CN113760516A - A method, device, device and medium for elastic scaling in a multi-cloud environment - Google Patents

Abstract

The present application provides an elastic scaling method in a multi-cloud environment, including: modeling resources provided by multiple cloud platforms, obtaining resource models of multiple cloud platforms, and monitoring indicators of application instances deployed on multiple cloud platforms When the value meets the preset conditions, the application instance is adjusted according to the resource models and elastic scaling policies of multiple cloud platforms. In this way, in the environment of multiple cloud platforms, automatic and cross-cloud elastic scaling of application instances is realized without manual intervention, which improves the efficiency of elastic scaling and avoids the waste of resources or the difficulty of guaranteeing service quality caused by manual intervention. The entire scaling process does not require manual participation, and can achieve elastic scaling of application instances across cloud platforms in minutes or even seconds, improving resource utilization and ensuring service quality.

Description

A method, device, device and medium for elastic scaling in a multi-cloud environment

technical field

The present application relates to the field of computer technologies, and in particular, to an elastic scaling method, apparatus, device, and computer-readable storage medium in a multi-cloud environment.

Background technique

Cloud technology refers to a kind of hosting technology that unifies a series of resources such as hardware, software, and network in a wide area network or a local area network to realize the calculation, storage, processing and sharing of data. A service provider that provides services through cloud technology according to user needs is a cloud service provider.

Applications can be deployed on cloud platforms provided by cloud service providers to provide external services. To ensure service quality and save resources, the industry proposes an elastic scaling method. Specifically, monitor specific indicators of the application, such as processor (central process unit, CPU) usage rate and memory usage rate, and when the indicator value of the specific indicator reaches a preset threshold, adjust the individual application instance according to the set elastic scaling policy number.

However, the above elastic scaling methods are mainly applicable to applications deployed on a single cloud platform. For applications deployed in a multi-cloud platform, that is, a multi-cloud environment, manual intervention is often required, which makes it difficult to achieve rapid elastic scaling, resulting in wasted resources or difficult to guarantee service quality.

SUMMARY OF THE INVENTION

The present application provides an elastic scaling method in a multi-cloud environment, which resolves the problems in the related art that manual intervention is required, rapid elastic scaling is difficult to achieve, and resources are wasted or service quality is difficult to guarantee. The present application also provides apparatuses, devices, computer-readable storage media, and computer program products corresponding to the above methods.

In a first aspect, the present application provides an elastic scaling method in a multi-cloud environment. The method is used to realize automatic, cross-cloud elastic scaling of application instances in the environment of multiple cloud platforms, without manual intervention, which improves the efficiency of elastic scaling, and avoids the waste of resources or the difficulty in guaranteeing service quality caused by manual intervention.

Specifically, each of the multiple cloud platforms is provided with at least one resource. The resource can be a hardware resource or a software resource that provides a service. Resources can also be classified into computing resources, storage resources, and network resources according to their functions. For example, computing resources may include processor resources, such as central processing unit (CPU, central processing unit) resources. The storage resources may include memory resources, external memory resources, and the like. The memory resource may be an internal memory, and the external memory resource may be a hard disk, an optical disk, a flash disk, and the like.

During specific implementation, the cross-cloud elastic scaling controller may first model resources provided by multiple cloud platforms to obtain resource models of the multiple cloud platforms. The resource model can realize the mapping and transformation of the same type of resources among multiple cloud platforms, for example, realize the mapping and transformation of storage resources between cloud platform A and cloud platform B. Then, when the monitoring index values of the application instances deployed on the multiple cloud platforms meet the preset conditions, for example, when the monitoring index value reaches the set threshold, the cross-cloud elastic scaling controller can The resource model and the elastic scaling strategy are used to adjust the application instance. Since no manual intervention is required, application instances can be scaled elastically in minutes or even seconds across cloud platforms, improving resource utilization and ensuring service quality.

In some possible implementations, the cross-cloud elastic scaling controller may be configured with one or more elastic scaling policies. For example, the cross-cloud elastic scaling controller may be configured with one or more of an affinity policy, an anti-affinity policy, a cost priority policy, and a performance priority policy. The affinity policy specifically refers to configuring the application instance in a close region (region) of the same cloud platform, and the close region includes the same region, or even close nodes (including the same node) in the same region. The anti-affinity policy specifically refers to avoiding configuring application instances in adjacent regions and adjacent nodes in the same region. The cost-first strategy refers to the strategy of elastic scaling with the goal of minimum cost, and the performance-first strategy refers to the strategy of elastic scaling with the goal of the highest performance.

Based on this, the cross-cloud elastic scaling controller can adjust the Applications. In this way, application instances can be elastically scaled according to user requirements.

In some possible implementations, the cross-cloud elastic scaling controller may adjust the number of application instances according to resource models of multiple cloud platforms and elastic scaling policies when elastically scaling application instances. For example, adding an application instance, or deleting an application instance. Of course, the cross-cloud elastic scaling controller may also adjust the configuration of the application instance according to the resource models and elastic scaling policies of the multiple cloud platforms. For example, increase or decrease the computing resources, storage resources and/or network resources of the application instance.

