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

Resource recommendation method, device and storage medium Download PDF

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Publication number
CN117909068A
CN117909068A CN202410008935.8A CN202410008935A CN117909068A CN 117909068 A CN117909068 A CN 117909068A CN 202410008935 A CN202410008935 A CN 202410008935A CN 117909068 A CN117909068 A CN 117909068A
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resource
cloud
resources
target service
service
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孙鹏
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China United Network Communications Group Co Ltd
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China United Network Communications Group Co Ltd
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Priority to CN202410008935.8A priority Critical patent/CN117909068A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/55—Push-based network services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5072—Grid computing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

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  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mathematical Physics (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

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 communications technologies, and in particular, to a method, an apparatus, and a storage medium for recommending resources.
Background
With the rapid development of cloud service technology, users can select corresponding cloud resources to perform service deployment according to service requirements or service scale so as to realize rapid development of services. Currently, the method for selecting the corresponding cloud resource by the user may include: and obtaining the estimated service scale of the user, and determining the cloud resources of the single class based on the estimated service scale.
However, the method cannot determine the total cloud resources required by the user, so that the cloud resources selected by the user are single, and further the cloud resources which are close to the service requirements are difficult to obtain.
Disclosure of Invention
The application provides a resource recommendation method, a resource recommendation device and a storage medium, which can improve the matching degree between recommended configuration resources and service demands.
In order to achieve the above purpose, the application adopts the following technical scheme:
In a first aspect, the present application provides a resource recommendation method, including: 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.
In one possible implementation, before determining the predicted concurrent connection number of the target service, the method further includes: acquiring predicted service information of a target service; the predicted traffic information includes: predicting the number of online users, the type of cloud resources used in a target service and a technical stack; a traffic portrayal of the target traffic is determined based on the predicted traffic information.
In one possible implementation, determining the predicted concurrent connection number of the target service includes: and determining the predicted concurrent connection number of the target service based on the predicted online user number and the preset proportion.
In one possible implementation, each first cloud resource includes a plurality of specification cloud resources; determining a performance baseline for each first cloud resource of the plurality of first cloud resources, comprising: determining the service connection number of at least one second cloud resource; the second cloud resource is any specification cloud resource in the first cloud resource; and determining an average value of the service connection numbers of the at least one second cloud resource as a performance baseline of the first cloud resource.
In one possible implementation, determining a total cost of recommending configuration resources; the total cost of the recommended configuration resources comprises the cost of the recommended configuration resources and the cost of the matched products required by using the recommended configuration resources; the recommended configuration resource and the total cost of the recommended configuration resource are determined as a resource recommendation list of the target service.
In a second aspect, the present application provides a resource recommendation device, including: a processing unit; the processing unit is used for determining the predicted concurrent connection number of the target service, the type of cloud resources 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; the processing unit is further configured to determine, as a recommended configuration resource of the target service, a first cloud resource, of the plurality of first cloud resources, wherein the cloud resource type is a cloud resource type used in the target service, and the performance baseline satisfies the predicted concurrent connection number.
In one possible implementation, before determining the predicted concurrent connection number of the target service, the apparatus further includes: a communication unit; the communication unit is used for acquiring the predicted service information of the target service; the predicted traffic information includes: predicting the number of online users, the type of cloud resources used in a target service and a technical stack; and the processing unit is also used for determining the service portraits of the target service based on the predicted service information.
In a possible implementation manner, the processing unit is further configured to determine a predicted concurrent connection number of the target service based on the predicted online user number and the preset proportion.
In one possible implementation, each first cloud resource includes a plurality of specification cloud resources; the processing unit is also used for determining the service connection number of at least one second cloud resource; the second cloud resource is any specification cloud resource in the first cloud resource; and the processing unit is also used for determining the average value of the service connection numbers of the at least one second cloud resource as the performance baseline of the first cloud resource.
In one possible implementation, the processing unit is further configured to determine a total cost of recommended configuration resources; the total cost of the recommended configuration resources comprises the cost of the recommended configuration resources and the cost of the matched products required by using the recommended configuration resources; the processing unit is further used for determining the recommended configuration resources and the total cost of the recommended configuration resources as a resource recommendation list of the target service.
