CN117909068A - Resource recommendation method, device and storage medium - Google Patents

Abstract

The application provides a resource recommendation method, a resource recommendation device and a storage medium, relates to the technical field of communication, and can improve the matching degree between recommended configuration resources and service requirements. The method comprises the following steps: determining a predicted concurrent connection number of a target service, a cloud resource type used in the target service, and a plurality of performance baselines of each first cloud resource in the plurality of first cloud resources; the first cloud resource is one of a plurality of types of opened cloud resources, wherein the resource load of the cloud resources is larger than or equal to a first threshold value; the performance base line is used for representing the number of concurrent service connections supportable by the first cloud resource; and determining the first cloud resources with the performance base line meeting the predicted concurrent connection number as recommended configuration resources of the target service, wherein the cloud resource type in the plurality of first cloud resources is the cloud resource type used in the target service.

Description

Resource recommendation method, device and storage medium

Technical Field

The present application relates to the field of communication technology, and in particular to a resource recommendation method, device and storage medium.

Background technique

With the rapid development of cloud service technology, users can select corresponding cloud resources for business deployment according to business needs or business scale to achieve rapid business development. At present, the method for users to select corresponding cloud resources may include: obtaining the estimated business scale of the user, and determining a single type of cloud resources based on the estimated business scale.

However, the above method cannot determine the full amount of cloud resources required by the user, resulting in the user selecting a relatively single cloud resource, and thus it is difficult to obtain cloud resources that are closer to business needs.

Summary of the invention

The present application provides a resource recommendation method, device and storage medium, which can improve the matching degree between recommended configuration resources and business requirements.

In order to achieve the above objectives, this application adopts the following technical solutions:

In a first aspect, the present application provides a resource recommendation method, the method comprising: determining a predicted number of concurrent connections for a target business, a type of cloud resource used in the target business, and multiple performance baselines for each of a plurality of first cloud resources; the first cloud resource is a cloud resource whose resource load is greater than or equal to a first threshold among multiple types of cloud resources that have been opened; a performance baseline is used to characterize the number of concurrent business connections that a first cloud resource can support; and determining a first cloud resource among the plurality of first cloud resources whose cloud resource type is the type of cloud resource used in the target business and whose performance baseline meets the predicted number of concurrent connections as a recommended configuration resource for the target business.

In one possible implementation, before determining the predicted number of concurrent connections of the target business, the method also includes: obtaining predicted business information of the target business; the predicted business information includes: the predicted number of online users, the type of cloud resources used in the target business, and the technology stack; and determining a business profile of the target business based on the predicted business information.

In a possible implementation manner, determining the predicted number of concurrent connections of the target service includes: determining the predicted number of concurrent connections of the target service based on the predicted number of online users and a preset ratio.

In one possible implementation, each first cloud resource includes cloud resources of multiple specifications; determining a performance baseline for each of the multiple first cloud resources includes: determining the number of business connections of at least one second cloud resource; the second cloud resource is a cloud resource of any specification among the first cloud resources; and determining an average value of the number of business connections of the at least one second cloud resource as the performance baseline of the first cloud resource.

In one possible implementation, the total cost of the recommended configuration resources is determined; the total cost of the recommended configuration resources includes the cost of the recommended configuration resources and the cost of supporting products required to use the recommended configuration resources; the recommended configuration resources and the total cost of the recommended configuration resources are determined as a resource recommendation list for the target business.

In a second aspect, the present application provides a resource recommendation device, which includes: a processing unit; a processing unit, used to determine the predicted number of concurrent connections of a target business, the type of cloud resources used in the target business, and multiple performance baselines of each first cloud resource in multiple first cloud resources; the first cloud resource is a cloud resource among multiple types of cloud resources that have been opened, and the resource load is greater than or equal to a first threshold; a performance baseline is used to characterize the number of concurrent business connections that a first cloud resource can support; the processing unit is also used to determine, among multiple first cloud resources, a first cloud resource whose cloud resource type is the cloud resource type used in the target business and whose performance baseline meets the predicted number of concurrent connections as a recommended configuration resource for the target business.

In one possible implementation, before determining the predicted number of concurrent connections of the target business, the device also includes: a communication unit; a communication unit for obtaining predicted business information of the target business; the predicted business information includes: the predicted number of online users, the type of cloud resources used in the target business, and the technology stack; a processing unit, further for determining a business profile of the target business based on the predicted business information.

In a possible implementation, the processing unit is further configured to determine the predicted number of concurrent connections of the target service based on the predicted number of online users and a preset ratio.