In some possible implementations, the cross-cloud elastic scaling controller can debug the application instance in the following ways. Specifically, the cross-cloud elastic scaling controller determines the target cloud platform according to the resource models of the multiple cloud platforms and the elastic scaling policy, and then creates a new application through the application programming interface (API) of the target cloud platform instance. The new instance is deployed on the target cloud platform.

In some possible implementations, after creating a new application instance, when the monitoring indicator values of the application instances deployed on the multiple cloud platforms do not meet the preset conditions, the cross-cloud elastic scaling controller deletes the application through the API The new application example.

In some possible implementations, the multiple cloud platforms include multiple public cloud platforms, or multiple private cloud platforms, or a hybrid cloud platform formed by at least one public cloud platform and at least one private cloud platform. Considering the cost issue, you can choose multiple public cloud platforms to deploy application instances. Considering security issues, you can choose multiple private cloud platforms to deploy application instances. Considering cost issues and security issues comprehensively, a hybrid cloud platform formed by at least one public cloud platform and at least one private cloud platform can be selected to deploy application instances.

In a second aspect, the present application provides an elastic scaling device in a multi-cloud environment. The device includes:

a modeling unit, configured to model resources provided by multiple cloud platforms, and obtain resource models of the multiple cloud platforms;

An adjustment unit, configured to adjust the application instances according to the resource models of the multiple cloud platforms and the elastic scaling policy when the monitoring index values of the application instances deployed on the multiple cloud platforms meet preset conditions.

In some possible implementations, the adjustment unit is specifically used for:

The application instance is adjusted according to the resource models of the multiple cloud platforms and any one or more strategies of an affinity strategy, an anti-affinity strategy, a cost priority strategy, and a performance priority strategy.

In some possible implementations, the adjustment unit is specifically used for:

Adjust the number of the application instances according to the resource models and elastic scaling policies of the multiple cloud platforms; or,

The configuration of the application instance is adjusted according to the resource models of the multiple cloud platforms and the elastic scaling policy.

In some possible implementations, the adjustment unit is specifically used for:

Determine the target cloud platform according to the resource models of the multiple cloud platforms and the elastic scaling strategy;

A new application instance is created through the application programming interface (API) of the target cloud platform.

In some possible implementations, the adjustment unit is further used for:

After a new application instance is created, when the monitoring index values of the application instances deployed on the multiple cloud platforms do not meet the preset conditions, the new application instance is deleted through the API.

In some possible implementations, the multiple cloud platforms include multiple public cloud platforms, or multiple private cloud platforms, or a hybrid cloud platform formed by at least one public cloud platform and at least one private cloud platform.

In a third aspect, the present application provides an apparatus including a processor and a memory. The processor and the memory communicate with each other. The processor is configured to execute the instructions stored in the memory, so that the device executes the method for elastic scaling in a multi-cloud environment according to the first aspect or any implementation manner of the first aspect.

In a fourth aspect, the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and the instructions instruct a device to execute the first aspect or any implementation manner of the first aspect. An elastic scaling method in a multi-cloud environment.

In a fifth aspect, the present application provides a computer program product comprising instructions, which, when executed on a device, enables the device to perform the elasticity in a multi-cloud environment according to the first aspect or any implementation manner of the first aspect Scaling method.

On the basis of the implementation manners provided by the above aspects, the present application may further combine to provide more implementation manners.

Description of drawings

In order to illustrate the technical methods of the embodiments of the present application more clearly, the following briefly introduces the accompanying drawings used in the embodiments.

FIG. 1 is a schematic diagram of an application scenario of an elastic scaling method in a multi-cloud environment provided by an embodiment of the present application;

FIG. 2 is an architectural diagram of an elastic scaling method in a multi-cloud environment provided by an embodiment of the present application;

FIG. 3 is an architectural diagram of an elastic scaling method in a multi-cloud environment provided by an embodiment of the present application;

FIG. 4 is a schematic structural diagram of an elastic scaling controller according to an embodiment of the present application;

FIG. 5 is a flowchart of an elastic scaling method in a multi-cloud environment provided by an embodiment of the present application;

FIG. 6 is a schematic structural diagram of elastic scaling in a multi-cloud environment according to an embodiment of the present application;

FIG. 7 is a schematic structural diagram of a device provided by an embodiment of the present application.

Detailed ways

The terms "first" and "second" in the embodiments of the present application are only used for the purpose of description, and cannot be understood as indicating or implying relative importance or implying the number of indicated technical features. Thus, a feature defined as "first" or "second" may expressly or implicitly include one or more of that feature.

First, some technical terms involved in the embodiments of this application are introduced.

Cloud technology refers to a kind of hosting technology that unifies a series of resources such as hardware, software, and network in a wide area network or a local area network to realize the calculation, storage, processing and sharing of data. A service provider that provides services through cloud technology according to user needs is a cloud service provider, and the services provided by a cloud service provider through cloud technology are also called cloud services.

Cloud services include the following service modes: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). In some implementations, the cloud service may also include a function as a service (Function as a Service, FaaS) model.

