis results of the candidate optimization suggestion information 1, the analysis results of the candidate optimization suggestion information 2, the analysis results of the candidate optimization suggestion information 3, and the analysis results of the candidate optimization suggestion information 4.

[0120] When the analysis requirement is local analysis, only the analysis result of the first candidate optimization suggestion information indicating the existence of optimization opportunities will be output. In this case, only one analysis result of the candidate optimization suggestion information indicates the existence of optimization opportunities, or the analysis results of zero candidate optimization suggestion information indicate the existence of optimization opportunities. When the analysis requirement is comprehensive analysis, all candidate optimization suggestion information will be analyzed and the analysis results of all candidate optimization suggestion information will be output. In this case, one or more analysis results indicate the existence of optimization opportunities, or the analysis results of zero candidate optimization suggestion information indicate the existence of optimization opportunities. The candidate optimization suggestion information whose analysis results indicate the existence of optimization opportunities is selected as the target optimization suggestion information.

[0121] For example, when the analysis requirement is local analysis, and only the analysis result of candidate optimization suggestion information 3 indicates that there is an optimization opportunity, candidate optimization suggestion information 3 is selected as target optimization suggestion information.

[0122] When the analysis requirement is local analysis, and the analysis results of candidate optimization suggestion information 3 and candidate optimization suggestion information 4 indicate that there is an optimization opportunity, candidate optimization suggestion information 3 and candidate optimization suggestion information 4 are selected as target optimization suggestion information.

[0123] The associated data acquisition module 400 acquires associated data from a data source corresponding to the cloud product according to the associated data type of the resource and target optimization suggestion information.

[0124] During the solution analysis phase, based on the target optimization suggestion information, the associated data types required by the target optimization suggestion information are parsed and aggregated. Based on the associated data types of the resources and target optimization suggestion information, the associated data is obtained from the data source corresponding to the cloud product and stored in the server cache.

[0125] In the suggestion analysis phase, the associated data corresponding to the current resource is read from the server's cache according to the associated data type of the target optimization suggestion information.

[0126] The source of the associated data is the data source corresponding to the cloud product. The data source corresponding to the cloud product includes the attributes and usage of current resources, historical data, market and pricing data, business and operational indicators, external data sources, configuration files, logs and monitoring data, and other related resources.

[0127] Among them, the attributes and usage of current resources include resource configuration, performance indicators, usage rate, cost data, etc. Historical data includes past usage patterns of resources, cost history and performance records, which can provide a basis for trend analysis. Market and pricing data include cloud service providers' pricing models, discount information, market trends, etc. Business and operational indicators include business requirements, service level agreements, budget constraints, etc. related to resources. External data sources include industry benchmarks, best practices, regulatory requirements, etc. Configuration files include user-defined configurations, preference settings, policy rules, etc. Log and monitoring data comes from system and application log files, output of monitoring tools, etc. Other related resources include data from other systems or services that interact with or depend on the current resources, etc.

[0128] When the associated data meets the optimization conditions of the optimization suggestion information, the suggestion analysis module 500 calculates the cost difference of the cloud product before and after the optimization. Specifically, when the associated data meets the optimization conditions of the optimization suggestion information, a recommendation scheme is generated according to the recommendation rules; and the cost difference of the cloud product before and after the optimization is calculated according to the recommendation scheme.

[0129] Among them, for the cost difference of the cloud product before and after optimization calculated according to the recommended solution, specifically, the first cost of the cloud product before optimization is calculated according to the resources corresponding to the cloud product before optimization; the second cost of the cloud product after optimization is calculated according to the resources corresponding to the recommended solution; the cost difference of the cloud product before and after optimization is calculated according to the first cost and the cost.

