Cost optimization method and device for hybrid cloud, and electronic equipment

Abstract

The embodiment of the invention discloses a hybrid cloud cost optimization method and device, belongs to the technical field of edge computing, and can solve the problem of how to realize automatic cost management in a multi-cloud scene. Comprising the steps that cloud resource costs of cloud products corresponding to multiple cloud resources are acquired, a cost bill is generated, the cost bill is used for recording data of the cloud resource costs in real time, the cloud resources comprise public clouds and / or private clouds, and one cloud resource corresponds to multiple cloud products; collecting performance data in the cloud management platform, wherein the performance data is used for representing the consumption condition of the cloud host under the cloud product; the cost bill and the performance data are input into a pre-trained cost optimization model, a value value corresponding to each cloud product is output, and the cost optimization model is used for calculating the value value of each cloud product; and a cost optimization strategy is determined based on the value value and a configuration rule of the cloud product, and the configuration rule is used for determining the cost pricing of the cloud product.

Description

Technical Field

[0001] The present application relates to the field of edge computing technology, and in particular to a hybrid cloud cost optimization method, device, and electronic device. Background Art

[0002] Currently, cloud cost management mainly relies on the billing information provided by cloud service providers. Cloud services are billed at a certain period, and users can view cost bills by logging into the cloud service provider's management platform.

[0003] Among the related technologies, cloud cost management technology can only provide billing measurement for each cloud, and lacks a unified perspective for cost management in multi-cloud scenarios. Users can only grasp the overall cost trend and coarse-grained cost structure based on the information, which provides insufficient information for cost optimization and makes it difficult to solve the problem of how to achieve automatic cost management in multi-cloud scenarios. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a hybrid cloud cost optimization method, device and electronic device to solve the problem of how to achieve automatic cost management in a multi-cloud scenario.

[0005] To solve the above technical problems, the embodiments of the present application are implemented as follows: In a first aspect, an embodiment of the present application provides a hybrid cloud cost optimization method, which is applied to a cloud management platform, including: obtaining cloud resource costs of cloud products corresponding to multiple cloud resources, and generating a cost bill, wherein the cost bill is used to record the cloud resource cost data in real time, wherein the cloud resources include public clouds and / or private clouds, wherein one cloud resource corresponds to multiple cloud products; collecting performance data in the cloud management platform, wherein the performance data is used to characterize the consumption of the cloud host when using the cloud products; inputting the cost bill and the performance data into a pre-trained cost optimization model, and outputting the value values ​​corresponding to each of the cloud products, wherein the cost optimization model is used to calculate the value values ​​of each of the cloud products; determining a cost optimization strategy based on the value values ​​and the configuration rules of the cloud products, wherein the configuration rules are used to determine the cost pricing of the cloud products.

[0006] In a second aspect, an embodiment of the present application provides a hybrid cloud cost optimization device, comprising: an acquisition module, used to acquire the cloud resource costs of cloud products corresponding to multiple cloud resources, and generate a cost bill, wherein the cost bill is used to record the data of the cloud resource costs in real time, wherein the cloud resources include public clouds and / or private clouds, wherein one cloud resource corresponds to multiple cloud products; a collection module, used to collect performance data in a cloud management platform, wherein the performance data is used to characterize the consumption of the cloud host when using the cloud products; an output module, used to input the cost bill and the performance data into a pre-trained cost optimization model, and output the value values ​​corresponding to each of the cloud products, wherein the cost optimization model is used to calculate the value values ​​of each of the cloud products; a determination module, used to determine a cost optimization strategy based on the value values ​​and the configuration rules of the cloud products, wherein the configuration rules are used to determine the cost pricing of the cloud products.

[0007] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory electrically connected to the processor, the memory storing a computer program, and the processor being used to call and execute the computer program from the memory to implement the above-mentioned hybrid cloud cost optimization method.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program can be executed by a processor to implement the above-mentioned hybrid cloud cost optimization method.

[0009] In a fifth aspect, an embodiment of the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the above-mentioned hybrid cloud cost optimization method.

[0010] In a sixth aspect, an embodiment of the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the above-mentioned hybrid cloud cost optimization method.