In the IaaS model, service providers provide hardware resources, and users deploy operating systems, middleware, and runtime libraries by themselves, and then install software by themselves.

In the PaaS model, service providers not only provide hardware resources, but also deploy operating systems, provide middleware and runtime libraries, etc., and users install software by themselves.

In the SaaS model, service providers provide hardware resources, deploy operating systems, and provide basic environments such as middleware and runtime libraries. In addition, service providers provide software that users can use directly.

In the FaaS model, software (such as applications) is abstracted into functions. The function start is executed only when the program is called. When the program is not called, it will not start, so it will not occupy resources.

A cloud platform, also known as a cloud system, cloud environment or cloud, is a software system for cloud providers to provide cloud services. It should be noted that the software system may be a software system that provides cloud services in an IaaS model, a PaaS model, a SaaS model, or a FaaS model.

Public cloud is a cloud platform provided by third-party public cloud providers for the majority of individuals or enterprises. In a public cloud, hardware, software, and other structures are owned and managed by a third-party public cloud provider.

A private cloud is a dedicated cloud platform provided for an enterprise or organization. A private cloud can be operated internally by the corresponding enterprise or organization. Private cloud is mainly for enterprise users, also known as enterprise cloud.

Hybrid cloud refers to a cloud platform formed by different cloud platforms. Hybrid cloud includes at least two cloud platforms, also known as multi-cloud platform or multi-cloud. Optionally, a hybrid cloud combines public and private clouds. For security reasons, some enterprise users prefer to store data in the private cloud, but at the same time hope to obtain the computing resources of the public cloud. In this context, hybrid clouds including public and private clouds are increasingly being adopted. A hybrid cloud mixes and matches public and private clouds for good use.

An application programming interface (API), also known as a program interface, is an "interface between a program and an operating system" provided by the operating system of the cloud platform to users, such as programmers. This interface may be used by a user (eg, a developer) while programming. Through the application program interface, users can access resources in the cloud platform and obtain corresponding services. An API is a collection of definitions, functions, procedures, and/or protocols. For example, an application program interface of a cloud platform includes one or more system calls, and each system call is a program that can perform a specific function.

API Gateway (API Gateway, APIG) is specifically a gateway that provides API hosting services. The API gateway can perform unified authentication, metering, publishing, and/or flow control management for APIs.

A resource can be a hardware resource or a software resource that provides a service. Resources can also be classified into computing resources, storage resources, and network resources according to their functions. For example, computing resources may include processor resources, such as central processing unit (CPU, central processing unit) resources. The storage resources may include memory resources, external memory resources, and the like. The memory resource may be an internal memory, and the external memory resource may be a hard disk, an optical disk, a flash disk, and the like.

Auto scaling is a service that automatically adjusts its business resources through policies according to users' business needs. For example, when the user's service volume is large, service resources can be automatically increased through a policy, such as adding an application instance corresponding to the service. The application instance refers to an instance (instance) created according to the application. An instance can be thought of as a running application. An instance is spawned by starting (or opening) an application.

Cloud service providers can provide services to users through cloud platforms. To ensure service quality and save resources, the industry proposes an elastic scaling method. Specifically, specific indicators of the application, such as CPU usage and memory usage, are monitored, and when the indicator value of the specific indicator reaches a preset threshold, the number of application instances is adjusted according to the set elastic scaling policy.

However, the above elastic scaling methods are mainly applicable to applications deployed on a single cloud platform. For applications deployed in a multi-cloud platform, that is, a multi-cloud environment, manual intervention is often required, which makes it difficult to achieve rapid elastic scaling, resulting in wasted resources or difficult to guarantee service quality.

In view of this, the present application provides an elastic scaling method in a multi-cloud environment. The method can be executed by the cross-cloud elastic scaling controller. Specifically, the cross-cloud elastic scaling controller may first model resources provided by multiple cloud platforms to obtain resource models of the multiple cloud platforms. The resource model can realize the mapping and transformation of the same type of resources among multiple cloud platforms, for example, realize the mapping and transformation of storage resources between cloud platform A and cloud platform B. Then, when the monitoring index values of the application instances deployed on the multiple cloud platforms meet the preset conditions, for example, when the monitoring index value reaches the set threshold, the cross-cloud elastic scaling controller can The resource model and the elastic scaling strategy are used to adjust the application instance.

In this way, in the environment of multiple cloud platforms, automatic and cross-cloud elastic scaling of application instances is realized without manual intervention, which improves the efficiency of elastic scaling and avoids the waste of resources or the difficulty of guaranteeing service quality caused by manual intervention. The entire scaling process does not require manual participation, and can achieve elastic scaling of application instances across cloud platforms in minutes or even seconds, improving resource utilization and ensuring service quality.

In order to make the technical solutions of the present application clearer and easier to understand, the following describes application scenarios of the elastic scaling method in a multi-cloud environment provided by the embodiments of the present application with reference to the accompanying drawings.