[0130] Exemplarily, for a cloud product of a cloud host, one optimization suggestion information is named utilization analysis, and the optimization condition of utilization analysis is to determine whether optimization can be achieved by downgrading based on the CPU and memory utilization of the cloud host, and the recommended rule is to reduce the CPU or memory, and to the specification closest to the current configuration. Another optimization suggestion information is named billing mode analysis, and the optimization condition of billing mode analysis is to determine whether optimization can be achieved by adjusting the billing mode based on the running time and billing mode of the cloud host, and the recommended rule is another billing mode. Yet another optimization suggestion information is named backup cleanup analysis, and the optimization condition of backup cleanup analysis is to determine whether optimization can be achieved by cleaning up backups based on the backup time, quantity, and strategy of the cloud host, and the recommended rule is to clean up backups before a certain point in time.

[0131] For cloud products of MySQL database, one optimization suggestion is named utilization analysis. The optimization condition of utilization analysis is to judge whether optimization can be achieved by downgrading according to the CPU and memory utilization of MySQL. The recommended rule is the specification that is closest to the current configuration and has both CPU and memory reduced. Another optimization suggestion is named exclusive analysis. The optimization condition of exclusive analysis is to judge whether optimization can be achieved by converting the attribute according to the exclusive attribute and utilization of the database. The recommended rule is another exclusive attribute. Another optimization suggestion is named instance type analysis. The optimization condition of instance type analysis is to judge whether optimization can be achieved by downgrading according to the environment, instance type, and role of MySQL. The recommended rule is the specification corresponding to the specific downgrading.

[0132] In summary, the main process of the cloud cost management method is: synchronize the data of cloud products; load the plan of optimization suggestion information, group and sort the plans according to users; traverse the plan group and analyze the plan of optimization suggestion information; save the analysis results of the optimization suggestion information, summarize and generate a report, and notify the user.

[0133] The solution analysis process is as follows: Synchronize product resources: First, synchronize all resources associated with the solution. This step is to ensure the comprehensiveness of the analysis. Get optimization suggestions: Collect optimization suggestions for all solutions and parse the required associated data types. Synchronize associated data: According to resource information and the data types required by the optimization suggestions, synchronize data from various data sources and put them into the cache for fast access. Sort optimization suggestions: Prioritize the optimization suggestions according to the preset standards. Traverse resource analysis: Analyze each resource in the order of optimization suggestions to determine whether there are optimization opportunities. Analysis result judgment: If the analysis result shows that the current resource configuration does not meet the optimization suggestion, continue to analyze other optimization suggestions in sequence. Return partial analysis results: If the analysis results meet the optimization suggestions and the solution configuration does not require a full analysis, return this part of the analysis results. Return full analysis results: If the solution is configured with full analysis, even if some analysis results already meet the optimization requirements, all suggestions should continue to be analyzed, and finally all analysis results that meet the optimization requirements are returned.

[0134] The recommended analysis process is as follows: Get related data: Get the related data corresponding to the current resource from the cache according to the data type required by the optimization recommendation. Determine the optimization conditions: Determine whether the current resource meets the optimization conditions according to the optimization recommendation rules. Generate a recommended solution: If the resource meets the optimization conditions, generate a recommended solution according to the recommendation rules, and calculate the cost of the current resource, the cost of the recommended solution, and the cost difference. Return analysis results: Return analysis results including cost analysis and recommended solutions. If the resource does not meet the optimization conditions, return the result that does not meet the optimization.

[0135] The two processes are interrelated. The solution analysis process determines which optimization recommendations should be considered, while the recommendation analysis process conducts a detailed analysis of each optimization recommendation to determine whether it can be implemented and the effect after implementation. This hierarchical analysis approach helps ensure that the FinOps solution can comprehensively evaluate all possible optimization opportunities while providing specific, data-driven recommendations.

[0136] The present application also provides an electronic device. Figure 8 The electronic device includes a memory 902 and a processor 901. The memory 902 stores a computer program. When the processor 901 executes the computer program, the cloud cost management method is implemented. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0137] The processor 901 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solution provided in the embodiment of the present application; the memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the cloud cost management method of the embodiment of the present application is called and executed by the processor 901.