[0011] The technical solution of the embodiment of the present application is adopted and applied to a cloud management platform to obtain the cloud resource costs of cloud products corresponding to multiple cloud resources and generate a cost bill. The cost bill is used to record the cloud resource cost data in real time. The cloud resources include public clouds and / or private clouds, wherein one cloud resource corresponds to multiple cloud products; performance data in the cloud management platform is collected, and the performance data is used to characterize the consumption of cloud hosts when using cloud products; the cost bill and performance data are input into a pre-trained cost optimization model, and the value values ​​corresponding to each cloud product are output. The cost optimization model is used to calculate the value value of each cloud product; based on the value values ​​and the configuration rules of the cloud products, a cost optimization strategy is determined, and the configuration rules are used to determine the cost pricing of the cloud products. It can be seen that the cloud management platform can obtain the cost bill of hybrid clouds including public clouds and / or private clouds in real time, and collect performance data. Through the pre-trained cost optimization model, it outputs the value value corresponding to each cloud product, and determines the cost optimization strategy based on the value value and the configuration rules of the cloud product. It can not only automatically calculate the cost of a single cloud, but also the cost of hybrid cloud scenarios, and manage the cost of hybrid clouds in combination with the value value and cost pricing of cloud products to select a more cost-effective cloud environment, which can solve the problem of realizing automatic cost management in multi-cloud scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate one or more embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in one or more embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0013] Figure 1 is a schematic flow chart of a hybrid cloud cost optimization method according to an embodiment of the present application; Figure 2 is a structural schematic diagram of a hybrid cloud cost optimization method according to an embodiment of the present application; Figure 3 is a schematic flow chart of a hybrid cloud cost optimization method according to another embodiment of the present application; Figure 4 is a schematic block diagram of a hybrid cloud cost optimization device according to an embodiment of the present application; Figure 5 It is a schematic diagram of the hardware structure of a hybrid cloud cost optimization device according to an embodiment of the present application. DETAILED DESCRIPTION

[0014] The embodiments of the present application provide a hybrid cloud cost optimization method, device, and electronic device to solve the problem of how to achieve automatic cost management in a multi-cloud scenario.

[0015] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0016] The hybrid cloud cost optimization method provided in the embodiment of the present application can be executed by an electronic device or by software installed in the electronic device. Specifically, the electronic device can be a terminal device or a server device. The terminal device can include a smart phone, a laptop computer, a smart wearable device, a vehicle terminal, etc. The server device can include an independent physical server, a server cluster composed of multiple servers, or a cloud server capable of cloud computing.

[0017] In the following, in conjunction with the accompanying drawings, a hybrid cloud cost optimization method provided by an embodiment of the present application is described in detail through specific embodiments and application scenarios.

[0018] Figure 1 A schematic flow chart of a hybrid cloud cost optimization method provided by an embodiment of the present invention is shown, which is applied to a cloud management platform. The method includes the following steps: S102, obtaining cloud resource costs of cloud products corresponding to the plurality of cloud resources, and generating a cost bill, the cost bill being used to record cloud resource cost data in real time.

[0019] Cloud resources include public clouds and / or private clouds, where one cloud resource corresponds to multiple cloud products.

[0020] Since cloud resources include public clouds and / or private clouds, it can be known that cloud resources can be a single cloud or a hybrid cloud.

[0021] Cloud products include Alibaba Cloud products, Huawei Cloud products and other different types of products, covering computing, storage, networking, security and multiple application scenarios, and can meet the diverse needs of different enterprises and developers.

[0022] Different cloud products have different costs. Cloud resource costs include computing costs, storage costs, and network costs, which are used to calculate the real-time costs of cloud products. Specifically, cloud resource costs are calculated based on the activation time and product specifications of cloud products. Since cloud products are used in real time, the cloud resource cost data changes all the time and is recorded, such as recording the corresponding cost price according to product specifications. The data on cloud resource costs of cloud products recorded in real time is archived and saved daily to generate a cost bill.

[0023] S104, collecting performance data in the cloud management platform.

[0024] Performance data is used to characterize the consumption of cloud hosts when using cloud products.

[0025] Specifically, the performance data includes the central processing unit (CPU) utilization, memory utilization, network traffic, input / output (IO) waiting time, etc. corresponding to each cloud host in the cloud management platform.

[0026] S106, input the cost bill and performance data into the pre-trained cost optimization model, and output the value value corresponding to each cloud product.

[0027] The cost optimization model is used to calculate the value of each cloud product.

[0028] The cost optimization model is a simple neural network, which is a model obtained through deep learning training.

[0029] The value value includes inputting the cost bill and performance data into the pre-trained cost optimization model, and then calculating the value value output by the cost optimization model. Each cloud product has its own value value. The value value can be a single value or an interval value.

[0030] S108, determining a cost optimization strategy based on the value value and the configuration rules of the cloud product.

[0031] Among them, configuration rules are used to determine the cost pricing of cloud products.

[0032] Cost pricing includes the pricing of cloud products by cloud service providers for different product specifications. Based on value data, the billing of each cloud product is determined. Based on the configuration rules of cloud products, the cost pricing of each cloud product is determined. Based on cost pricing and usage value data, the cost optimization strategy is determined.

[0033] Cost optimization strategies include a variety of suggestions for optimizing costs, such as selecting a target cloud product with a higher cost-performance ratio based on the cost pricing of the cloud product and the value generated during use.