Referring to the schematic diagram of an application scenario of the elastic scaling method in a multi-cloud environment shown in FIG. 1 , as shown in FIG. 1 , the scenario includes an elastic scaling controller 102 and multiple cloud platforms 104 . The multiple cloud platforms 104 may be respectively denoted as cloud platform 1 to cloud platform n, where n is greater than 1.

At least one cloud platform from platform 1 to cloud platform n has an instance of application A deployed. Wherein, at least one cloud platform can deploy one or more instances of application A. In one example, cloud platform 1 deploys multiple instances of application A, specifically instance 11 to instance 1m, and cloud platform n deploys multiple instances of application A, specifically instance n1 to instance nm. where m is a positive integer.

The elastic scaling controller 102 is connected to the above-mentioned multiple cloud platforms 104, for example, through a communication channel. The elastic scaling controller 102 models the resources provided by the multiple cloud platforms 104 to obtain resource models of the multiple cloud platforms 104 . When the monitoring index values of the application instances deployed on the multiple cloud platforms 104 meet the preset conditions, the elastic scaling controller 102 adjusts the application instances according to the resource models of the multiple cloud platforms and the elastic scaling policy. In this way, automatic elastic scaling in a multi-cloud environment is achieved.

As shown in FIG. 2 , the elastic scaling controller 102 may be deployed in a cloud environment, specifically, one or more computing devices (eg, a central server) on the cloud environment. The elastic scaling controller 102 may also be deployed in an edge environment, specifically on one or more computing devices (edge computing devices) in the edge environment, and the edge computing devices may be servers, computing boxes, and the like. The cloud environment indicates a cluster of central computing devices owned by the cloud service provider and used to provide computing, storage, and communication resources; the edge environment indicates that the edge environment is geographically close to the end device (ie, end-side device). Clusters of edge computing devices that provide computing, storage, and communication resources.

The above-mentioned elastic scaling controller 102 may also be deployed on a terminal device. End devices include physical machines such as terminals. Wherein, the terminal includes but is not limited to a desktop computer, a notebook computer, a tablet computer or a smart phone. The elastic scaling controller 102 can also be deployed in a virtual machine (virtual machine) or a container (container) on the above-mentioned physical machine. Considering load balancing and reliability, in some implementations, the elastic scaling controller 102 may also be deployed in a cluster in the form of multiple copies.

Further, as shown in FIG. 3 , the elastic scaling controller 102 may include multiple parts (eg, including multiple functional modules). Based on this, various parts of the elastic scaling controller 102 can also be deployed in different environments in a distributed manner. For example, a part of the elastic scaling controller 102 may be deployed on three environments of a cloud environment, an edge environment, and an end device, or any two of the environments.

The functional modules inside the elastic scaling controller 102 may be divided in various manners, which are not limited in this application. FIG. 4 is an exemplary division manner. As shown in FIG. 4 , the elastic scaling controller 102 includes an indicator monitoring module 1022 and an elastic scaling control module 1024 . In some implementations, the elastic scaling controller 102 may further include one or more of an elastic scaling policy management module 1026 , a multi-cloud cost insight module 1028 , and a multi-cloud performance evaluation module 1029 .

The indicator monitoring module 1022 is configured to read the monitoring indicator value corresponding to the application-related monitoring indicator through the API opened by the monitoring system of each cloud platform. When the monitoring index value satisfies a preset condition, for example, when the monitoring index value reaches a preset threshold, the elastic scaling control module 1024 may adjust the application instance according to the resource models of the multiple cloud platforms 104 and the elastic scaling policy.

In some manners, the elastic scaling policy management module 1026 is configured to manage the elastic scaling policy for use by the elastic scaling control module 1024 . Specifically, the elastic scaling policy management module 1026 provides a cross-cloud elastic scaling policy management component, and a user can configure an elastic scaling policy based on various application performances or other user-defined indicators through the cross-cloud elastic scaling policy management component. The elastic scaling strategy may include one or more of an affinity strategy, an anti-affinity strategy, a performance priority strategy or a cost priority strategy.

Regarding the elastic scaling policy, the embodiments of the present application only describe the affinity policy, the anti-affinity policy, the cost priority policy, the performance priority policy, and the like. In some implementations, the elastic scaling controller 102 may also standardize and plug-in the elastic scaling policy management module 1026 . If more similar policies need to be supported in the future, third-party developers can develop and select corresponding plug-ins as needed.

The affinity policy specifically refers to configuring the application instance in a similar region (including the same region) of the same cloud platform, or even a similar node (including the same node) in the same region. Specifically, the anti-affinity policy refers to avoiding configuring application instances in adjacent regions or adjacent nodes in the same region. When the application requires high response efficiency, the application instance can be configured in a similar region of the same cloud platform. When applications have high requirements on service reliability, it is possible to avoid configuring application instances in similar regions of the same cloud platform.