In one possible implementation, each first cloud resource includes cloud resources of multiple specifications; the processing unit is further used to determine the number of business connections of at least one second cloud resource; the second cloud resource is a cloud resource of any specification among the first cloud resources; the processing unit is further used to determine the average number of business connections of at least one second cloud resource as the performance baseline of the first cloud resource.

In one possible implementation, the processing unit is further used to determine the total cost of the recommended configuration resources; the total cost of the recommended configuration resources includes the cost of the recommended configuration resources and the cost of supporting products required to use the recommended configuration resources; the processing unit is further used to determine the recommended configuration resources and the total cost of the recommended configuration resources as a resource recommendation list for the target business.

In a third aspect, the present application provides a resource recommendation device, comprising: a processor and a communication interface; the communication interface and the processor are coupled, and the processor is used to run a computer program or instructions to implement the resource recommendation method described in the first aspect and any possible implementation method of the first aspect.

In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed on a terminal, the terminal executes the resource recommendation method described in the first aspect and any possible implementation of the first aspect.

In a fifth aspect, the present application provides a computer program product comprising instructions, which, when executed on a resource recommendation device, enables the resource recommendation device to perform the resource recommendation method as described in the first aspect and any possible implementation manner of the first aspect.

In a sixth aspect, the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface and the processor are coupled, and the processor is used to run a computer program or instructions to implement the resource recommendation method as described in the first aspect and any possible implementation method of the first aspect.

Specifically, the chip provided in the present application also includes a memory for storing computer programs or instructions.

In the resource recommendation method provided in the embodiment of the present application, the resource recommendation device determines, among multiple first cloud resources, a first cloud resource whose cloud resource type is the cloud resource type used in the target business and whose performance baseline meets the predicted number of concurrent connections as a recommended configuration resource for the target business. Since a performance baseline is used to characterize the number of concurrent business connections that a first cloud resource can support, the number of concurrent business connections that the recommended configuration resource can support can meet the predicted number of concurrent connections. Since the first cloud resource is a cloud resource whose resource load is greater than or equal to a first threshold among the multiple types of cloud resources that have been opened, the resource recommendation device can determine all types of cloud resources among the multiple types of cloud resources used in the target business from the multiple types of cloud resources that have been opened. The recommended configuration resources obtained in this way are more comprehensive and can better meet the business needs of the target business.

In addition, the resource recommendation device determines the performance baseline of cloud resources whose resource load is greater than or equal to the first threshold, which can avoid the problem of inaccurate performance baseline caused by excessively high or low cloud resource load, and further avoid the situation where the recommended configuration resources do not meet the business requirements of the target business.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of the structure of a resource recommendation system provided in an embodiment of the present application;

FIG2 is a schematic diagram of the structure of a resource recommendation device provided in an embodiment of the present application;

FIG3 is a flow chart of a resource recommendation method provided in an embodiment of the present application;

FIG4 is an example diagram of determining a performance baseline provided in an embodiment of the present application;

FIG5 is an example diagram of a resource recommendation list provided in an embodiment of the present application;

FIG6 is a flow chart of another resource recommendation method provided in an embodiment of the present application;

FIG. 7 is a schematic diagram of the structure of another resource recommendation device provided in an embodiment of the present application.

Detailed ways

The resource recommendation method, device and storage medium provided in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

The term "and/or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and/or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

With the rapid development of technologies such as cloud computing and cloud services, most companies' business software has changed from traditional stand-alone local deployment to cloud deployment, so that companies can provide public cloud or private cloud services to the outside world, so that users can choose corresponding cloud resources (for example, containers, database services, middleware redis, middleware kafka and other resources) for business deployment, thereby achieving rapid business development. At present, the method for users to select corresponding cloud resources may include: calculating the load of resources that the user has opened, and based on the load of the above-mentioned opened resources, determining that the resources whose load is within a preset range are recommended configuration resources. Alternatively, obtain the user's estimated business scale, and determine a single type of cloud resources based on the above-mentioned estimated business scale.

However, the above method cannot determine the full amount of cloud resources required by the user, resulting in the user's selection of relatively single cloud resources, and the user's understanding of the computing power of cloud resources and the resource specifications required for the business scale is relatively superficial, resulting in the user's selection process in the process of selecting cloud resource product specifications may be time-consuming, or the selected cloud resources are less matched with the actual business needs.