[0034] The technical solution of the embodiment of the present application is adopted and applied to a cloud management platform to obtain the cloud resource costs of cloud products corresponding to multiple cloud resources and generate a cost bill. The cost bill is used to record the cloud resource cost data in real time. The cloud resources include public clouds and / or private clouds, wherein one cloud resource corresponds to multiple cloud products; performance data in the cloud management platform is collected, and the performance data is used to characterize the consumption of cloud hosts when using cloud products; the cost bill and performance data are input into a pre-trained cost optimization model, and the value values ​​corresponding to each cloud product are output. The cost optimization model is used to calculate the value value of each cloud product; based on the value values ​​and the configuration rules of the cloud products, a cost optimization strategy is determined, and the configuration rules are used to determine the cost pricing of the cloud products. It can be seen that the cloud management platform can obtain the cost bill of hybrid clouds including public clouds and / or private clouds in real time, and collect performance data. Through the pre-trained cost optimization model, it outputs the value value corresponding to each cloud product, and determines the cost optimization strategy based on the value value and the configuration rules of the cloud product. It can not only automatically calculate the cost of a single cloud, but also the cost of hybrid cloud scenarios, and manage the cost of hybrid clouds in combination with the value value and cost pricing of cloud products to select a more cost-effective cloud environment, which can solve the problem of realizing automatic cost management in multi-cloud scenarios.

[0035] In one embodiment, before obtaining the cloud resource costs of cloud products corresponding to the plurality of cloud resources and generating a cost bill (ie, S102), the following steps A1-A3 may be performed: Step A1: Obtain the cloud service provider pricing of each cloud product, and aggregate the cloud service provider pricing of each cloud product into a cost unit.

[0036] The cost unit is used to record the cost pricing data of each cloud product.

[0037] It should be understood that the cloud management platform sets pricing models for cloud hosts, bare metal, enterprise information portals (EIP), infrastructure and other product types, and can set pricing models of mainstream cloud service providers such as product specifications or single instances of cloud products. Among them, the pricing model includes: determining the most appropriate price based on multiple factors, including product specifications, single instances, user needs, competition, market positioning, etc.

[0038] Among them, the cloud service provider pricing for each cloud product obtained by the cloud service provider is aggregated into the cost unit. Specifically, for product types such as cloud host and bare metal, the cloud service provider pricing for each cloud product is aggregated into the cost unit according to the product specifications or atomic computing units of cloud products on different clouds.

[0039] Cost pricing includes cloud service provider pricing for cloud products. The cost unit can record the cost pricing data for each cloud product.

[0040] Step A2, configure the allocation rules for the corresponding public infrastructure in the cloud management platform. The allocation rules are used to allocate the expenses of the public infrastructure to the corresponding cloud products to obtain the allocated cost of each cloud product.

[0041] In addition to the cloud service provider pricing of cloud products, the cloud management platform also includes the prices of public infrastructure. For the prices of public equipment, you can configure allocation rules to allocate the costs of public infrastructure to the corresponding cloud products, so that the cost pricing of each cloud product also includes the allocated costs of public infrastructure. Specifically, for product types such as EIP, infrastructure (racks, network equipment, security equipment), configure different allocation rules to obtain the corresponding allocated costs for each cloud product, and record the allocated costs in the cost unit.

[0042] Step A3: The cloud service provider pricing and the allocated costs of the cloud product are aggregated into cost units as cost pricing, and the configuration rules of the cloud product are determined based on the cost units.

[0043] Configuration rules include allocation rules for cloud service provider pricing of cloud products and allocation rules for allocating public infrastructure to cloud products.

[0044] The cost unit forms a product catalog that records the cost pricing of cloud products. The cost pricing includes the cloud service provider pricing and shared costs of cloud products.

[0045] In this embodiment, the cloud service provider pricing of each cloud product is recorded in the cost unit. Based on the product catalog of various cloud products in the hybrid cloud, the resource costs of public infrastructure equipment are also allocated to the cost unit through configuration rules. Therefore, the data combining the cloud service provider pricing and the allocated costs is recorded in the cost unit as the cost pricing of the cloud product, which enables more refined management of cloud products for subsequent active calculation of the cost bill of the cloud product.

[0046] In one embodiment, the performance data in the cloud management platform is collected (ie, S104), and the following steps B1-B3 may be performed: Step B1, regularly collect network management performance data in the network management system corresponding to the cloud management platform.

[0047] The network management system includes systems that monitor and manage network equipment, reflect and record network performance and abnormal conditions. It can help network administrators understand network conditions in real time, and quickly discover and solve network problems, thereby ensuring the stable operation of the network.

[0048] A cloud management platform includes one or more network management systems, and collects network management performance data from the network management system at a preset time of one day or one week.

[0049] The network management performance data is used to record the usage and consumption of the cloud host corresponding to each cloud product in the cloud management platform, such as CPU utilization, memory utilization, network traffic, IO waiting time, etc.

[0050] Step B2: Format and convert the network management performance data using the network management system, and remove redundant data using a data fluctuation monitoring algorithm to obtain processed data.

[0051] Data fluctuation monitoring algorithms are used to detect and identify abnormal or sudden fluctuations in time series data caused by factors such as equipment failures, abnormal events, and market changes. Commonly used methods include statistical methods, machine learning methods, and time series analysis methods.