In a third aspect, the present application provides a resource recommendation device, including: a processor and a communication interface; the communication interface is coupled to a processor for running a computer program or instructions to implement the resource recommendation method as described in any one of the possible implementations of the first aspect and the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium having instructions stored therein which, when run on a terminal, cause the terminal to perform a resource recommendation method as described in any one of the possible implementations of the first aspect and the first aspect.
In a fifth aspect, the present application provides a computer program product comprising instructions which, when run on a resource recommendation device, cause the resource recommendation device to perform the resource recommendation method as described in any of the possible implementations of the first aspect and the first aspect.
In a sixth aspect, the present application provides a chip comprising a processor and a communication interface, the communication interface and the processor being coupled, the processor being for running a computer program or instructions to implement a resource recommendation method as described in any one of the possible implementations of the first aspect and the first aspect.
In particular, the chip provided in the present application further includes a memory for storing a computer program or instructions.
In the resource recommendation method provided by the embodiment of the application, the resource recommendation equipment determines the cloud resource type in the plurality of first cloud resources as the cloud resource type used in the target service, and the performance base line meets the requirements of the first cloud resource for predicting the concurrent connection number as the recommended configuration resource of the target service.
In addition, the resource recommendation device determines the performance base line of the cloud resource with the resource load larger than or equal to the first threshold value, so that the problem of inaccurate performance base line caused by too high or too low cloud resource load can be avoided, and further, the situation that the recommended configuration resource does not meet the service requirement of the target service can be avoided.
Drawings
Fig. 1 is a schematic structural diagram of a resource recommendation system according to an embodiment of the present application;
Fig. 2 is a schematic structural diagram of a resource recommendation device according to an embodiment of the present application;
FIG. 3 is a flowchart of a resource recommendation method according to an embodiment of the present application;
FIG. 4 is an exemplary diagram of determining a performance baseline provided by an embodiment of the present application;
FIG. 5 is an exemplary diagram of a resource recommendation list provided in an embodiment of the present application;
FIG. 6 is a flowchart of another resource recommendation method according to an embodiment of the present application;
Fig. 7 is a schematic structural diagram of another resource recommendation device according to an embodiment of the present application.
Detailed Description
The resource recommendation method, device and storage medium provided by the embodiment of the application are described in detail below with reference to the accompanying drawings.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone.
The terms "first" and "second" and the like in the description and in the drawings are used for distinguishing between different objects or between different processes of the same object and not for describing a particular order of objects.
Furthermore, references to the terms "comprising" and "having" and any variations thereof in the description of the present application are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.
It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g." in an embodiment should not be taken as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
With the rapid development of technologies such as cloud computing and cloud service, business software of most enterprises is changed from traditional single-machine local deployment to cloud deployment, so that the enterprises can provide public cloud or private cloud service to the outside, and users can select corresponding cloud resources (such as containers, database services, middleware rediss, middleware kafka and other resources) to perform business deployment, thereby realizing rapid development of the business. Currently, the method for selecting the corresponding cloud resource by the user may include: and calculating the load of the resources which are opened by the user, and determining the resources with the load within a preset range as recommended configuration resources based on the load of the opened resources. Or obtaining the estimated service scale of the user, and determining the cloud resources of the single class based on the estimated service scale.
However, the method cannot determine the total cloud resources required by the user, so that the cloud resources selected by the user are single, the calculation power of the cloud resources and the knowledge of the resource specification required by the service scale are shallow, and the time consumption for selecting the product specification of the cloud resources is long or the matching degree between the selected cloud resources and the actual demands of the service is low.
In view of this, the embodiment of the present application provides a resource recommendation method, in which a resource recommendation device determines, from a plurality of first cloud resources, a cloud resource type that is used in a target service, where a performance baseline satisfies a predicted concurrent connection number, and determines, as a recommended configuration resource for the target service, the first cloud resource that is used to characterize a service concurrent connection number that may be supported by the first cloud resource, where the recommended configuration resource may satisfy the predicted concurrent connection number, and where, in addition, since the first cloud resource is a cloud resource that is opened and has a resource load greater than or equal to a first threshold, the resource recommendation device may determine, from the plurality of cloud resources that are opened, all types of cloud resources that are used in the cloud resource type that is used in the target service, where the obtained recommended configuration resource is relatively comprehensive, and may better fit the service requirement of the target service.