In view of this, an embodiment of the present application provides a resource recommendation method, in which a resource recommendation device determines, among multiple first cloud resources, a first cloud resource whose cloud resource type is the cloud resource type used in a target business and whose performance baseline meets the predicted number of concurrent connections as a recommended configuration resource for the target business. Since a performance baseline is used to characterize the number of concurrent business connections that a first cloud resource can support, the number of concurrent business connections that the recommended configuration resource can support can meet the predicted number of concurrent connections. Since the first cloud resource is a cloud resource whose resource load is greater than or equal to a first threshold among multiple types of cloud resources that have been opened, the resource recommendation device can determine all types of cloud resources in the cloud resource type used in the target business from the multiple types of cloud resources that have been opened. The recommended configuration resources obtained in this way are more comprehensive and can better meet the business needs of the target business.

In addition, the resource recommendation device determines the performance baseline of cloud resources whose resource load is greater than or equal to the first threshold, which can avoid the problem of inaccurate performance baseline caused by excessively high or low cloud resource load, and further avoid the situation where the recommended configuration resources do not meet the business requirements of the target business.

For example, as shown in Figure 1, Figure 1 shows a schematic diagram of the structure of a resource recommendation system provided by an embodiment of the present application. The resource recommendation system includes: a resource recommendation device 101 and a user device 102. Figure 1 takes the resource recommendation system including a resource recommendation device 101 and a user device 102 as an example for explanation.

The resource recommendation device 101 is used to determine the predicted number of concurrent connections of the target business, the type of cloud resources used in the target business, and multiple performance baselines of each of multiple first cloud resources, and determine the first cloud resource among the multiple first cloud resources whose cloud resource type is the cloud resource type used in the target business and whose performance baseline meets the predicted number of concurrent connections as the recommended configuration resource for the target business.

The user device 102 is used to provide a data basis for the resource recommendation device 101 to determine the predicted number of concurrent connections of the target service and the type of cloud resources used in the target service.

The first cloud resource is a cloud resource whose resource load is greater than or equal to the first threshold among multiple types of cloud resources that have been enabled. A performance baseline is used to characterize the number of concurrent business connections that a first cloud resource can support.

In an example, the resource recommendation device 101 may be a server. The server may be a single server, or a server cluster composed of multiple servers. In some implementations, the server cluster may also be a distributed cluster.

In another example, the resource recommendation device 101 may be a terminal equipment or a user equipment (UE) or a mobile station (MS) or a mobile terminal (MT), etc. Specifically, the resource recommendation device 101 may be a mobile phone, a tablet computer or a computer with a wireless transceiver function, or a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in unmanned driving, a wireless terminal in telemedicine, a wireless terminal in a smart grid, a wireless terminal in a smart city, a smart home, a vehicle-mounted terminal, etc. In the embodiment of the present application, the device for realizing the function of the resource recommendation device 101 may be the resource recommendation device 101, or may be a device capable of supporting the resource recommendation device 101 to realize the function, such as a chip or a chip system.

In another example, the user equipment 102 may be a terminal (terminal equipment) or a user equipment (UE) or a mobile station (MS) or a mobile terminal (MT), etc. Specifically, the user equipment 102 may be a mobile phone, a tablet computer or a computer with a wireless transceiver function, or a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in unmanned driving, a wireless terminal in telemedicine, a wireless terminal in a smart grid, a wireless terminal in a smart city, a smart home, a vehicle-mounted terminal, etc. In the embodiment of the present application, the device for implementing the function of the user equipment 102 may be the user equipment 102, or may be a device capable of supporting the user equipment 102 to implement the function, such as a chip or a chip system.

In addition, the resource recommendation system described in the embodiment of the present application is intended to more clearly illustrate the technical solution of the embodiment of the present application, and does not constitute a limitation on the technical solution provided in the embodiment of the present application. A person of ordinary skill in the art can know that with the evolution of network architecture and the emergence of new resource recommendation systems, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

In specific implementation, the devices in FIG1 may adopt the composition structure shown in FIG2, or include the components shown in FIG2. FIG2 is a composition diagram of a resource recommendation device 200 provided in an embodiment of the present application. The resource recommendation device 200 may be a resource recommendation device 101 or a chip or a system on chip in the resource recommendation device 101. Alternatively, the resource recommendation device 200 may be a resource recommendation device 102 or a chip or a system on chip in the resource recommendation device 102. As shown in FIG2, the resource recommendation device 200 may include a processor 201 and a communication line 202.

Furthermore, the resource recommendation device 200 may also include a communication interface 203 and a memory 204. The processor 201, the memory 204 and the communication interface 203 may be connected via a communication line 202.