[0052] Through the network management system, the network management performance data is formatted and converted to obtain data with a fixed format and sequence, and the data fluctuation monitoring algorithm is used to remove redundant data, filter fault content data, clean up redundant data, and perform other denoising processes on the network management performance data to obtain processed data.

[0053] It should be noted that the peripheral system includes the network management system and other systems outside the network management system. At the same time, the peripheral system can also format and convert the peripheral performance data to obtain data with a fixed format and order, and use data fluctuation monitoring algorithms to clear redundant data, filter fault content data, clean up redundant data, and perform other denoising processes on the peripheral performance data.

[0054] The data related to the cloud management platform in the processed network management performance data and peripheral performance data is determined as the processed data. Generally, the corresponding network management performance data in the network management system is the data related to the cloud management platform.

[0055] Step B3, integrate the processed data according to the time dimension to obtain performance data in the cloud management platform.

[0056] The processed performance data is archived and backed up, and sorted and integrated according to the time dimension to obtain the performance data corresponding to the cloud products in the cloud management platform. The performance data can represent the consumption of cloud hosts using cloud products in the cloud management platform.

[0057] Specifically, a first task can be set, which is used to collect data provided by peripheral systems and gateway systems, and format the data, filter fault content and clean up redundant data through a data fluctuation monitoring algorithm to obtain processed data, archive and back up the processed data, aggregate and integrate them according to the time dimension, obtain performance data in the cloud management platform, and output the performance data to the second task. The second task can be to input the performance data and cost bill into a pre-trained cost optimization model for calculation by scheduling resources.

[0058] In this embodiment, the network management performance data in the network management system of the cloud management platform is collected regularly, and the network management performance data is converted and processed, and the data fluctuation monitoring algorithm is used to monitor the change state of the data, and the redundant data is cleared to obtain the performance data in the cloud management platform. It is possible to more accurately control the consumption and changes of the cloud host using the cloud product.

[0059] In one embodiment, the cost bill and performance data are input into a pre-trained cost optimization model, and the value value corresponding to each cloud product is output (ie, S106), and the following step C may be performed: Step C: Input the cost bill and performance data into the pre-trained cost optimization model, and calculate the value of each cloud product based on the regression algorithm in the cost optimization model.

[0060] By scheduling resources, the cost bill and performance data of cloud products are input into the pre-trained cost optimization model. The cost optimization model includes a regression algorithm, which is used to calculate the value of cloud products.

[0061] Regression algorithm is a prediction method in machine learning, which is used to predict continuous-valued target variables. Among them, linear regression is one of the most basic regression algorithms, which predicts the target variable by finding the best fit line between data points.

[0062] In this embodiment, the cost bill and performance data of cloud products are obtained by scheduling resources, input into the pre-trained cost optimization model, and the value of cloud products is calculated and output through the regression algorithm. It can intelligently schedule tasks to calculate the cost and performance data of cloud products, determine the value value by combining the regression algorithm, and intelligently predict the value benefits of cloud products to provide a basis for cloud resource optimization.

[0063] In one embodiment, the method further includes training the cost optimization model, and specifically the following steps D1-D3 may be performed: Step D1, detecting the online status of multiple sample cloud products, and when the sample cloud products are online, obtaining sample cost bills, sample performance data, and current value values ​​of the multiple sample cloud products.

[0064] Detect the online status of multiple sample cloud products, that is, whether the cloud products are used. When the sample cloud products are online, obtain sample cost bills, sample performance data, and current value values ​​of the multiple sample cloud products.

[0065] Step D2, inputting the sample cost bill and the sample performance data into the cost optimization model to be trained, and calculating the sample value of the sample cloud product based on the regression algorithm in the cost optimization model to be trained.

[0066] The sample cost bills and sample performance data in the cost optimization model to be trained are used as training sets. New features are generated through polynomial features, logarithmic transformation and other methods. The sample value values ​​of the sample cloud products corresponding to the new features are calculated through the regression algorithm of the neural network, and the cost optimization model to be trained is trained.

[0067] Step D3, based on the sample value and the current value, the cost optimization model to be trained is trained to obtain a trained cost optimization model.

[0068] According to the sample value and the current value, adjust the parameters of the cost optimization model to be trained to optimize the model performance, and finally make the sample value and the current value consistent. Deploy the trained cost optimization model to the production environment for real-time prediction of resource optimization strategies.

[0069] In this embodiment, by training the cost optimization model, the cost bill and performance data of the cloud product can be intelligently calculated, and the value value can be determined by combining the regression algorithm, so that the corresponding cost optimization strategy can be taken in real time based on the predicted value value of the cloud product.

[0070] In one embodiment, based on the value value and the configuration rules of the cloud product, a cost optimization strategy is determined (ie, S108), and the following steps E1-E3 may be performed: Step E1: Determine the cost pricing of the cloud product according to the configuration rules of the cloud product.