In addition, the resource recommendation device determines the performance base line of the cloud resource with the resource load larger than or equal to the first threshold value, so that the problem of inaccurate performance base line caused by too high or too low cloud resource load can be avoided, and further, the situation that the recommended configuration resource does not meet the service requirement of the target service can be avoided.
Exemplary, as shown in fig. 1, fig. 1 shows a schematic structural diagram of a resource recommendation system provided by an embodiment of the present application. The resource recommendation system comprises: a resource recommendation device 101 and a user device 102. Fig. 1 illustrates an example in which a resource recommendation system includes a resource recommendation device 101 and a user device 102.
The resource recommendation device 101 is configured to determine a predicted concurrent connection number of the target service, a cloud resource type used in the target service, and a plurality of performance baselines of each of the plurality of first cloud resources, and determine, as a recommended configuration resource of the target service, a first cloud resource whose performance baselines satisfy the predicted concurrent connection number, the cloud resource type being a cloud resource type used in the target service, among the plurality of first cloud resources.
The user equipment 102 is configured to provide a data basis for the resource recommendation device 101 to determine the predicted concurrent connection number of the target service and the cloud resource type used in the target service.
The first cloud resources are cloud resources with the resource load being greater than or equal to a first threshold value in the opened cloud resources of a plurality of types. A performance baseline is used for representing the number of concurrent connections of the business supportable by the first cloud resource.
In one example, the resource recommendation device 101 may be a server. The server may be a single server or may be a server cluster formed by a plurality of 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 (terminal equipment) or a User Equipment (UE) or a Mobile Station (MS) or a Mobile Terminal (MT), or the like. Specifically, the resource recommendation device 101 may be a mobile phone (mobile phone), a tablet computer, or a computer with a wireless transceiver function, and may also be a Virtual Reality (VR) terminal, an augmented reality (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 smart grid, a wireless terminal in smart city (SMART CITY), a smart home, a vehicle-mounted terminal, and the like. In the embodiment of the present application, the means for implementing the function of the resource recommendation device 101 may be the resource recommendation device 101, or may be a means, such as a chip or a chip system, capable of supporting the resource recommendation device 101 to implement the function.
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), or the like. Specifically, the user device 102 may be a mobile phone (mobile phone), a tablet computer, or a computer with a wireless transceiver function, and may also be a Virtual Reality (VR) terminal, an augmented reality (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 smart grid, a wireless terminal in smart city (SMART CITY), a smart home, a vehicle-mounted terminal, and the like. In the embodiment of the present application, the means for implementing the function of the user equipment 102 may be the user equipment 102, or may be a means capable of supporting the user equipment 102 to implement the function, for example, a chip or a chip system.
In addition, the resource recommendation system described in the embodiments of the present application is for more clearly describing the technical solution of the embodiments of the present application, and does not constitute a limitation on the technical solution provided in the embodiments of the present application, and as a person of ordinary skill in the art can know that, with the evolution of the network architecture and the appearance of the new resource recommendation system, the technical solution provided in the embodiments of the present application is applicable to similar technical problems.
In particular, the apparatus of fig. 1 may employ the constituent structure shown in fig. 2, or may include the components shown in fig. 2. Fig. 2 is a schematic diagram of a resource recommendation device 200 according to an embodiment of the present application, where 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. Or the resource recommendation device 200 may be the resource recommendation device 102 or a chip or a system on chip in the resource recommendation device 102. As shown in fig. 2, the resource recommendation device 200 may include a processor 201 and a communication line 202.
Further, the resource recommendation device 200 may further include a communication interface 203 and a memory 204. The processor 201, the memory 204, and the communication interface 203 may be connected through a communication line 202.
The processor 201 is a CPU, a general-purpose processor, a network processor (network processor, NP), a digital signal processor (DIGITAL SIGNAL processing, DSP), a microprocessor, a microcontroller, a programmable logic device (programmable logic device, PLD), or any combination thereof. The processor 201 may also be other devices with processing functions, such as, without limitation, circuits, devices, or software modules.
A communication line 202 for transmitting information between the components included in the resource recommendation device 200.