The processor 201 is a CPU, a general processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The processor 201 may also be other devices with processing functions, such as circuits, devices, or software modules, without limitation.

The communication line 202 is used to transmit information between the components included in the resource recommendation device 200.

The communication interface 203 is used to communicate with other devices or other communication networks. The other communication networks may be Ethernet, radio access network (RAN), wireless local area network (WLAN), etc. The communication interface 203 may be a module, a circuit, a communication interface or any device capable of achieving communication.

The memory 204 is used to store instructions, where the instructions may be computer programs.

The memory 204 may be a read-only memory (ROM) or other types of static storage devices that can store static information and/or instructions, or a random access memory (RAM) or other types of dynamic storage devices that can store information and/or instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, etc., without limitation.

It should be noted that the memory 204 can exist independently of the processor 201, or can be integrated with the processor 201. The memory 204 can be used to store instructions or program codes or some data, etc. The memory 204 can be located in the resource recommendation device 200, or can be located outside the resource recommendation device 200, without limitation. The processor 201 is used to execute the instructions stored in the memory 204 to implement the resource recommendation method provided in the following embodiments of the present application.

In one example, the processor 201 may include one or more CPUs, for example, CPU0 and CPU1.

As an optional implementation, the resource recommendation device 200 includes multiple processors.

As an optional implementation, the resource recommendation apparatus 200 further includes an output device and an input device. For example, the output device is a display screen, a speaker, and the like, and the input device is a keyboard, a mouse, a microphone, or a joystick, and the like.

It should be noted that the resource recommendation device 200 may be a desktop computer, a portable computer, a network server, a mobile phone, a tablet computer, a wireless terminal, an embedded device, a chip system or a device having a similar structure as shown in FIG2. In addition, the composition structure shown in FIG2 does not constitute a limitation on the various devices in FIG1 and FIG2. In addition to the components shown in FIG2, the various devices in FIG1 and FIG2 may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

In the embodiment of the present application, the chip system may be composed of a chip, or may include a chip and other discrete devices.

In addition, the actions, terms, etc. involved in the various embodiments of the present application can refer to each other without limitation. The message name or parameter name in the message exchanged between the various devices in the embodiments of the present application is only an example, and other names can also be used in the specific implementation without limitation.

The resource recommendation method provided by the embodiment of the present application is described below in conjunction with the resource recommendation system shown in Figure 1. Among them, the actions, terms, etc. involved in the various embodiments of the present application can refer to each other without limitation. The message name or parameter name in the message exchanged between the various devices in the embodiment of the present application is only an example, and other names can also be used in the specific implementation without limitation. The actions involved in the various embodiments of the present application are only an example, and other names can also be used in the specific implementation, such as: "included in" in the embodiment of the present application can also be replaced by "carried on" or "carried in", etc.

In order to solve the problems existing in the above-mentioned prior art, the embodiment of the present application proposes a resource recommendation method, which can improve the matching degree between the recommended configuration resources and the business requirements. As shown in Figure 3, the method includes:

S301. A resource recommendation device determines a predicted number of concurrent connections of a target service, a type of cloud resources used in the target service, and multiple performance baselines of each of a plurality of first cloud resources.

The first cloud resource is a cloud resource whose resource load is greater than or equal to the first threshold among multiple types of cloud resources that have been enabled. A performance baseline is used to characterize the number of concurrent business connections that a first cloud resource can support.

In a possible embodiment, the resource recommendation device determines the predicted number of concurrent connections of the target service by: the resource recommendation device determines the predicted number of concurrent connections of the target service based on the predicted number of online users and a preset ratio.

It should be noted that the above-mentioned predicted number of online users can be determined by predicted business information of the target business, and can also be determined by a business profile of the target business.

Optionally, the preset ratio may be a ratio of the predicted number of online users to the predicted number of concurrent connections. For example, the ratio of the predicted number of online users to the predicted number of concurrent connections may be 10:1. The above is only an exemplary description of the preset ratio in the general algorithm in the industry, and the preset ratio may also be other ratios, and this application does not impose any limitation on this.

For example, taking the predicted number of online users as 20,000, the preset ratio is the ratio of the predicted number of online users to the predicted number of concurrent connections, and the above ratio is 10:1 as an example: the resource recommendation device determines that the predicted number of concurrent connections of the target service is 20,000/10=2,000.