[0071] The configuration rules for cloud products include the allocation rules for cloud service provider pricing of cloud products and the allocation rules for allocating public infrastructure to cloud products. The cloud service provider pricing and allocated costs of cloud products will be determined through the configuration rules, and the cost pricing for cloud products will be actively calculated and determined based on the cloud service provider pricing and allocated costs, without having to wait for the cloud service provider to push the bill.

[0072] Step E2: Determine the value and benefit of the cloud product based on the value and cost of the cloud product.

[0073] The value and benefit of cloud products are determined based on their value and cost pricing. The value and benefit of cloud products include the relationship between the cost and value of using cloud products. For example, the cost and value of cloud products are obtained. When the cost is high and the value is low, it means that the value and benefit of cloud products are low.

[0074] Step E3: Determine the cost optimization strategy based on the value of cloud products.

[0075] Cost optimization strategy, including selecting reasonable target cloud products based on the value benefits of cloud products.

[0076] It should be noted that by regularly calculating the value data of each cloud product, the optimal deployment cloud resource pool is generated to provide a migration strategy for cloud products. When new cloud products are added subsequently, it can be prompted to create resources on other nodes.

[0077] In this embodiment, the cost bills and performance data of cloud product settlement are integrated, the value of cloud products is calculated using a regression algorithm, and the product value benefits are determined based on the cost pricing of cloud products. A cost optimization strategy is selected based on the product value benefits to optimize cloud resources.

[0078] In one embodiment, based on the value of cloud products, a cost optimization strategy is determined (i.e., step E3), and the following step F may be performed: Step F: When the value of the cloud product value benefit is lower than a preset normal value, a cost optimization strategy report is generated for downsizing or retiring the cloud product.

[0079] The preset normal value is the range of value benefits generated by cloud products corresponding to cloud resources based on historical experience. When the value of the cloud product is lower than the preset normal value, it is necessary to take measures such as downsizing the cloud product, or terminate the lease of the cloud product and choose other cloud products. A cost optimization strategy report is generated based on the treatment plan to show users the selection recommendations for cloud products.

[0080] When the value of the cloud product value benefit is higher than or equal to the preset normal value, a cost optimization strategy report is generated for not changing the cloud product or changing it to a target cloud product that is better than the current cloud product.

[0081] Preferably, according to the cost pricing and optimization status of each cloud product recorded in each cost unit, the effectiveness of cost optimization management is analyzed and cost optimization strategy reports are provided regularly.

[0082] In this embodiment, when the value of the cloud product value benefit is lower than the preset normal value, a cost optimization strategy report is generated for downsizing or terminate the cloud product. Different cost optimization strategies can be adopted for different situations, and corresponding cost optimization strategy reports can be generated.

[0083] In one embodiment, Figure 2 It is a structural diagram of the cost optimization method of hybrid cloud, such as Figure 2 As shown, it includes a cloud management platform and peripheral systems outside the cloud management platform system. The peripheral systems include a network management system that records the performance data of each cloud product in the cloud management platform. In the cloud management platform, first, according to the configuration rules, the pricing of the host cloud products and infrastructure products is determined, and the cost pricing of multi-cloud products is actively calculated and determined. Multi-cloud products include hybrid cloud products. Then, based on the cost pricing, the cloud resource costs of the cloud products opened according to business needs are actively settled to obtain a cost bill. For each resource instance, that is, cloud product, performance data is collected from the network management system, and the collected performance data and cost bill are input into the cost optimization model, and data matching calculation is performed to obtain the predicted value of the cloud product. Based on the value and configuration rules of each cloud product, a cost optimization strategy is generated, and the information of the cost optimization strategy is recorded in the database of the peripheral system, and the cost optimization strategy is displayed in the form of a generated cost optimization strategy report.

[0084] In this embodiment, for multi-cloud products including hybrid cloud, the cost pricing of multi-cloud products can be actively calculated through configuration rules, and the cost bills of each cloud product can be actively settled based on the configuration rules, breaking the barriers of each cloud environment without waiting for the cloud service provider to push the bill. At the same time, based on the calculated value of multi-cloud products, a cost optimization strategy for hybrid cloud is provided, not just reflected in a single cloud, which can solve the problem of realizing automatic cost management in multi-cloud scenarios.

[0085] Figure 3 is a schematic flow chart of a hybrid cloud cost optimization method according to another embodiment of the present application, such as Figure 3 As shown, the method comprises the following steps: S301, according to the configuration rules of each cloud product under different cloud resources in the cloud management platform, obtain the cloud service provider pricing of the cloud product, and allocate the cost of the public infrastructure to the corresponding cloud products to obtain the allocated cost of each cloud product.

[0086] S302, recording the cloud service provider's pricing and allocated costs as the cost pricing of the cloud product in the cost unit.

[0087] S303: Obtain cloud resource costs of cloud products corresponding to the multiple cloud resources and generate a cost bill.

[0088] S304, regularly collect network management performance data of each cloud product in the cloud management platform.

[0089] S305, the network management system performs data conversion and denoising on the network management performance data to obtain performance data of each cloud product.