[0138] The input / output interface 903 is used to realize information input and output; the communication interface 904 is used to realize communication interaction between this device and other devices, and communication can be realized through wired methods (such as USB, network cable, etc.) or wireless methods (such as mobile network, WI FI, Bluetooth, etc.); the bus 905 transmits information between the various components of the device (such as the processor 901, the memory 902, the input / output interface 903 and the communication interface 904); the processor 901, the memory 902, the input / output interface 903 and the communication interface 904 realize communication connection with each other within the device through the bus 905.

[0139] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned cloud cost management method is implemented.

[0140] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0141] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0142] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0143] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0145] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0146] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0147] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0148] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0149] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0150] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially 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, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0151] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A cloud cost management method, characterized in that: include: Get data about cloud products; Synchronize the resources owned by the user corresponding to the cloud product; Analyze the data of the cloud product to obtain target optimization recommendation information; According to the data types associated with the resource and the target optimization suggestion information, acquiring associated data from a data source corresponding to the cloud product; When the associated data meets the optimization condition of the optimization suggestion information, the cost difference of the cloud product before and after the optimization is calculated.

2. The cloud cost management method according to claim 1, characterized in that: The step of analyzing the data of the cloud product to obtain target optimization suggestion information includes: Analyze the data of the cloud product to obtain multiple candidate optimization suggestion information; According to the priority of the candidate optimization suggestion information, the candidate optimization suggestion information is analyzed according to the resource to obtain an analysis result; The candidate optimization suggestion information for which the analysis result indicates that there is an optimization opportunity is selected as the target optimization suggestion information.

3. The cloud cost management method according to claim 2, characterized in that: The analyzing the candidate optimization suggestion information according to the priority of the candidate optimization suggestion information and according to the resource to obtain the analysis result includes: When the analysis requirement is local analysis, the candidate optimization suggestion information is analyzed one by one according to the priority of the candidate optimization suggestion information and the resource, until the first candidate optimization suggestion information for which the analysis result indicates that there is an optimization opportunity is detected; The first analysis result outputs the analysis result of candidate optimization recommendation information indicating the existence of optimization opportunities.

4. The cloud cost management method according to claim 2, characterized in that: The analyzing the candidate optimization suggestion information according to the priority of the candidate optimization suggestion information and according to the resource to obtain the analysis result includes: When the analysis requirement is a comprehensive analysis, all of the candidate optimization suggestion information are analyzed according to the priority of the candidate optimization suggestion information and the resources to obtain analysis results of all of the candidate optimization suggestion information.

5. The cloud cost management method according to claim 1, characterized in that: When the associated data meets the optimization condition of the optimization suggestion information, the cost difference of the cloud product before and after the optimization is calculated, including: When the associated data meets the optimization condition of the optimization suggestion information, a recommendation scheme is generated according to the recommendation rule; The cost difference of the cloud product before and after optimization is calculated based on the recommended solution.

6. The cloud cost management method according to claim 1, characterized in that: The cost difference of the cloud product before and after optimization is calculated according to the recommended solution, including: The first cost of the cloud product before optimization is calculated based on the resources corresponding to the cloud product before optimization; Calculate the second cost of the cloud product after optimization based on the resources corresponding to the recommended solution; The cost difference of the cloud product before and after the optimization is obtained according to the first cost and the cost calculation.

7. The cloud cost management method according to claim 1, characterized in that: The associated data is stored in a cache of the cloud server.

8. A cloud cost management device, characterized in that: include: Data input module, used to obtain data of cloud products; A resource synchronization module, used to synchronize the resources owned by the user corresponding to the cloud product; A solution analysis module is used to analyze the data of the cloud product to obtain target optimization suggestion information; A related data acquisition module, configured to acquire related data from a data source corresponding to the cloud product according to the type of related data between the resource and the target optimization suggestion information; The suggestion analysis module is used to calculate the cost difference of the cloud product before and after optimization when the associated data meets the optimization conditions of the optimization suggestion information.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the cloud cost management method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the cloud cost management method according to any one of claims 1 to 7 is implemented.

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