It can be understood that the resource recommendation device can predict the number of concurrent connections actually required by the target business (i.e., the predicted number of concurrent connections) based on the predicted number of online users and the preset ratio. In this way, the resource recommendation device can subsequently determine the cloud resources actually required by the target business based on the number of concurrent connections actually required by the target business, thereby ensuring that the recommended configuration resources of the target business meet the actual needs of the target business.

It should be understood that since the first cloud resource is a cloud resource whose resource load is greater than or equal to the first threshold among multiple types of cloud resources that have been opened, the resource recommendation device can determine the performance baseline of the cloud resource whose resource load meets certain conditions. This can more realistically reflect the performance of the cloud resource under a certain load, so as to avoid the problem of large performance deviation of the cloud resource under high or low load conditions, and thus make the recommended configuration resources for the target business determined subsequently more accurate.

It should be noted that the above-mentioned multiple types of cloud resources that have been opened may include multiple types of cloud resources that have been opened and run in all businesses, that is, the above-mentioned multiple types of cloud resources that have been opened may include all cloud resources existing on the market.

Optionally, the resource recommendation device may set a first threshold value according to the load of the cloud resources. For example, the resource recommendation device may determine that the first threshold value is 75% of the cloud resource load. The above is only an exemplary description of the first threshold value, and the above first threshold value may also be other values (for example, 80% of the resource load), and this application does not impose any limitation on this.

In a possible embodiment, each first cloud resource includes cloud resources of multiple specifications. The resource recommendation device determines the performance baseline of each of the multiple first cloud resources in the implementation process may be: the resource recommendation device determines the number of service connections of at least one second cloud resource, and determines the average value of the number of service connections of at least one second cloud resource as the performance baseline of the first cloud resource. The second cloud resource is a cloud resource of any specification among the first cloud resources.

Exemplarily, the multiple first cloud resources may include at least one of the following: container resources, database resources, and middleware resources. The above is only an exemplary description of the first cloud resources, and the first cloud resources may also include other cloud resources (for example, cloud server resources), and this application does not impose any restrictions on this.

As an example, when the first cloud resource is a container resource, the second cloud resources of multiple specifications included in the first cloud resource may be 4c8g containers and 8c16g containers. When the first cloud resource is a database resource, the second cloud resources of multiple specifications included in the first cloud resource may be 4c8g databases and 8c16g databases. The above is only an exemplary description of the second cloud resource, and the second cloud resource may also be a cloud resource of other specifications, and this application does not impose any restrictions on this.

As shown in Figure 4, Figure 4 shows an example diagram of a resource recommendation device determining a performance baseline. Exemplarily, taking at least one second cloud resource as 500 4c8g containers as an example: the resource recommendation device can determine the number of concurrent connections of each of the 500 4c8g containers, and determine the average value of the sum of the concurrent connections of the 500 4c8g containers as the performance baseline of the 4c8g container. The resource recommendation device can determine the performance baseline of the 4c8g container as a performance baseline of the first cloud resource.

Similarly, when the second cloud resource is an 8c16g container, a 4c8g database, or a Redis master-slave version of 4c16g, the implementation process of the resource recommendation device determining the performance baseline can be understood by referring to the description of the corresponding position above, and will not be repeated here.

It is understandable that in the process of the resource recommendation device determining the performance baseline of the first cloud resource, the resource recommendation device can obtain performance baselines of different specifications under the first cloud resource based on the average number of business connections of the same second cloud resource in the first cloud resource.

In a possible implementation, the resource recommendation device may periodically determine the performance baseline of the first cloud resource. For example, the resource recommendation device may count the first cloud resource among multiple types of cloud resources that have been enabled on a monthly basis and determine multiple performance baselines of the first cloud resource.

Optionally, the resource recommendation device may save multiple performance baselines of the first cloud resource into a database for subsequent use.

S302. The resource recommendation device determines, among multiple first cloud resources, a first cloud resource whose cloud resource type is the cloud resource type used in the target business and whose performance baseline meets the predicted number of concurrent connections as a recommended configuration resource for the target business.

As an example, assuming that the cloud resource types used in the target business include containers, databases, and middleware redis, and the predicted number of concurrent connections is 2000, the resource recommendation device can determine the container resources, database resources, and middleware redis resources in the first cloud resources, and determine the specifications of the container resources with a performance baseline of 2000 from the above container resources, determine the specifications of the database resources with a performance baseline of 2000 from the above database resources, and determine the specifications of the middleware redis resources with a performance baseline of 2000 from the above middleware redis resources. The resource recommendation device can determine the cloud resources of multiple specifications with a performance baseline of 2000 as recommended configuration resources for the target business.