Communication interface 203 for communicating with other devices or other communication networks. The other communication network may be an ethernet, a radio access network (radio access network, RAN), a wireless local area network (wireless local area networks, WLAN), etc. The communication interface 203 may be a module, a circuit, a communication interface, or any device capable of enabling communication.
Memory 204 for storing instructions. Wherein the instructions may be computer programs.
The memory 204 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device capable of storing static information and/or instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device capable of storing information and/or instructions, an EEPROM, a CD-ROM (compact disc read-only memory) or other optical disk storage, an optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, etc.
It should be noted that the memory 204 may exist separately from the processor 201 or may be integrated with the processor 201. Memory 204 may be used to store instructions or program code or some data, etc. The memory 204 may be located inside the resource recommendation device 200 or outside the resource recommendation device 200, and is not limited. The processor 201 is configured 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, processor 201 may include one or more CPUs, e.g., CPU0 and CPU1.
As an alternative implementation, the resource recommendation device 200 includes a plurality of processors.
As an alternative implementation, the resource recommendation apparatus 200 further includes an output device and an input device. The output device is illustratively a display screen, speaker (speaker), etc., and the input device is a keyboard, mouse, microphone, or joystick, etc.
It should be noted that the resource recommendation device 200 may be a desktop, a portable computer, a web server, a mobile phone, a tablet computer, a wireless terminal, an embedded device, a chip system, or a device having a similar structure as in fig. 2. Furthermore, the constituent structures shown in fig. 2 do not constitute limitations on the respective apparatuses in fig. 1 and 2, and the respective apparatuses in fig. 1 and 2 may include more or less components than illustrated, or may combine some components, or may be arranged differently, in addition to the components shown in fig. 2.
In the embodiment of the application, the chip system can be composed of chips, and can also comprise chips and other discrete devices.
Further, actions, terms, and the like, which are referred to between embodiments of the present application, are not limited thereto. The message names of interactions between the devices or parameter names in the messages in the embodiments of the present application are just an example, and other names may be used in specific implementations without limitation.
The resource recommendation method provided by the embodiment of the application is described below with reference to the resource recommendation system shown in fig. 1. In which the terms and the like related to the actions of the embodiments of the present application are mutually referred to, without limitation. The message names of interactions between the devices or parameter names in the messages in the embodiments of the present application are just an example, and other names may be used in specific implementations without limitation. The actions involved in the embodiments of the present application are just an example, and other names may be adopted in the specific implementation, for example: the "included" of the embodiments of the present application may also be replaced by "carried on" or the like.
In order to solve the problems in the prior art, the embodiment of the application provides a resource recommendation method, which can improve the matching degree between recommended configuration resources and service demands. As shown in fig. 3, the method includes:
S301, the resource recommendation device determines the predicted concurrent connection number of the target service, the type of cloud resources used in the target service and a plurality of performance baselines of each of the plurality of first cloud resources.
The first cloud resources are cloud resources with the resource load being greater than or equal to a first threshold value in the opened cloud resources of a plurality of types. A performance baseline is used for representing the number of concurrent connections of the business supportable by the first cloud resource.
In one possible embodiment, the implementation process of determining the predicted concurrent connection number of the target service by the resource recommendation device may be: the resource recommendation device determines the predicted concurrent connection number of the target service based on the predicted online user number and the preset proportion.
The predicted online user number may be determined by predicted service information of the target service or may be determined by a service representation of the target service.
Optionally, the preset ratio may be a ratio of the number of predicted online users to the number of predicted concurrent connections. For example, the ratio of the number of predicted online users to the number of predicted concurrent connections may be 10:1. The foregoing is merely an exemplary illustration of the preset ratio in the general algorithm in the industry, and the preset ratio may be other ratios, which is not limited in any way by the present application.
For example, taking the predicted online user number as 20000, the preset ratio is a ratio of the predicted online user number to the predicted concurrent connection number, and the ratio is 10:1 as an example: the resource recommendation device determines that the predicted concurrent connection number of the target service is 20000/10=2000.
It can be understood that the resource recommendation device can predict the number of concurrent connections of the actual demand of the target service (i.e. predict the number of concurrent connections) based on the predicted online user number and the preset proportion, so that the resource recommendation device can determine cloud resources of the actual demand of the target service according to the number of concurrent connections of the actual demand of the target service, and further can ensure that the recommended configuration resources of the target service meet the actual demand of the target service.