The multi-cloud cost insight module 1028 is used to evaluate the cost of resources on multiple cloud platforms. For the public cloud, the multi-cloud cost insight module 1028 can access the resource quotation system of the public cloud to obtain quotations of various types of resources and various specifications, so as to obtain the costs of various types of resources on the public cloud in various specifications. For a private cloud, the multi-cloud cost insight module 1028 can analyze the one-time fixed investment cost and periodic maintenance cost of the private cloud according to the hardware configuration, operating system configuration and other configuration information of the private cloud, and determine the cost of various types of resources based on the cost. Costs, such as determining the usage cost of various computing, storage, network and other resources per unit of time, unit of measurement, and unit of measurement.

The multi-cloud performance evaluation module 1029 is used to evaluate the performance of resources on each cloud platform. Specifically, the multi-cloud performance evaluation module 1029 can perform performance tests on the same configuration resources of different cloud platforms and specifications, such as running score tests, to obtain performance such as computing rate, network rate, and disk input/output (I/O) rate. value.

When the cost priority strategy is adopted for elastic scaling, the elastic scaling control module 1024 can also obtain the costs of various types of resources on multiple cloud platforms 104 from the multi-cloud cost insight module 1028, and then based on the resource models of the multiple cloud platforms 104, The application instance is adjusted according to the cost of various types of resources and various specifications on the multiple cloud platforms 104 and the cost priority policy.

When the performance-priority policy is adopted for elastic scaling, the elastic scaling control module 1024 can also obtain the performance of various types of resources on multiple cloud platforms 104 from the multi-cloud performance evaluation module 1029, and then based on the resource models of the multiple cloud platforms 104, The application instance is adjusted according to the performance of various types of resources on the multiple cloud platforms 104 and the performance priority policy of various specifications.

Next, from the perspective of an elastic scaling controller, the elastic scaling method provided in the embodiment of the present application in a multi-cloud environment is introduced.

Referring to the flowchart of the elastic scaling method in a multi-cloud environment shown in FIG. 5, the method includes:

S502: The elastic scaling controller 102 models resources provided by multiple cloud platforms to obtain resource models of the multiple cloud platforms.

The resource model can realize the mapping and transformation of the same type of resources among multiple cloud platforms, for example, realize the mapping and transformation of storage resources between cloud platform A and cloud platform B. In some implementations, resources can be characterized by resource parameters. The resource parameter may specifically be a parameter describing the resource configuration situation. For example, for computing resources, the resource parameter may include the number of cores, for example, the CPU is 4 cores. For another example, for a storage resource, the resource parameter may include a memory capacity, for example, the memory is 2GB (gigabyte, GigByte).

Resource parameter format (schema, also called specification), referred to as resource parameter format. For example, the resource parameter format corresponding to a certain resource may be: the resource parameter is a computing resource specification parameter such as the number of CPUs or the number of GPUs. During specific implementation, the elastic scaling controller 102 may unify the resource parameter format to implement unified modeling of resources.

In some implementations, the elastic scaling controller 102 may obtain a standard resource parameter format, and then establish the resource parameter format supported by at least one cloud platform (for example, each cloud platform) among the multiple cloud platforms 104 and the above standard resource parameter format Thereby, the resource models of the multiple cloud platforms 104 are obtained, and the resource models may also be called escape models. Taking the standard resource parameter format in the resource model as a reference value, and escaping the resource parameter format, the mapping and conversion relationship of the same type of resources among multiple cloud platforms can be obtained.

The standard resource parameter format may be a resource parameter format supported by one cloud platform among the multiple cloud platforms 104 . Certainly, the standard resource parameter format may also be obtained by processing a resource parameter format supported by at least one cloud platform among the multiple cloud platforms 104 .

S504: When the monitoring index values of the application instances deployed on the multiple cloud platforms 104 satisfy the preset conditions, the elastic scaling controller 102 adjusts the application instances according to the resource models and elastic scaling policies of the multiple cloud platforms .

The elastic scaling controller 102 may monitor the indicators of the application instances deployed on the multiple cloud platforms 104 to obtain monitoring indicator values. Specifically, the elastic scaling controller 102 may read the monitoring indicator values from the monitoring system on the cloud platform 104 . The monitoring system may include indicator values of multiple monitoring indicators, and the elastic scaling controller 102 obtains corresponding monitoring indicator values according to actual requirements. In some examples, the monitoring indicator values obtained by the elastic scaling controller 102 may be CPU usage, memory usage, and the like.

When the monitoring index value satisfies the preset condition, the elastic scaling controller 102 may adjust the application instance according to the resource models of the multiple cloud platforms 104 and the elastic scaling policy. The preset conditions can be set according to actual needs. For example, the preset condition may be set such that the value of the monitoring indicator is greater than the threshold value corresponding to the indicator, or the value of the monitoring indicator is smaller than the threshold value corresponding to the indicator, and so on. The threshold corresponding to the index may be set according to an empirical value, which is not limited in this embodiment of the present application.

In some implementations, when the elastic scaling controller 102 adjusts the application instance, it can be based on the resource models of the multiple cloud platforms and the affinity policy, anti-affinity policy, cost priority policy, and performance priority policy. any one or more of the strategies to adjust the application instance.