Specifically, the recommended configuration resources of the target business may include container resources with a specification of 8c16g, database resources with a specification of 4c8g, and middleware redis resources with a specification of 4c16g. The above is only an exemplary description of the recommended configuration resources of the target business. The recommended configuration resources of the target business may also include other resources, and this application does not impose any restrictions on this.

After the resource recommendation device determines the recommended configuration resources for the target business, the resource recommendation device can determine the cost of the recommended configuration resources and summarize the costs of the above recommended configuration resources into a resource recommendation list, so that the user can quickly determine the cloud resources required for the target business based on the above resource recommendation list.

In a possible embodiment, the resource recommendation device determines the resource recommendation list of the target business in an implementation process as follows: the resource recommendation device determines the total cost of the recommended configuration resources, and determines the recommended configuration resources and the total cost of the recommended configuration resources as the resource recommendation list of the target business. The total cost of the recommended configuration resources includes the cost of the recommended configuration resources and the cost of the supporting products required to use the recommended configuration resources.

Exemplarily, the supporting products required for using the recommended configuration resources may be supporting products for use with containers, including virtual private networks (virtual private clouds, VPCs), etc.

As shown in FIG5 , FIG5 shows an example diagram of a resource recommendation list. The resource recommendation list may include resources required by the target business, specifications of the resources required by the target business, the quantity of resources of each specification, the price of each specification of the resources required by the target business, and the total price of the resources required by the target business. The user can modify or activate the resources deployed by the target business according to the above resource recommendation list.

Optionally, the resource recommendation device may send the resource recommendation list of the target service to the user device. The user device may receive the resource recommendation list of the target service, so that the user can quickly select and activate the cloud resources corresponding to the target service according to the resource recommendation list of the target service, which can solve the problem of difficulty in selecting user resource specifications.

It should be noted that the resource recommendation method provided in the embodiment of the present application can be applied to public cloud or private cloud services. In this way, in the process of deploying cloud resources for the target business, the subsequent resource expansion or reduction can be carried out through the above-mentioned resource recommendation scheme, so that the cloud resources deployed by the target business meet the business needs of the target business. It can also save users' time in resource selection, improve cloud efficiency, and improve user satisfaction.

In the resource recommendation method provided in the embodiment of the present application, the resource recommendation device determines, among multiple first cloud resources, a first cloud resource whose cloud resource type is the cloud resource type used in the target business and whose performance baseline meets the predicted number of concurrent connections as a recommended configuration resource for the target business. Since a performance baseline is used to characterize the number of concurrent business connections that a first cloud resource can support, the number of concurrent business connections that the recommended configuration resource can support can meet the predicted number of concurrent connections. Since the first cloud resource is a cloud resource whose resource load is greater than or equal to a first threshold among the multiple types of cloud resources that have been opened, the resource recommendation device can determine all types of cloud resources among the multiple types of cloud resources used in the target business from the multiple types of cloud resources that have been opened. The recommended configuration resources obtained in this way are more comprehensive and can better meet the business needs of the target business.

In addition, the resource recommendation device determines the performance baseline of cloud resources whose resource load is greater than or equal to the first threshold, which can avoid the problem of inaccurate performance baseline caused by excessively high or low cloud resource load, and further avoid the situation where the recommended configuration resources do not meet the business requirements of the target business.

In a possible embodiment, before the resource recommendation device determines the predicted number of concurrent connections of the target business, the resource recommendation device can obtain the predicted business information of the target business, and determine the business profile of the target business based on the predicted business information. In this way, the resource recommendation device can determine the business needs of the target business based on the business profile of the target business, and then recommend cloud resources that meet the business needs of the target business. Based on the method embodiment shown in Figure 3, this embodiment provides a possible implementation method. In combination with Figure 3, as shown in Figure 6, the implementation process of the resource recommendation device determining the business profile of the target business can be determined by the following S601 to S602.

S601: The resource recommendation device obtains predicted service information of a target service.

The predicted service information includes at least one of the following: the predicted number of online users, the type of cloud resources used in the target service, and the technology stack.

Optionally, the above is only an exemplary description of the predicted service information, and the predicted service information may also include other information (eg, predicted total registered user scale). This application does not impose any limitation on this.

As a possible implementation, the implementation process of S601 may be: the user may fill in information such as predicted online users of the target business, cloud resource types used in the target business, and technology stack on the front-end page of the resource recommendation device. In response to the user operation, the resource recommendation device may determine the relevant information of the target business filled in by the user on the front-end page as the predicted business information of the target business.