It is understood that, because the first cloud resource is the cloud resource with the resource load greater than or equal to the first threshold value in the plurality of types of opened cloud resources, the resource recommendation device can determine the performance baseline of the cloud resource with the resource load meeting a certain condition, so that the performance situation of the cloud resource under a certain load can be reflected more truly, the problem that the performance deviation of the cloud resource is larger under the condition of higher or lower load is avoided, and further, the recommendation configuration resource of the target service determined later can be more accurate.
It should be noted that the plurality of types of cloud resources that have been opened may include a plurality of types of cloud resources that have been opened for operation in all services, that is, the plurality of types of cloud resources that have been opened may include all cloud resources existing on the market.
Optionally, the resource recommendation device may set the first threshold according to a load condition of the cloud resource. For example, the resource recommendation device may determine that the first threshold is 75% of the cloud resource load. The above is merely an exemplary illustration of the first threshold, and the first threshold may be another value (e.g., 80% of the resource load), which is not limited in this way by the present application.
In one possible embodiment, each first cloud resource comprises a plurality of specification cloud resources. The implementation process of the resource recommendation device in determining the performance baseline of each first cloud resource in the plurality of first cloud resources may be: the resource recommendation device determines the service connection number of the at least one second cloud resource, and determines the average value of the service connection numbers of the at least one second cloud resource as a performance baseline of the first cloud resource. The second cloud resource is any specification cloud resource in the first cloud resource.
Illustratively, the plurality of first cloud resources may include at least one of: container resources, database resources, and middleware resources. The foregoing is merely an exemplary illustration of a first cloud resource, and the first cloud resource may further include other cloud resources (e.g., cloud server resources), which the present application is not limited to.
As an example, where the first cloud resource is a container resource, the first cloud resource may include a plurality of specifications of second cloud resources that are 4c8g containers and 8c16g containers. In the case where the first cloud resource is a database resource, the second cloud resource of the plurality of specifications included in the first cloud resource may be a 4c8g database and an 8c16g database. The foregoing is merely an exemplary illustration of the second cloud resource, and the second cloud resource may be another cloud resource with other specifications, which is not limited in this disclosure.
As shown in FIG. 4, FIG. 4 illustrates an exemplary diagram of a resource recommendation device determining a performance baseline. Illustratively, take at least one second cloud resource as 500 4c8g containers as an example: the resource recommendation device may determine the number of concurrent connections of each 4c8g container of the 500 4c8g containers, and determine an average value of the sum of the number of concurrent connections of the 500 4c8g containers as a performance baseline of the 4c8g containers. The resource recommendation device may determine that the performance baseline of the 4c8g container is one performance baseline of the first cloud resource.
Similarly, in the case that the second cloud resource is an 8c16g container, a 4c8g database, or a Redis master-slave version is 4c16g, the implementation process of determining the performance baseline by the resource recommendation device may be understood by referring to the description of the corresponding location, which is not described herein.
It can be understood that in the process that the resource recommendation device determines the performance baseline of the first cloud resource, the resource recommendation device may obtain the performance baselines of different specifications under the first cloud resource according to an average value of service connection numbers of the same second cloud resource in the first cloud resource.
In one possible implementation, the resource recommendation device may periodically determine a performance baseline for the first cloud resource. For example, the resource recommendation device may count a first cloud resource of the plurality of types of cloud resources that are already opened according to a month period, and determine a plurality of performance baselines of the first cloud resource.
Optionally, the resource recommendation device may save the plurality of performance baselines of the first cloud resource to a database for subsequent use.
S302, the resource recommendation device determines the first cloud resource with the performance baseline meeting the predicted concurrent connection number as the recommended configuration resource of the target service, wherein the cloud resource type is the cloud resource type used in the target service in the plurality of first cloud resources.