[0090] S306: Input the cost bill and performance data corresponding to each cloud product into the pre-trained cost optimization model, and use the regression algorithm to calculate and output the value value corresponding to each cloud product.

[0091] S307, determining the value benefit of each cloud product based on the value value of each cloud product and the cost pricing recorded in the cost unit.

[0092] S308: Determine a cost optimization strategy based on the value benefits of each cloud product and generate a cost optimization strategy report.

[0093] The specific process from S301 to S308 has been described in detail in the above embodiment and will not be repeated here.

[0094] The technical solution of the embodiment of the present application is adopted and applied to a cloud management platform to obtain the cloud resource costs of cloud products corresponding to multiple cloud resources and generate a cost bill. The cost bill is used to record the cloud resource cost data in real time. The cloud resources include public clouds and / or private clouds, wherein one cloud resource corresponds to multiple cloud products; performance data in the cloud management platform is collected, and the performance data is used to characterize the consumption of cloud hosts when using cloud products; the cost bill and performance data are input into a pre-trained cost optimization model, and the value values ​​corresponding to each cloud product are output. The cost optimization model is used to calculate the value value of each cloud product; based on the value values ​​and the configuration rules of the cloud products, a cost optimization strategy is determined, and the configuration rules include the cost pricing of cloud products. It can be seen that the cloud management platform can obtain the cost bill of hybrid clouds including public clouds and / or private clouds in real time, and collect performance data. Through the pre-trained cost optimization model, it outputs the value value corresponding to each cloud product, and determines the cost optimization strategy based on the value value and the configuration rules of the cloud product. It can not only automatically calculate the cost of a single cloud, but also the cost of hybrid cloud scenarios, and manage the cost of hybrid clouds in combination with the value value and cost pricing of cloud products to select a more cost-effective cloud environment, which can solve the problem of realizing automatic cost management in multi-cloud scenarios.

[0095] In summary, specific embodiments of the present subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recorded in the claims can be performed in a different order and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing can be advantageous.

[0096] The above is a hybrid cloud cost optimization method provided in an embodiment of the present application. Based on the same idea, an embodiment of the present application also provides a hybrid cloud cost optimization device.

[0097] Figure 4 Schematic diagram of a hybrid cloud cost optimization device according to an embodiment of the present invention. Figure 4 As shown, the hybrid cloud cost optimization device includes: an acquisition module 41, a collection module 42, an output module 43, and a determination module 44: An acquisition module 41 is used to acquire cloud resource costs of cloud products corresponding to multiple cloud resources and generate a cost bill, where the cost bill is used to record cloud resource cost data in real time. Cloud resources include public clouds and / or private clouds, where one cloud resource corresponds to multiple cloud products. The collection module 42 is used to collect performance data in the cloud management platform, and the performance data is used to characterize the consumption of the cloud host when using the cloud product; An output module 43 is used to input the cost bill and performance data into a pre-trained cost optimization model, and output the value value corresponding to each cloud product. The cost optimization model is used to calculate the value value of each cloud product. The determination module 44 is used to determine the cost optimization strategy based on the value value and the configuration rules of the cloud product, and the configuration rules are used to determine the cost pricing of the cloud product.

[0098] In one embodiment, the device further includes an apportionment module, specifically including: The acquisition unit is used to obtain the cloud service provider pricing of each cloud product and aggregate the cloud service provider pricing of each cloud product into the cost unit. The cost unit is used to record the cost pricing data of each cloud product. The allocation unit is used to configure allocation rules for the corresponding public basic equipment in the cloud management platform. The allocation rules are used to allocate the expenses of the public basic equipment to the corresponding cloud products to obtain the allocation cost of each cloud product; The aggregation unit is used to aggregate the cloud service provider pricing and allocated costs of cloud products into the cost unit as cost pricing, and determine the configuration rules of cloud products based on the cost unit.

[0099] In one embodiment, the collection module 42 is specifically used to periodically collect network management performance data in the network management system corresponding to the cloud management platform; use the network management system to format and convert the network management performance data, and obtain processed data after removing redundant data through a data fluctuation monitoring algorithm; integrate the processed data according to the time dimension to obtain performance data in the cloud management platform.

[0100] In one embodiment, the output module 43 is specifically used to input the pre-trained cost optimization model according to the cost bill and performance data, and calculate the value of each cloud product according to the regression algorithm in the cost optimization model.

[0101] In one embodiment, the device further includes a training module for training the cost optimization model, specifically including: A detection unit is used to detect the online status of multiple sample cloud products, and when the sample cloud products are online, obtain sample cost bills, sample performance data, and current value values ​​of the multiple sample cloud products; A calculation unit, used for inputting the sample cost bill and the sample performance data into the cost optimization model to be trained, and calculating the sample value of the sample cloud product based on the regression algorithm in the cost optimization model to be trained; The training unit is used to train the cost optimization model to be trained based on the sample value and the current value to obtain the trained cost optimization model.