Wherein, when the application has higher requirements on the response rate, the elastic scaling controller 102 may adjust the application instance according to the resource models and affinity policies of the multiple cloud platforms. For example, the elastic scaling controller 102 may deploy application instances that require a higher response rate in a close region of the same cloud platform, for example, on a close node in the same region.

When the application has high requirements on reliability and availability, the elastic scaling controller 102 may adjust the application instance according to the resource models of the multiple cloud platforms 104 and the anti-affinity policy. For example, the elastic scaling controller 102 can avoid deploying application instances that require higher reliability and availability in a close region of the same cloud platform, that is, the elastic scaling controller 102 can deploy them on different cloud platforms 104 .

When the user prioritizes cost, when adjusting the application instance, the elastic scaling controller 102 may adjust the application instance according to the resource models of the multiple cloud platforms 104 and the cost priority policy. For example, the elastic scaling controller 102 can select the cloud platform 104 with the lowest price to deploy the application instance.

When the user prioritizes performance, when adjusting the application instance, the elastic scaling controller 102 can adjust the application instance according to the resource models of the multiple cloud platforms 104 and the performance priority policy. For example, the elastic scaling controller 102 can select the cloud platform 104 with the best performance to deploy the application instance.

In some implementations, when adjusting the application instances, the elastic scaling controller 102 may adjust the number of the application instances according to the resource models of the multiple cloud platforms and the elastic scaling policy. For example, the elastic scaling controller 102 may add new application instances according to resource models of multiple cloud platforms and elastic scaling policies. For another example, the elastic scaling controller 102 may delete application instances according to resource models of multiple cloud platforms and elastic scaling policies.

In other implementations, when adjusting the application instance, the elastic scaling controller 102 may adjust the configuration of the application instance according to the resource models of the multiple cloud platforms and the elastic scaling policy. For example, the elastic scaling controller 102 may upgrade the configuration of the application instance from 1 core 1G (indicating that the processor is a single core and the memory is 1G) to 4 cores and 2G (indicating that the processor is 4 cores and the memory is 2G).

The following describes the adjustment process in detail by taking the adjustment of the number of application instances as an example.

Specifically, the elastic scaling controller 102 may determine the target cloud platform according to the resource models of the multiple cloud platforms and the elastic scaling policy, and then create a new application instance through the API of the target cloud platform. In this example, it is assumed that the elastic scaling policy is a cost-first policy. When the monitoring index value is greater than the corresponding threshold, the elastic scaling controller 102 needs to add an application instance.

To this end, the elastic scaling controller 102 can determine how many resources of the cloud platform of the measurement unit correspond to the standard resources of one measurement unit according to the corresponding relationship between the resource parameter formats supported by each cloud platform and the standard resource parameter formats in the resource model, and then Based on the costs of various resources and specifications, determine the cloud platform with the lowest total cost of deploying application instances as the target cloud platform. The elastic scaling controller 102 calls the API to create a new application instance on the target cloud platform, so as to ensure service quality through more application instances.

Further, after adding an application instance, when the monitoring index values of the application instances deployed on the multiple cloud platforms do not meet the preset conditions, the elastic scaling controller 102 may also delete the new application through the API instance to avoid wasting resources.

It should be noted that, when adding an application instance or deleting an application instance, the elastic scaling controller 102 can also select an appropriate cloud platform, that is, a target cloud platform, and create or delete an application instance through the hybrid cloud API gateway.

The elastic scaling method in a multi-cloud environment provided by the embodiments of the present application is described in detail above with reference to FIGS. 1 to 5 , and the apparatus and equipment provided by the embodiments of the present application will be introduced below with reference to the accompanying drawings.

Referring to the schematic structural diagram of an elastic scaling device in a cloudy environment shown in FIG. 6 , the device 600 includes:

A modeling unit 602, configured to model resources provided by multiple cloud platforms, and obtain resource models of the multiple cloud platforms;

The adjustment unit 604 is configured to adjust the application instances according to the resource models and elastic scaling policies of the multiple cloud platforms when the monitoring index values of the application instances deployed on the multiple cloud platforms meet the preset conditions.

In some possible implementations, the adjusting unit 604 is specifically used for:

The application instance is adjusted according to the resource models of the multiple cloud platforms and any one or more strategies of an affinity strategy, an anti-affinity strategy, a cost priority strategy, and a performance priority strategy.

In some possible implementations, the adjusting unit 604 is specifically used for:

Adjust the number of the application instances according to the resource models and elastic scaling policies of the multiple cloud platforms; or adjust the configuration of the application instances according to the resource models and elastic scaling policies of the multiple cloud platforms.

In some possible implementations, the adjusting unit 604 is specifically used for:

Determine the target cloud platform according to the resource models of the multiple cloud platforms and the elastic scaling strategy;

A new application instance is created through the application programming interface (API) of the target cloud platform.

In some possible implementations, the adjustment unit 604 is further configured to:

After a new application instance is created, when the monitoring index values of the application instances deployed on the multiple cloud platforms do not meet the preset conditions, the new application instance is deleted through the API.

In some possible implementations, the multiple cloud platforms include multiple public cloud platforms, or multiple private cloud platforms, or a hybrid cloud platform formed by at least one public cloud platform and at least one private cloud platform.