As an example, assuming that the cloud resource type used in the target service includes a container, a database, and a middleware redis and the predicted concurrent connection number is 2000, the resource recommendation apparatus may determine the container resource, the database resource, and the middleware redis resource in the first cloud resource, and determine the specification of the container resource whose performance baseline is 2000 from the container resources, the specification of the database resource whose performance baseline is 2000 from the database resources, and the specification of the middleware redis resource whose performance baseline is 2000 from the middleware redis resources. The resource recommendation device may determine the cloud resources with the performance baseline of 2000 and the specifications as recommended configuration resources of the target service.
Specifically, the recommended configuration resources of the target service may include container resources with a specification of 8c16g, database resources with a specification of 4c8g, and middleware redis resources with a specification of 4c16 g. The above exemplary description of the recommended configuration resource for the target service only may further include other resources, which is not limited in this way by the present application.
After the resource recommendation device determines the recommended configuration resource of the target service, the resource recommendation device can determine the cost of the recommended configuration resource and summarize the cost of the recommended configuration resource into a resource recommendation list, so that a user can quickly determine cloud resources required by the target service according to the resource recommendation list.
In one possible embodiment, the implementation process of determining, by the resource recommendation device, the resource recommendation list of the target service may be: the resource recommendation device determines a total cost of the recommended configuration resources and determines the recommended configuration resources and the total cost of the recommended configuration resources as a resource recommendation list of the target service. The total cost of the recommended configuration resource comprises the cost of the recommended configuration resource and the cost of the matched product required by using the recommended configuration resource.
For example, the above-described kits for using recommended configuration resources may be kits for container collocation, may include virtual private networks (virtual private cloud, VPC), etc.
As shown in FIG. 5, FIG. 5 illustrates an example diagram of a resource recommendation list. The resource recommendation list may include the target service required resources, the specifications of the target service required resources, the number of each specification resource, the prices of each specification resource in the target service required resources, and the total price of the target service required resources. The user can modify or open the resources deployed by the target service according to the resource recommendation list.
Alternatively, the resource recommendation device may send the resource recommendation list of the target service to the user device. The user equipment can receive the resource recommendation list of the target service, so that the user can quickly select and open cloud resources corresponding to the target service according to the resource recommendation list of the target service, and the problem of difficult selection of user resource specifications can be solved.
It should be noted that, the resource recommendation method provided by the embodiment of the application can be applied to public cloud or proprietary cloud service, so that in the process of deploying cloud resources by a target service, subsequent expansion or contraction of resources can be performed by the resource recommendation scheme, so that the cloud resources deployed by the target service meet the service requirements of the target service, the time for selecting resources by a user can be saved, the cloud loading efficiency is improved, and the user satisfaction is improved.
In the resource recommendation method provided by the embodiment of the application, the resource recommendation equipment determines the cloud resource type in the plurality of first cloud resources as the cloud resource type used in the target service, and the performance base line meets the requirements of the first cloud resource for predicting the concurrent connection number as the recommended configuration resource of the target service.
In addition, the resource recommendation device determines the performance base line of the cloud resource with the resource load larger than or equal to the first threshold value, so that the problem of inaccurate performance base line caused by too high or too low cloud resource load can be avoided, and further, the situation that the recommended configuration resource does not meet the service requirement of the target service can be avoided.
In a possible embodiment, before the resource recommendation device determines the predicted concurrent connection number of the target service, the resource recommendation device may obtain predicted service information of the target service, and determine a service representation of the target service according to the predicted service information, so that the resource recommendation device may determine a service requirement of the target service according to the service representation of the target service, and may further recommend cloud resources meeting the service requirement of the target service for the target service, and on the basis of the method embodiment shown in fig. 3, this embodiment provides a possible implementation manner, as shown in fig. 6 in conjunction with fig. 3, and the implementation process of determining the service representation of the target service by the resource recommendation device may be determined by the following S601 to S602.
S601, the resource recommendation device acquires predicted service information of a target service.
Wherein the predicted traffic information includes at least one of: predicting the number of online users, the type of cloud resources used in a target service, and a technology stack.
Alternatively, the above is merely an exemplary illustration of predicted traffic information, which may also include other information (e.g., predicting total registered user size). The present application is not limited in this regard.
As a possible implementation manner, the implementation process of S601 may be: the user can fill in information such as a predicted online user of the target service, a cloud resource type used in the target service, a technical stack and the like in a front-end page of the resource recommendation device. In response to the user operation, the resource recommendation device may determine, as predicted service information of the target service, information about the target service that the user fills in on the front-end page.