[0102] In one embodiment, the determination module 44 is specifically used to: determine the cost pricing of the cloud product according to the configuration rules of the cloud product; determine the value benefit of the cloud product according to the value value and cost pricing of the cloud product; and determine the cost optimization strategy based on the value benefit of the cloud product.

[0103] In one embodiment, the determination module 44 is further specifically used to generate a cost optimization strategy report for downsizing or retiring the cloud product when the value of the cloud product value benefit is lower than a preset normal value.

[0104] The technical solution of the embodiment of the present application is adopted and applied to a cloud management platform to obtain the cloud resource costs of cloud products corresponding to multiple cloud resources and generate a cost bill. The cost bill is used to record the cloud resource cost data in real time. The cloud resources include public clouds and / or private clouds, wherein one cloud resource corresponds to multiple cloud products; performance data in the cloud management platform is collected, and the performance data is used to characterize the consumption of cloud hosts when using cloud products; the cost bill and performance data are input into a pre-trained cost optimization model, and the value values ​​corresponding to each cloud product are output. The cost optimization model is used to calculate the value value of each cloud product; based on the value values ​​and the configuration rules of the cloud products, a cost optimization strategy is determined, and the configuration rules include the cost pricing of cloud products. It can be seen that the cloud management platform can obtain the cost bill of hybrid clouds including public clouds and / or private clouds in real time, and collect performance data. Through the pre-trained cost optimization model, it outputs the value value corresponding to each cloud product, and determines the cost optimization strategy based on the value value and the configuration rules of the cloud product. It can not only automatically calculate the cost of a single cloud, but also the cost of hybrid cloud scenarios, and manage the cost of hybrid clouds in combination with the value value and cost pricing of cloud products to select a more cost-effective cloud environment, which can solve the problem of realizing automatic cost management in multi-cloud scenarios.

[0105] Those skilled in the art should understand that Figure 4 The hybrid cloud cost optimization device in the present invention can be used to implement the hybrid cloud cost optimization method described above, and the detailed description thereof should be similar to that described in the method section above, and will not be further described here to avoid redundancy.

[0106] Based on the same technical concept, an embodiment of the present application further provides an electronic device, which is used to execute the above hybrid cloud cost optimization method. Figure 5 A schematic diagram of the structure of an electronic device for implementing various embodiments of the present application. The electronic device may have relatively large differences due to different configurations or performances, and may include a processor (processor) 510, a communication interface (Communications Interface) 520, a memory (memory) 530 and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call a computer program stored in the memory 530 and executable on the processor 510 to perform the following steps: Obtain cloud resource costs of cloud products corresponding to multiple cloud resources and generate a cost bill, which is used to record cloud resource cost data in real time. Cloud resources include public clouds and / or private clouds, where one cloud resource corresponds to multiple cloud products. Collect performance data from the cloud management platform. The performance data is used to characterize the consumption of cloud hosts when using cloud products. Input the cost bill and performance data into the pre-trained cost optimization model, and output the value value corresponding to each cloud product. The cost optimization model is used to calculate the value value of each cloud product. Determine the cost optimization strategy based on the value value and the configuration rules of the cloud products. The configuration rules are used to determine the cost pricing of the cloud products.

[0107] The technical solution of the embodiment of the present application is adopted and applied to a cloud management platform to obtain the cloud resource costs of cloud products corresponding to multiple cloud resources and generate a cost bill. The cost bill is used to record the cloud resource cost data in real time. The cloud resources include public clouds and / or private clouds, wherein one cloud resource corresponds to multiple cloud products; performance data in the cloud management platform is collected, and the performance data is used to characterize the consumption of cloud hosts when using cloud products; the cost bill and performance data are input into a pre-trained cost optimization model, and the value values ​​corresponding to each cloud product are output. The cost optimization model is used to calculate the value value of each cloud product; based on the value values ​​and the configuration rules of the cloud products, a cost optimization strategy is determined, and the configuration rules include the cost pricing of cloud products. It can be seen that the cloud management platform can obtain the cost bill of hybrid clouds including public clouds and / or private clouds in real time, and collect performance data. Through the pre-trained cost optimization model, it outputs the value value corresponding to each cloud product, and determines the cost optimization strategy based on the value value and the configuration rules of the cloud product. It can not only automatically calculate the cost of a single cloud, but also the cost of hybrid cloud scenarios, and manage the cost of hybrid clouds in combination with the value value and cost pricing of cloud products to select a more cost-effective cloud environment, which can solve the problem of realizing automatic cost management in multi-cloud scenarios.

[0108] The specific execution steps can refer to the various steps of the above hybrid cloud cost optimization method embodiment, and can achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0109] It should be noted that the electronic devices in the embodiments of the present application include: servers, terminals or other devices except terminals.

[0110] The above electronic device structure does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, the input unit may include a graphics processing unit (GPU) and a microphone, and the display unit may be configured with a display panel in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit includes a touch panel and at least one of other input devices. The touch panel is also called a touch screen. Other input devices may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0111] The memory can be used to store software programs and various data. The memory may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory may include a volatile memory or a non-volatile memory, or the memory may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct memory bus random access memory (DRRAM).