The elastic scaling apparatus 600 in the multi-cloud environment according to the embodiments of the present application may correspond to the above-mentioned and other operations and/or functions of each module/unit of the elastic scaling apparatus 600 in the multi-cloud environment by executing the methods described in the embodiments of the present application and the elastic scaling apparatus 600 in the multi-cloud environment In order to implement the corresponding processes of each method in the embodiment shown in FIG. 5 , for the sake of brevity, details are not repeated here.

This embodiment of the present application further provides a device 700 . The device 700 may be an end-side device such as a notebook computer and a desktop computer, or may be a computer cluster in a cloud environment or an edge environment. The elastic scaling controller 102 is deployed in the device 700, and the device 700 is specifically configured to implement the function of the elastic scaling device 600 in the multi-cloud environment in the embodiment shown in FIG. 6 .

FIG. 7 provides a schematic structural diagram of a device 700 . As shown in FIG. 7 , the device 700 includes a bus 701 , a processor 702 , a communication interface 703 and a memory 704 . The processor 702 , the memory 704 and the communication interface 703 communicate through the bus 701 .

The bus 701 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus or the like. The bus can be divided into address bus, data bus, control bus and so on. For ease of presentation, only one thick line is used in FIG. 7, but it does not mean that there is only one bus or one type of bus.

The processor 702 may be a central processing unit (central processing unit, CPU). Any one or more of processors such as a graphics processor (graphics processing unit, GPU), a microprocessor (micro processor, MP), or a digital signal processor (digital signal processor, DSP).

The communication interface 703 is used for external communication. For example, the cost of various types of resources on multiple cloud platforms 104 with various specifications is obtained, or the performance of various types of resources on multiple cloud platforms 104 with various specifications is acquired.

Memory 704 may include volatile memory, such as random access memory (RAM). The memory 704 may also include non-volatile memory (non-volatile memory), such as read-only memory (ROM), flash memory, hard drive drive (HDD) or solid state drive (SSD) ).

Executable code is stored in the memory 704, and the processor 702 executes the executable code to execute the aforementioned elastic scaling method in a multi-cloud environment.

Specifically, in the case where the embodiment shown in FIG. 6 is implemented, and each unit of the elastic scaling device 600 in the multi-cloud environment described in the embodiment in FIG. 6 is implemented by software, the modeling in FIG. 6 is performed. The software or program codes required for the functions of the unit 602 and the elastic scaling control unit 604 are stored in the memory 704 . The communication module function is realized through the communication interface 703 .

The communication interface 703 receives the resource parameter formats of the multiple cloud platforms 104, and transmits them to the processor 702 through the bus 701. The processor 702 executes the program codes corresponding to each unit stored in the memory 704, such as the modeling unit 602, the elastic scaling control The program code corresponding to the unit 604 is used to model the resources provided by the multiple cloud platforms according to the resource parameter format and the standard resource parameter format of the multiple cloud platforms 104, and obtain the resource models of the multiple cloud platforms. When the monitoring index values of the application instances of the multiple cloud platforms meet the preset conditions, the step of adjusting the application instances according to the resource models of the multiple cloud platforms and the elastic scaling policy.

In some implementations, the processor 702 is specifically configured to execute program codes corresponding to the elastic scaling control unit 604 to perform the following method steps:

The application instance is adjusted according to the resource models of the multiple cloud platforms and any one or more strategies of an affinity strategy, an anti-affinity strategy, a cost priority strategy, and a performance priority strategy.

In some implementations, the processor 702 is specifically configured to execute program codes corresponding to the elastic scaling control unit 604 to perform the following method steps:

Adjust the number of the application instances according to the resource models and elastic scaling policies of the multiple cloud platforms; or,

The configuration of the application instance is adjusted according to the resource models of the multiple cloud platforms and the elastic scaling policy.

In some implementations, the processor 702 is specifically configured to execute program codes corresponding to the elastic scaling control unit 604 to perform the following method steps:

Determine the target cloud platform according to the resource models of the multiple cloud platforms and the elastic scaling strategy;

A new application instance is created through the application programming interface (API) of the target cloud platform.

In some implementations, the processor 702 is specifically configured to execute program codes corresponding to the elastic scaling control unit 604 to perform the following method steps:

After a new application instance is created, when the monitoring index values of the application instances deployed on the multiple cloud platforms do not meet the preset conditions, the new application instance is deleted through the API.

Embodiments of the present application further provide a computer-readable storage medium, where the computer-readable storage medium includes instructions, the instructions instruct a computer to execute the above-mentioned elastic scaling method in a multi-cloud environment that is applied to the elastic scaling device 600 in a multi-cloud environment.

Embodiments of the present application further provide a computer-readable storage medium, where the computer-readable storage medium includes instructions, the instructions instruct a computer to execute the above-mentioned elastic scaling method in a multi-cloud environment that is applied to the elastic scaling device 600 in a multi-cloud environment.