As another possible implementation manner, the implementation process of S601 may further be: in response to a user operation, the user equipment may determine predicted traffic information of the target traffic and send the predicted traffic information of the target traffic to the resource recommendation device. The resource recommendation device may receive the predicted traffic information of the target traffic.
As an example, the predicted traffic information of the target traffic may include: the predicted total registered user scale is 100000, the predicted online user number is 20000, 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 the service portraits of the target service based on the predicted service information.
As a possible implementation manner, the implementation process of S602 may be: the resource recommendation device may use a knowledge graph technique to represent the predicted traffic information as a graph and analyze the graph to determine key features and attributes of the target traffic (e.g., the type of cloud resources used by the target traffic and the predicted online users). The resource recommendation device may determine the key features and attributes of the target service as a service representation of the target service.
The resource recommendation device may verify whether the service representation of the target service is consistent with the actual service of the target service. Under the condition that the service portraits of the target service are inconsistent with the actual service, the resource recommendation equipment can optimize the atlas corresponding to the predicted service information and re-extract the service portraits of the target service so as to ensure the accuracy and the effectiveness of the service portraits of the target service.
In the resource recommendation method provided by the embodiment of the application, the resource recommendation equipment determines the service image of the target service based on the predicted service information of the target service, so that the service requirement of the target service can be better understood, further, a basis can be provided for the follow-up determination of the recommended configuration resource, the resource recommendation equipment can determine the cloud resource which is relatively attached to the service requirement of the target service for the target service by analyzing the service image of the target service, and further, the matching degree between the recommended configuration resource and the service requirement of the target service can be improved.
It is understood that the resource recommendation method described above may be implemented by a resource recommendation device. The resource recommendation device comprises a hardware structure and/or a software module corresponding to each function for realizing the functions. Those of skill in the art will readily appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments.
The disclosed embodiment of the application can divide the function modules according to the resource recommendation device generated by the method example, for example, each function module can be divided corresponding to each function, and two or more functions can be integrated in one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that, in the embodiment of the present application, the division of the modules is schematic, which is merely a logic function division, and other division manners may be implemented in actual implementation.
Fig. 7 is a schematic structural diagram of a resource recommendation device according to an embodiment of the present invention. As shown in fig. 7, the resource recommendation device 70 may be used to perform the resource recommendation method shown in fig. 3 and 6. The resource recommendation device 70 includes: a processing unit 701.
A processing unit 701, configured to determine 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 of a 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; the processing unit 701 is further configured to determine, as a recommended configuration resource of the target service, a first cloud resource whose cloud resource type is a cloud resource type used in the target service and whose performance baseline satisfies the predicted concurrent connection number, from among the plurality of first cloud resources.
In one possible implementation, before determining the predicted concurrent connection number of the target service, the apparatus further includes: a communication unit 702; a communication unit 702, configured to obtain predicted service information of a target service; the predicted traffic information includes: predicting the number of online users, the type of cloud resources used in a target service and a technical stack; the processing unit 701 is further configured to determine a service representation of the target service based on the predicted service information.
In a possible implementation manner, the processing unit 701 is further configured to determine a predicted concurrent connection number of the target service based on the predicted online user number and the preset proportion.
In one possible implementation, each first cloud resource includes a plurality of specification cloud resources; the processing unit 701 is further configured to determine a service connection number of at least one second cloud resource; the second cloud resource is any specification cloud resource in the first cloud resource; the processing unit 701 is further configured to determine an average value of the service connection numbers of the at least one second cloud resource as a performance baseline of the first cloud resource.
In a possible implementation, the processing unit 701 is further configured to determine a total cost of recommending configuration resources; the total cost of the recommended configuration resources comprises the cost of the recommended configuration resources and the cost of the matched products required by using the recommended configuration resources; the processing unit 701 is further configured to determine the recommended configuration resource and the total cost of the recommended configuration resource as a resource recommendation list of the target service.
From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.
The present disclosure also provides a computer-readable storage medium having instructions stored thereon that, when executed by a processor of an electronic device, enable the electronic device to perform the resource recommendation method provided by the embodiments of the present disclosure described above.