As another possible implementation, the implementation process of S601 may also be: in response to the user operation, the user device may determine the predicted service information of the target service and send the predicted service information of the target service to the resource recommendation device. The resource recommendation device may receive the predicted service information of the target service.

In one example, the predicted business information of the target business may include: the predicted total registered user scale is 100,000, the predicted number of online users is 20,000, and the cloud resource types used in the target business are container service, MySQL database, middleware redis, and middleware kafka.

S602: The resource recommendation device determines a business profile of the target business based on the predicted business information.

As a possible implementation, the implementation process of S602 may be: the resource recommendation device may use knowledge graph technology to represent the predicted business information as a graph, and analyze the graph to determine the key features and attributes of the target business (for example, the cloud resource type used by the target business and the predicted number of online users). The resource recommendation device may determine the key features and attributes of the target business as a business profile of the target business.

It should be noted that the resource recommendation device can verify whether the business profile of the target business is consistent with the actual business of the target business. In the case where the business profile of the target business is inconsistent with the actual business, the resource recommendation device can optimize the graph corresponding to the predicted business information and re-extract the business profile of the target business to ensure the accuracy and effectiveness of the business profile of the target business.

In the resource recommendation method provided in the embodiment of the present application, the resource recommendation device determines the business profile of the target business based on the predicted business information of the target business, which can better understand the business needs of the target business, and thus provide a basis for the subsequent determination of recommended configuration resources. The resource recommendation device can determine the cloud resources that are more in line with the business needs of the target business by analyzing the business profile of the target business, and thus can improve the matching degree between the recommended configuration resources and the business needs of the target business.

It is understandable that the above-mentioned resource recommendation method can be implemented by a resource recommendation device. In order to realize the above-mentioned functions, the resource recommendation device includes hardware structures and/or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the modules and algorithm steps of each example described in the embodiments disclosed herein, the embodiments disclosed in the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the embodiments disclosed in this application.

The embodiment disclosed in the present application can divide the functional modules of the resource recommendation device generated by the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment disclosed in the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

Fig. 7 is a schematic diagram of the structure of a resource recommendation device provided by an embodiment of the present invention. As shown in Fig. 7, the resource recommendation device 70 can be used to execute the resource recommendation method shown in Fig. 3 and Fig. 6. The resource recommendation device 70 includes: a processing unit 701.

Processing unit 701 is used to determine the predicted number of concurrent connections of a target business, the type of cloud resources used in the target business, and multiple performance baselines of each first cloud resource among multiple first cloud resources; the first cloud resource is a cloud resource whose resource load is greater than or equal to a first threshold among multiple types of cloud resources that have been opened; a performance baseline is used to characterize the number of concurrent business connections that a first cloud resource can support; processing unit 701 is also used to determine, among multiple first cloud resources, a first cloud resource whose cloud resource type is the type of cloud resources used in the target business and whose performance baseline meets the predicted number of concurrent connections as a recommended configuration resource for the target business.

In one possible implementation, before determining the predicted number of concurrent connections of the target business, the device also includes: a communication unit 702; the communication unit 702 is used to obtain predicted business information of the target business; the predicted business information includes: the predicted number of online users, the type of cloud resources used in the target business, and the technology stack; the processing unit 701 is also used to determine the business profile of the target business based on the predicted business information.

In a possible implementation, the processing unit 701 is further configured to determine the predicted number of concurrent connections of the target service based on the predicted number of online users and a preset ratio.

In one possible implementation, each first cloud resource includes cloud resources of multiple specifications; the processing unit 701 is further used to determine the number of business connections of at least one second cloud resource; the second cloud resource is a cloud resource of any specification among the first cloud resources; the processing unit 701 is further used to determine the average number of business connections of at least one second cloud resource as the performance baseline of the first cloud resource.

In one possible implementation, processing unit 701 is also used to determine the total cost of recommended configuration resources; the total cost of recommended configuration resources includes the cost of the recommended configuration resources and the cost of supporting products required to use the recommended configuration resources; processing unit 701 is also used to determine the recommended configuration resources and the total cost of the recommended configuration resources as a resource recommendation list for the target business.

Through the description of the above implementation methods, technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

The present disclosure also provides a computer-readable storage medium having instructions stored thereon. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the resource recommendation method provided by the above-mentioned embodiment of the present disclosure.