An embodiment of the present application further provides a computer program product, when the computer program product is executed by a computer, the computer executes any one of the foregoing elastic scaling methods in a multi-cloud environment. The computer program product may be a software installation package, and when any one of the aforementioned elastic scaling methods in a multi-cloud environment needs to be used, the computer program product may be downloaded and executed on a computer.

In addition, it should be noted that the device embodiments described above are only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be A physical unit, which can be located in one place or distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. In addition, in the drawings of the device embodiments provided in the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general-purpose hardware. Special components, etc. to achieve. Under normal circumstances, all functions completed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structures used to implement the same function can also be various, such as analog circuits, digital circuits or special circuit, etc. However, software program implementation is the preferred embodiment in many cases for this application. Based on this understanding, the technical solutions of the present application can be embodied in the form of software products in essence, or the parts that make contributions to the prior art. The computer software products are stored in a readable storage medium, such as a floppy disk of a computer. , U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., including several instructions to enable a computer device (which may be a personal computer, training device, or network device, etc.) to execute the various embodiments of the application. method.

In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented in software, it can be implemented in whole or in part in the form of a computer program product.

The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be retrieved from a website, computer, training device, or data Transmission from the center to another website site, computer, training facility or data center via wired (eg coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (eg infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that can be stored by a computer, or a data storage device such as a training device, a data center, or the like that includes an integration of one or more available media. The usable media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVD), or semiconductor media (eg, Solid State Disk (SSD)), and the like.

Claims (14)

1. An elastic expansion method in a cloudy environment, the method comprising:

modeling resources provided by a plurality of cloud platforms to obtain resource models of the cloud platforms;

and when the monitoring index values of the application examples deployed on the cloud platforms meet preset conditions, adjusting the application examples according to the resource models and the elastic scaling strategies of the cloud platforms.

2. The method of claim 1, wherein the adjusting the application instance according to the resource model of the plurality of cloud platforms and the elastic scaling policy comprises:

and adjusting the application examples according to the resource models of the cloud platforms and any one or more of an affinity strategy, an anti-affinity strategy, a cost priority strategy and a performance priority strategy.

3. The method of claim 1 or 2, wherein the adapting the application instance according to the resource model of the plurality of cloud platforms and the elastic scaling policy comprises:

adjusting the number of the application instances according to the resource models of the cloud platforms and an elastic expansion strategy; or,

and adjusting the configuration of the application instance according to the resource models of the cloud platforms and the elastic scaling strategy.

4. The method of any of claims 1 to 3, wherein said adapting the application instance according to the resource model of the plurality of cloud platforms and the elastic scaling policy comprises:

determining a target cloud platform according to the resource models of the cloud platforms and an elastic expansion strategy;

creating a new application instance through an Application Programming Interface (API) of the target cloud platform.

5. The method of claim 4, wherein after creating the new application instance, the method further comprises:

and when the monitoring index values of the application instances deployed on the plurality of cloud platforms do not meet the preset conditions, deleting the new application instances through the API.

6. The method of any one of claims 1 to 5, wherein the plurality of cloud platforms comprises a plurality of public cloud platforms, or a plurality of private cloud platforms, or a hybrid cloud platform formed by at least one public cloud platform and at least one private cloud platform.

7. An elastic telescopic device in a cloudy environment, the device comprising:

the modeling unit is used for modeling resources provided by a plurality of cloud platforms to obtain resource models of the cloud platforms;

and the adjusting unit is used for adjusting the application examples according to the resource models and the elastic scaling strategies of the cloud platforms when the monitoring index values of the application examples deployed on the cloud platforms meet preset conditions.

8. The apparatus according to claim 7, wherein the adjusting unit is specifically configured to:

and adjusting the application examples according to the resource models of the cloud platforms and any one or more of an affinity strategy, an anti-affinity strategy, a cost priority strategy and a performance priority strategy.

9. The apparatus according to claim 7 or 8, wherein the adjusting unit is specifically configured to:

adjusting the number of the application instances according to the resource models of the cloud platforms and an elastic expansion strategy; or,

and adjusting the configuration of the application instance according to the resource models of the cloud platforms and the elastic scaling strategy.

10. The apparatus according to any one of claims 7 to 9, wherein the adjusting unit is specifically configured to:

determining a target cloud platform according to the resource models of the cloud platforms and an elastic expansion strategy;

creating a new application instance through an Application Programming Interface (API) of the target cloud platform.

11. The apparatus of claim 10, wherein the adjustment unit is further configured to:

after a new application instance is created, when the monitoring index values of the application instances deployed on the plurality of cloud platforms do not meet preset conditions, deleting the new application instance through the API.

12. The apparatus of any of claims 7 to 11, wherein the plurality of cloud platforms comprises a plurality of public cloud platforms, or a plurality of private cloud platforms, or a hybrid cloud platform formed by at least one public cloud platform and at least one private cloud platform.

13. An apparatus, comprising a processor and a memory;

the processor is to execute instructions stored in the memory to cause the device to perform the method of any of claims 1 to 6.

14. A computer-readable storage medium comprising instructions that direct a device to perform the method of any of claims 1-6.

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