Cost optimization method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of cost management, in particular to a cost optimization method and device, computer equipment and a storage medium, and the method comprises the steps: determining a resource occupation level corresponding to a cloud server instance through employing a first deep learning algorithm after obtaining resource data of the cloud server instance, and obtaining the resource occupation level of the cloud server instance; and a cost optimization strategy is determined according to the resource occupation level, so that cost optimization of the cloud platform is completed according to the cost optimization strategy. According to the cost optimization process, the processes of data analysis, strategy formulation and execution are autonomously completed without manual participation, and the efficiency and accuracy of cost optimization are improved.

Description

Technical Field

[0001] This disclosure relates to the field of cost management technology, and in particular to a cost optimization method, apparatus, computer equipment, and storage medium. Background Technology

[0002] With the rapid development of cloud computing, more and more enterprises are migrating their mission-critical systems to cloud platforms, where cloud services have played a powerful role due to their speed, agility, and scalability. However, if used carelessly, these advantages can quickly become a burden. Without careful planning and close monitoring, computing resources can easily exceed the budget, leading to cloud computing cost overruns. Therefore, managing cloud costs is crucial.

[0003] Currently, cloud cost management lacks intelligent functions. Most products require users to manually analyze data and formulate optimization strategies, and do not have the ability to automatically identify, analyze and optimize. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a cost optimization method, apparatus, computer equipment, and storage medium.

[0005] In a first aspect, this disclosure provides a cost optimization method, including:

[0006] Obtain resource data corresponding to the cloud server instance; use the first deep learning algorithm to analyze the resource usage of the cloud server instance based on the resource data to obtain the resource usage level of the cloud server instance; determine the cost optimization strategy based on the resource usage level; and optimize the cost of the cloud platform based on the cost optimization strategy.

[0007] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0008] The cost optimization method provided in this embodiment, after obtaining the resource data of the cloud server instance, uses a first deep learning algorithm to determine the resource occupancy level of the cloud server instance, and determines the cost optimization strategy based on the resource occupancy level, thereby completing the cost optimization of the cloud platform according to the cost optimization strategy; the above cost optimization process autonomously completes the data analysis, strategy formulation and execution process without human intervention, improving the efficiency and accuracy of cost optimization.

[0009] In one optional implementation, obtaining resource data corresponding to the cloud server instance includes:

[0010] The instance identifier of the cloud server instance is obtained from the cloud platform. The instance identifier is set according to the preset specification. The instance identifier is divided according to the attributes to obtain the attribute data corresponding to each attribute. Resource data is read from the attribute data.

[0011] In an alternative implementation, the method further includes:

[0012] Obtain billing data from the cloud platform; perform anomaly analysis on the billing data using a second-depth algorithm; and display the anomalies in the billing data when the analysis results indicate anomalies.

[0013] In one alternative implementation, the second deep learning algorithm is trained using historical billing data and anomaly data labeled within the historical billing data.

[0014] In one optional implementation, determining a cost optimization strategy based on resource utilization levels includes:

[0015] Select the resource configuration information corresponding to the resource occupancy level from the preset mapping relationship table; generate a cost optimization strategy based on the resource configuration information.

[0016] In an optional implementation, after optimizing the cost of the cloud platform based on a cost optimization strategy, the method further includes:

[0017] Within a preset duration starting from the moment the cost optimization strategy is implemented, the resource data of the cloud server instance is monitored.

[0018] Secondly, this disclosure provides a cost optimization apparatus, comprising:

[0019] The first acquisition module is used to acquire resource data corresponding to the cloud server instance; the analysis module is used to analyze the resource usage of the cloud server instance based on the resource data using the first deep learning algorithm to obtain the resource usage level of the cloud server instance; the determination module is used to determine the cost optimization strategy based on the resource usage level; and the optimization module is used to optimize the cost of the cloud platform based on the cost optimization strategy.

[0020] In one alternative implementation, the acquisition module includes:

[0021] The acquisition submodule is used to obtain the instance identifier of the cloud server instance from the cloud platform. The instance identifier is set according to the preset specification. The partitioning submodule is used to partition the instance identifier according to the attributes, obtain the attribute data corresponding to each attribute, and read the resource data from the attribute data.

[0022] Thirdly, this disclosure provides a computer device, including:

[0023] The memory and processor are interconnected and communicate with each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the cost optimization method of the first aspect of the invention and any embodiment thereof.