The embodiments of the present disclosure also provide a computer program product including instructions, which, when executed on an electronic device, enables the electronic device to execute the resource recommendation method provided by the embodiments of the present disclosure.

Among them, the computer readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (ASIC). In the embodiments of the present application, a computer-readable storage medium may be any tangible medium that contains or stores a program, which may be used by or in conjunction with an instruction execution system, apparatus, or device.

The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (12)

1. A resource recommendation method, characterized in that the method comprises: Determine the predicted number of concurrent connections of a target business, the type of cloud resources used in the target business, and multiple performance baselines of each of multiple first cloud resources; the first cloud resource is a cloud resource whose resource load is greater than or equal to a first threshold among multiple types of cloud resources that have been enabled; one performance baseline is used to characterize the number of concurrent business connections that can be supported by one of the first cloud resources; A first cloud resource among the multiple first cloud resources, whose cloud resource type is the cloud resource type used in the target business and whose performance baseline meets the predicted number of concurrent connections, is determined as a recommended configuration resource for the target business. 2. The method according to claim 1, characterized in that before determining the predicted number of concurrent connections of the target service, the method further comprises: Acquire predicted business information of the target business; the predicted business information includes: predicted number of online users, cloud resource types used in the target business, and technology stack; A business profile of the target business is determined based on the predicted business information. 3. The method according to claim 2, wherein determining the predicted number of concurrent connections of the target service comprises: Based on the predicted number of online users and the preset ratio, the predicted number of concurrent connections of the target service is determined. 4. The method according to claim 3, wherein each of the first cloud resources comprises cloud resources of multiple specifications; and determining the performance baseline of each of the multiple first cloud resources comprises: Determine the number of service connections of at least one second cloud resource; the second cloud resource is a cloud resource of any specification among the first cloud resources; An average value of the number of service connections of the at least one second cloud resource is determined as a performance baseline of the first cloud resource. 5. The method according to any one of claims 1 to 4, characterized in that the method further comprises: Determine the total cost of the recommended configuration resources; the total cost of the recommended configuration resources includes the cost of the recommended configuration resources and the cost of supporting products required to use the recommended configuration resources; The recommended configuration resources and the total cost of the recommended configuration resources are determined as a resource recommendation list for the target business. 6. A resource recommendation device, characterized in that the device comprises: a processing unit; The processing unit is used to determine the predicted number of concurrent connections of the target business, the type of cloud resources used in the target business, and multiple performance baselines of each of the multiple first cloud resources; the first cloud resource is a cloud resource whose resource load is greater than or equal to a first threshold among the multiple types of cloud resources that have been opened; one performance baseline is used to characterize the number of concurrent business connections that can be supported by one of the first cloud resources; The processing unit is further used to determine, among the multiple first cloud resources, a first cloud resource whose cloud resource type is the cloud resource type used in the target business and whose performance baseline meets the predicted number of concurrent connections as a recommended configuration resource for the target business. 7. The device according to claim 6, characterized in that before determining the predicted number of concurrent connections of the target service, the device further comprises: a communication unit; The communication unit is used to obtain predicted business information of the target business; the predicted business information includes: the predicted number of online users, the type of cloud resources used in the target business, and the technology stack; The processing unit is further used to determine the business profile of the target business based on the predicted business information. 8. The device according to claim 7, characterized in that The processing unit is further configured to determine the predicted number of concurrent connections of the target service based on the predicted number of online users and a preset ratio. 9. The device according to claim 8, wherein each of the first cloud resources comprises cloud resources of multiple specifications; The processing unit is further used to determine the number of service connections of at least one second cloud resource; the second cloud resource is a cloud resource of any specification of the first cloud resource; The processing unit is further configured to determine an average value of the number of service connections of the at least one second cloud resource as a performance baseline of the first cloud resource. 10. The device according to any one of claims 6 to 9, characterized in that: The processing unit is further used to determine the total cost of the recommended configuration resources; the total cost of the recommended configuration resources includes the cost of the recommended configuration resources and the cost of the supporting products required to use the recommended configuration resources; The processing unit is further configured to determine the recommended configuration resources and the total cost of the recommended configuration resources as a resource recommendation list for the target business. 11. A resource recommendation device, characterized in that it comprises: a processor and a communication interface; the communication interface is coupled to the processor, and the processor is used to run a computer program or instruction to implement the resource recommendation method as described in any one of claims 1-5. 12. A computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, wherein when a computer executes the instructions, the computer executes the resource recommendation method described in any one of claims 1 to 5.