The disclosed embodiments also provide a computer program product containing instructions that, when executed on an electronic device, cause the electronic device to perform the resource recommendation method provided by the disclosed embodiments.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (Random Access Memory, RAM), a read-only memory (ROM), an erasable programmable read-only memory (Erasable Programmable Read Only Memory, EPROM), a register, a hard disk, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an Application SPECIFIC INTEGRATED Circuit (ASIC). In embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The present application is not limited to the above embodiments, and any changes or substitutions within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims (12)

1. A method for recommending resources, the method comprising:
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, and the resource load of the cloud resources is larger than or equal to a first threshold value; one performance baseline is used for representing the number of concurrent service connections supportable by one first cloud resource;
And determining the first cloud resources with the performance baseline 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.
2. The method of claim 1, wherein prior to said determining the predicted number of concurrent connections for the target traffic, the method further comprises:
acquiring predicted service information of the target service; the predicted traffic information includes: predicting the number of online users, the type of cloud resources used in the target service and a technical stack;
and determining the service portraits of the target service based on the predicted service information.
3. The method of claim 2, wherein determining the predicted number of concurrent connections for the target traffic comprises:
And determining the predicted concurrent connection number of the target service based on the predicted online user number and a preset proportion.
4. The method of claim 3, wherein each first cloud resource comprises a plurality of specification cloud resources; the determining a performance baseline for each first cloud resource of the plurality of first cloud resources includes:
Determining the service connection number of at least one second cloud resource; the second cloud resource is any specification cloud resource in the first cloud resource;
and determining an average value of the service connection numbers of the at least one second cloud resource as a performance baseline of the first cloud resource.
5. The method according to any one of claims 1-4, further comprising:
Determining a total cost of the recommended configuration resource; the total cost of the recommended configuration resources comprises the cost of the recommended configuration resources and the cost of the matched products required by using the recommended configuration resources;
And determining the recommended configuration resource and the total cost of the recommended configuration resource as a resource recommendation list of the target service.
6. A resource recommendation device, the device comprising: a processing unit;
The processing unit is used for determining the predicted concurrent connection number of the target service, the type of the cloud resources 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, and the resource load of the cloud resources is larger than or equal to a first threshold value; one performance baseline is used for representing the number of concurrent service connections supportable by one first cloud resource;
The processing unit is further configured to determine, as a recommended configuration resource of the target service, a first cloud resource whose cloud resource type is a cloud resource type used in the target service and the performance baseline satisfies the predicted concurrent connection number, from the plurality of first cloud resources.
7. The apparatus of claim 6, wherein prior to said determining the predicted number of concurrent connections for the target traffic, the apparatus further comprises: a communication unit;
The communication unit is used for acquiring the predicted service information of the target service; the predicted traffic information includes: predicting the number of online users, the type of cloud resources used in the target service and a technical stack;
The processing unit is further configured to determine a service representation of the target service based on the predicted service information.
8. The apparatus of claim 7, wherein the device comprises a plurality of sensors,
The processing unit is further configured to determine a predicted concurrent connection number of the target service based on the predicted online user number and a preset proportion.
9. The apparatus of claim 8, wherein each first cloud resource comprises a plurality of specification cloud resources;
The processing unit is further used for determining the service connection number of at least one second cloud resource; the second cloud resource is any specification cloud resource in the first cloud resource;
The processing unit is further configured to determine an average value of service connection numbers 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, wherein,
The processing unit is further configured to determine a total cost of the recommended configuration resource; the total cost of the recommended configuration resources comprises the cost of the recommended configuration resources and the cost of the matched products required by using the recommended configuration resources;
the processing unit is further configured to determine the recommended configuration resource and the total cost of the recommended configuration resource as a resource recommendation list of the target service.
11. A resource recommendation device, comprising: a processor and a communication interface; the communication interface being coupled to the processor for executing a computer program or instructions for implementing the resource recommendation method as claimed in any of the claims 1-5.
12. A computer readable storage medium having instructions stored therein, characterized in that when executed by a computer, the computer performs the resource recommendation method according to any of the preceding claims 1-5.
CN202410008935.8A 2024-01-03 2024-01-03 Resource recommendation method, device and storage medium Pending CN117909068A (en)

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