[0024] Fourthly, this disclosure provides a computer-readable storage medium storing computer instructions for causing a computer to execute the cost optimization method of the first aspect of the invention and any embodiment thereof. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0026] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart illustrating the cost optimization method provided according to embodiments of this disclosure;

[0028] Figure 2 This is a schematic flowchart of a cost optimization method provided according to another embodiment of the present disclosure;

[0029] Figure 3 This is a structural block diagram of a cost optimization apparatus provided according to embodiments of the present disclosure;

[0030] Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0031] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0032] Numerous specific details are set forth in the following description to provide a thorough understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of this disclosure, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0033] According to an embodiment of the present invention, a cost optimization method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0034] This embodiment provides a cost optimization method that can be used on servers or mobile terminals, such as mobile phones or tablets. Figure 1 This is a flowchart of a cost optimization method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0035] Step S101: Obtain the resource data corresponding to the cloud server instance.

[0036] Specifically, by calling the Application Programming Interface (API) of the cloud platform to be optimized (hereinafter referred to as the cloud platform), resource data of at least one cloud server instance within a preset time period, either at the time of reading or with the time of reading as the termination time, is obtained from the cloud platform. Each cloud server instance corresponds to a set of resource data at any given time. This set of resource data represents the resource usage of the cloud server instance at that time, including but not limited to CPU resource utilization, memory resource utilization, disk resource utilization, and network resource utilization.

[0037] In one optional embodiment, the method for obtaining resource data corresponding to a cloud server instance includes: obtaining an instance identifier of the cloud server instance from the cloud platform, wherein the instance identifier is set according to a preset specification; dividing the instance identifier according to attributes to obtain attribute data corresponding to each attribute; and reading resource data from the attribute data.

[0038] For example, before executing step S101, the instance identifiers of each cloud server instance in the cloud platform are set according to a preset specification. Setting the instance identifiers in the cloud platform according to a unified specification enables the device executing the cost optimization method to support rapid access to multiple data sources and formats, thereby facilitating the processing of resource data from any vendor or platform. The preset specification involves writing the attribute information corresponding to the cloud server instance into the instance identifier. For example, the instance identifier includes attribute data corresponding to the instance number, business, environment, system, service, creation time, billing data, and resource data. Among them, the billing data includes data for different periods such as daily billing data, weekly billing data, and monthly billing data, and the resource data includes data corresponding to each resource at each time. Since the amount of billing data and resource data is large, their storage address in the cloud platform can be written into the corresponding instance identifier. The instance identifier can be the instance name.

[0039] For example, taking a cloud server instance as an example, the instance identifier of cloud server instance A obtained from the cloud platform is as follows: ID A1, Business A2, Environment A3, System A4, Service A5, Creation Time A6, Billing Data A7, and Resource Data A8. The instance identifier is divided according to attributes to obtain the attribute data corresponding to each attribute. For example, the attribute data corresponding to the environment attribute is A3, and the attribute data corresponding to the billing attribute can be found through storage address A7. After obtaining the attribute data, all attribute data corresponding to cloud server instance A can be written as a record into the database of the device executing this cost optimization method for subsequent data retrieval. Resource data can be read from the attribute data before or after being stored in the database; this embodiment does not limit the timing of resource data acquisition.

[0040] Step S102: Using the first deep learning algorithm, analyze the resource usage of the cloud server instance based on the resource data to obtain the resource usage level of the cloud server instance.

[0041] Specifically, the first deep learning algorithm is an algorithm trained using multiple training datasets and validated using a validation dataset before step S102. Each training dataset includes a real resource dataset (i.e., a set of resource data corresponding to each historical moment within a preset time period) and a real resource occupancy level determined by staff based on the changes in real resource data over time within the preset time period. The real resource occupancy level is used to characterize the resource scarcity of cloud server instances; the higher the level, the greater the resource utilization and the more scarce the resources. After training begins, each real resource dataset is sequentially input into the first deep learning algorithm, which then estimates the resource occupancy level corresponding to the real resource dataset, obtaining the estimated resource occupancy level for each set of real resource datasets. The estimated resource occupancy level and the real resource occupancy level are then input into a loss function to calculate the loss value. If the loss value does not reach a preset loss threshold, the algorithm parameters are adjusted, and the resource occupancy level corresponding to the real resource dataset is re-estimated until the loss value reaches the preset loss threshold, at which point the training of the first deep learning algorithm ends.

[0042] For example, the resource data of cloud server instance A for the past 6 months is obtained through step S101. The resource data for the past 6 months is input into the data analysis platform, and the data analysis platform calls the trained first deep learning algorithm to output the resource occupancy level of cloud server instance A for the past 6 months based on the changes in the resource data over the past 6 months.

[0043] Step S103: Determine the cost optimization strategy based on the resource utilization level.

[0044] Specifically, cost optimization strategies, also known as resource adjustment strategies, include upgrading and downgrading the configuration of cloud server instances. Upgrading cloud server instance configurations includes, but is not limited to, expanding capacity, such as increasing memory, deleting unnecessary data from the hard drive, and increasing the number of CPU cores; downgrading cloud server instance configurations is the opposite, i.e., reducing capacity, such as reducing memory allocation space.