[0112] The processor may include one or more processing units; optionally, the processor integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to the operating system, user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor.

[0113] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned hybrid cloud cost optimization method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0114] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0115] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned hybrid cloud cost optimization method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0116] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0117] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the processor is used to run the program or instructions to implement the various processes of the above-mentioned product recommendation method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0118] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0119] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0120] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A hybrid cloud cost optimization method, applied to a cloud management platform, characterized in that: The method comprises: Obtaining cloud resource costs of cloud products corresponding to multiple cloud resources, and generating a cost bill, wherein the cost bill is used to record data of the cloud resource costs in real time, wherein the cloud resources include public clouds and / or private clouds, wherein one cloud resource corresponds to multiple cloud products; Collecting performance data in the cloud management platform, where the performance data is used to characterize the consumption of the cloud host when using the cloud product; Inputting the cost bill and the performance data into a pre-trained cost optimization model, and outputting a value value corresponding to each of the cloud products, wherein the cost optimization model is used to calculate the value value of each of the cloud products; A cost optimization strategy is determined based on the value value and configuration rules of the cloud product, wherein the configuration rules are used to determine the cost pricing of the cloud product.

2. The method according to claim 1, characterized in that The cloud resource costs of the cloud products corresponding to the multiple cloud resources are obtained and before the cost bill is generated, the following steps are included: Obtaining cloud service provider pricing for each of the cloud products, and aggregating the cloud service provider pricing for each of the cloud products into a cost unit, wherein the cost unit is used to record the cost pricing data for each of the cloud products; Configuring a sharing rule for the corresponding public infrastructure in the cloud management platform, wherein the sharing rule is used to share the cost of the public infrastructure to the corresponding cloud products to obtain the shared cost of each cloud product; The cloud service provider pricing and the allocated cost of the cloud product are aggregated into the cost unit as the cost pricing, and the configuration rule of the cloud product is determined based on the cost unit.

3. The method according to claim 1, characterized in that The collecting of performance data in the cloud management platform includes: Regularly collect network management performance data in the network management system corresponding to the cloud management platform; The network management performance data is formatted and converted by using a network management system, and redundant data is removed by using a data fluctuation monitoring algorithm to obtain processed data; The processed data is integrated according to the time dimension to obtain the performance data in the cloud management platform.

4. The method according to claim 1, characterized in that: The step of inputting the cost bill and the performance data into a pre-trained cost optimization model and outputting a value value corresponding to each of the cloud products includes: The cost bill and the performance data are input into a pre-trained cost optimization model, and the value value of each of the cloud products is calculated according to a regression algorithm in the cost optimization model.

5. The method according to claim 4, characterized in that The method further includes: training a cost optimization model: Detecting the online status of multiple sample cloud products, and when the sample cloud products are online, obtaining sample cost bills, sample performance data, and current value values ​​of the multiple sample cloud products; Inputting the sample cost bill and the sample performance data into the cost optimization model to be trained, and calculating the sample value of the sample cloud product based on the regression algorithm in the cost optimization model to be trained; Based on the sample value and the current value, the cost optimization model to be trained is trained to obtain the trained cost optimization model.

6. The method according to claim 1, characterized in that The determining of a cost optimization strategy based on the value value and the configuration rule of the cloud product includes: Determining the cost pricing of the cloud product according to the configuration rules of the cloud product; Determine the value benefit of the cloud product according to the value value and the cost pricing of the cloud product; The cost optimization strategy is determined based on the value benefit of the cloud product.

7. The method according to claim 1, characterized in that The determining the cost optimization strategy based on the value benefit of the cloud product includes: When the value of the cloud product value benefit is lower than a preset normal value, a cost optimization strategy report is generated for downsizing or retiring the cloud product.

8. A hybrid cloud cost optimization device, characterized in that: The device comprises: An acquisition module, used to acquire cloud resource costs of cloud products corresponding to multiple cloud resources, and generate a cost bill, wherein the cost bill is used to record the data of the cloud resource costs in real time, wherein the cloud resources include public clouds and / or private clouds, wherein one cloud resource corresponds to multiple cloud products; A collection module, used to collect performance data in the cloud management platform, wherein the performance data is used to characterize the consumption of the cloud host when using the cloud product; An output module, used to input the cost bill and the performance data into a pre-trained cost optimization model, and output a value value corresponding to each of the cloud products, wherein the cost optimization model is used to calculate the value value of each of the cloud products; A determination module is used to determine a cost optimization strategy based on the value value and a configuration rule of the cloud product, wherein the configuration rule is used to determine the cost pricing of the cloud product.

9. An electronic device, characterized in that: It includes a processor and a memory electrically connected to the processor, the memory stores a computer program, and the processor is used to call and execute the computer program from the memory to implement a hybrid cloud cost optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium is used to store a computer program, and the computer program can be executed by a processor to implement a hybrid cloud cost optimization method as described in any one of claims 1 to 7.

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