[0045] In one optional embodiment, the method for determining a cost optimization strategy based on resource occupancy level includes: selecting resource configuration information corresponding to the resource occupancy level from a preset mapping table; and generating a cost optimization strategy based on the resource configuration information.

[0046] For example, the preset mapping table is a table constructed by those skilled in the art based on historical data or practical experience, representing the mapping relationship between resource occupancy levels and resource configuration information. After obtaining the resource occupancy level of the cloud server instance through step S102, the resource configuration information corresponding to the resource occupancy level of the cloud server instance can be determined by querying the preset mapping table. Then, a cost optimization strategy is generated based on the queried resource configuration information. The resource configuration information includes the adjustment method and adjustment amount for each resource, such as increasing memory by 200G or increasing the number of CPU cores by 1.

[0047] Step S104: Optimize the cost of the cloud platform based on the cost optimization strategy.

[0048] Specifically, the original configuration information of cloud server instances is automatically modified based on the resource configuration information in the cost optimization strategy, thereby completing the cost optimization of the cloud platform.

[0049] The cost optimization method provided in this embodiment, after obtaining the resource data of the cloud server instance, uses a first deep learning algorithm to determine the resource occupancy level of the cloud server instance, and determines the cost optimization strategy based on the resource occupancy level, thereby completing the cost optimization of the cloud platform according to the cost optimization strategy; the above cost optimization process autonomously completes the data analysis, strategy formulation and execution process without human intervention, improving the efficiency and accuracy of cost optimization.

[0050] In an optional embodiment, after step S104, the method further includes: monitoring the resource data of the cloud server instance within a preset duration starting from the moment the cost optimization strategy is executed.

[0051] For example, after optimizing the cloud platform based on the cost optimization strategy, in order to monitor the effect of the cost optimization and to adjust the optimization strategy in a timely manner according to changes in resources, this embodiment uses a closed-loop feedback method to monitor the resource data of the cloud server instance within a preset time period starting from the moment the cost optimization strategy is executed. The monitored resource data is compared and analyzed with the expected target to better identify problems and deficiencies in the execution process and make timely adjustments and optimizations.

[0052] In the actual operation of cloud platforms, it is sometimes necessary to analyze resource usage based on billing data in order to formulate cost optimization strategies that are more in line with actual conditions. Therefore, in an optional embodiment, the cost optimization method provided by this invention further includes... Figure 2 The steps shown are as follows:

[0053] Step S201: Obtain the billing data from the cloud platform.

[0054] Specifically, the cloud platform's billing data can be understood as the sum of the billing data corresponding to each cloud server instance within the cloud platform. The method for obtaining the billing data for each cloud server instance is the same as the method for obtaining resource data in step S101. It should also be noted that the billing data corresponds to a period, such as daily billing data, weekly billing data, monthly billing data, and annual billing data. The billing data includes the amount generated by each resource within the corresponding period, as well as the total amount determined based on the amounts of each resource.

[0055] For example, one could obtain monthly billing data for a cloud platform over the past year, where each monthly billing data is the sum of the billing data for each resource.

[0056] Step S202: Use the second deep algorithm to perform anomaly analysis on the billing data and output the analysis results.

[0057] Specifically, the second deep learning algorithm is trained using historical billing data and labeled outlier data within that data. The training process for the second deep learning algorithm is similar to that of the first deep learning algorithm and will not be repeated here. The labeling process for outlier data will be explained in detail here. For example, historical billing data represents the amount (i.e., cost) generated each month over the past year; therefore, billing data is also called cost data. Based on the changes in historical billing data over time, the cost change trend over the past year can be obtained, and extreme points are selected from this cost change trend as outliers and labeled. The determination of outliers is not specifically limited here, but only illustrated illustratively.

[0058] Specifically, the output analysis results include two types: one is that the billing data is normal, and the other is that the billing data is abnormal.

[0059] Step S203: When the analysis result indicates that there are abnormalities in the billing data, the abnormalities in the billing data are displayed.

[0060] For example, when the billing data is normal, no action is required. When the billing data is abnormal, the abnormal data can be displayed, including but not limited to highlighting the abnormal data in red or making it flash. Those skilled in the art will perform correlation analysis on the displayed abnormal data to determine the cause of the anomaly. For instance, if the billing data for 11 months of the year remains around 1 million, with only February's billing data reaching 2 million, it can be found that 100 cloud server instances were activated in February due to a sudden surge in data volume. Furthermore, those skilled in the art can also formulate subsequent procurement plans based on the cause of the anomaly and other data to indirectly achieve cost optimization.

[0061] This embodiment also provides a cost optimization device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0062] This embodiment provides a cost optimization device, such as... Figure 3 As shown, it includes:

[0063] The first acquisition module 301 is used to acquire resource data corresponding to the cloud server instance.

[0064] Analysis module 302 is used to analyze the resource usage of cloud server instances based on resource data using a first deep learning algorithm, and to obtain the resource usage level of cloud server instances.

[0065] Module 303 is used to determine cost optimization strategies based on resource occupancy levels.

[0066] Optimization module 304 is used to optimize the cost of the cloud platform based on cost optimization strategies.

[0067] In some alternative implementations, the acquisition module 301 includes:

[0068] The acquisition submodule is used to obtain the instance identifier of the cloud server instance from the cloud platform. The instance identifier is set according to the preset specification. The partitioning submodule is used to partition the instance identifier according to the attributes, obtain the attribute data corresponding to each attribute, and read the resource data from the attribute data.

[0069] In some alternative embodiments, the apparatus further includes:

[0070] The second acquisition module is used to acquire billing data from the cloud platform; the anomaly detection module is used to perform anomaly analysis on the billing data using the second deep algorithm and output the analysis results; the display module is used to display the anomalies in the billing data when the analysis results indicate that there are anomalies in the billing data.

[0071] In some alternative implementations, the second deep learning algorithm in the anomaly detection module is trained using historical billing data and anomaly data already labeled within the historical billing data.

[0072] In some alternative implementations, the determining module 303 includes:

[0073] The selection submodule is used to select resource configuration information corresponding to the resource occupancy level from the preset mapping relationship table; the generation submodule is used to generate cost optimization strategies based on the resource configuration information.

[0074] In some alternative implementations, after optimization module 304, the apparatus further includes:

[0075] The monitoring module is used to monitor the resource data of cloud server instances within a preset time period starting from the moment the cost optimization strategy is implemented.

[0076] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0077] In this embodiment, the cost optimization device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0078] This invention also provides a computer device having the above-described features. Figure 4 The cost optimization device shown.

[0079] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 10 as an example.

[0080] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0081] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0082] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0083] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0084] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0085] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0086] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0087] The above are merely specific embodiments of this disclosure, enabling those skilled in the art to understand or implement this disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A cost optimization method, characterized in that, include: Retrieve resource data corresponding to the cloud server instance; Using a first deep learning algorithm, the resource usage of the cloud server instance is analyzed based on the resource data to obtain the resource usage level of the cloud server instance; Determine the cost optimization strategy based on the resource utilization level; The cost optimization strategy described above is used to optimize the cost of the cloud platform.

2. The method according to claim 1, characterized in that, The process of obtaining resource data corresponding to the cloud server instance includes: Obtain the instance identifier of the cloud server instance from the cloud platform; the instance identifier is set according to a preset specification. The instance identifier is divided according to its attributes to obtain attribute data corresponding to each attribute, and the resource data is read from the attribute data.

3. The method according to claim 1 or 2, characterized in that, The method further includes: Obtain billing data from the cloud platform; The second-depth algorithm is used to perform anomaly analysis on the billing data, and the analysis results are output. When the analysis results indicate that the billing data is abnormal, the abnormality in the billing data is displayed.

4. The method according to claim 3, characterized in that, The second deep learning algorithm is trained using historical billing data and anomaly data labeled in the historical billing data.

5. The method according to claim 1, characterized in that, The step of determining the cost optimization strategy based on the resource occupancy level includes: Select the resource configuration information corresponding to the resource occupancy level from the preset mapping table; The cost optimization strategy is generated based on the resource configuration information.

6. The method according to claim 1, characterized in that, After optimizing the cost of the cloud platform based on the cost optimization strategy, the method further includes: Within a preset duration starting from the moment the cost optimization strategy is executed, the resource data of the cloud server instance is monitored.

7. A cost optimization device, characterized in that, include: The first acquisition module is used to acquire resource data corresponding to the cloud server instance; The analysis module is used to analyze the resource usage of the cloud server instance based on the resource data using a first deep learning algorithm, and to obtain the resource usage level of the cloud server instance. The determination module is used to determine a cost optimization strategy based on the resource occupancy level; The optimization module is used to optimize the cost of the cloud platform based on the cost optimization strategy.

8. The apparatus according to claim 7, characterized in that, The acquisition module includes: The acquisition submodule is used to obtain the instance identifier of the cloud server instance from the cloud platform, and the instance identifier is set according to a preset specification; The partitioning submodule is used to partition the instance identifier according to the attributes, obtain the attribute data corresponding to each attribute, and read the resource data from the attribute data.

9. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the cost optimization method of any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the cost optimization method according to any one of claims 1 to 6.

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