WO2018086467A1

WO2018086467A1 - Method, apparatus and system for allocating resources of application clusters under cloud environment

Info

Publication number: WO2018086467A1
Application number: PCT/CN2017/108252
Authority: WO
Inventors: 刘岳嘉
Original assignee: 阿里巴巴集团控股有限公司
Priority date: 2016-11-08
Filing date: 2017-10-30
Publication date: 2018-05-17
Also published as: CN108063784A; TW201818244A; CN108063784B; TWI755415B

Abstract

Embodiments of the present application provide a method, apparatus and system for allocating resources of application clusters under a cloud environment. The method comprises: obtaining resource utilization rates of all application clusters in a specified transaction link under a cloud environment; determining resource load levels of all the application clusters according to the resource utilization rates, the resource load levels comprising a high occupancy level and a low occupancy level; applying for resources for an application cluster at the high occupancy level; releasing resources of an application cluster at the low occupancy level according to the resource load levels if the applying for resources for an application cluster at the high occupancy level fails; and allocating the released resources to the application cluster at the high occupancy level. The embodiments of the present application can level resource loads of the application clusters under the entire cloud environment, and can make full use of the existing resources to improve the resource utilization efficiency of the application clusters, so that the application clusters can support higher access traffic.

Description

Method, device and system for applying cluster resource allocation in cloud environment

This application claims the priority of the Chinese Patent Application No. 201610982629.X filed on November 8, 2016, entitled "Method, Apparatus and System for Application Cluster Resource Allocation in a Cloud Environment", the entire contents of which are The citations are incorporated herein by reference.

Technical field

The present invention relates to the field of data processing technologies, and in particular, to a method for applying cluster resource allocation in a cloud environment, a device for applying cluster resource allocation in a cloud environment, and a system for applying cluster resource allocation in a cloud environment.

Background technique

Cloud computing is a computing model that provides dynamically scalable virtual resources over the Internet in a service manner. In this way, shared hardware and software resources and information can be provided to computers and other devices on demand. The basic environment of cloud computing is virtualization, and resource sharing exists in the virtualized environment. In a cloud computing environment (hereinafter referred to as a cloud environment), virtual machines (VMs, Virtual Machines) are often used, and system resources of these virtual machines are mainly associated.

These resources in the cloud environment are shared by virtual machines. When the virtual machine needs to apply for resources, it will apply for idle resources from the cloud environment. However, in the case of limited resources, if the idle resources are exhausted, the virtual machine fails to apply for resources, and the virtual machine will not work properly due to excessive resource load, which greatly affects work efficiency.

Summary of the invention

In view of the above problems, embodiments of the present application are provided to provide a method for applying cluster resource allocation in a cloud environment, which overcomes the above problems or at least partially solves the above problems, and a device for applying cluster resource allocation in a cloud environment, and A system for applying cluster resource allocation in a cloud environment.

In order to solve the above problem, the embodiment of the present application discloses a system for applying cluster resource allocation in a cloud environment, where the system includes:

One or more processors;

Memory; and

One or more modules, the one or more modules being stored in the memory and configured to be executed by the one or more processors, the one or more modules having the following functions:

Obtain resource usage rates of application clusters in a specified transaction link in a cloud environment;

Determining a resource load level of each application cluster according to the resource usage rate; the resource load level includes a high Occupancy level and low occupancy level;

Applying for the application cluster of the high occupancy level;

If the application for the high-occupancy application cluster fails, the resource of the application cluster with a low occupation level is released according to the resource load level;

Allocating the released resources to the application cluster of the high occupancy level.

The embodiment of the present application further discloses a method for applying cluster resource allocation in a cloud environment, including:

Determining, according to the resource usage rate, a resource load level of each application cluster; the resource load level includes a high occupancy level and a low occupancy level;

Applying for the application cluster of the high occupancy level;

Preferably, the step of acquiring the resource usage rate of each application cluster in the specified transaction link in the cloud environment includes:

When the access traffic is increased, the resource usage rate of each application cluster in the specified transaction link in the cloud environment is obtained.

Preferably, the cloud environment includes a resource pool, and the step of applying for resources for the application cluster with a high occupancy level includes:

Applying for resources to the resource pool;

Allocating resources in the resource pool to the application cluster of the high occupancy level when the resources of the resource pool are sufficient;

When the resources of the resource pool are exhausted, the application for the high occupancy level application cluster fails.

Preferably, the application cluster has a corresponding priority, and the step of allocating resources in the resource pool to an application cluster with a high occupancy level includes:

When the application cluster of the high-occupancy level is two or more, the resources in the resource pool are allocated according to the application cluster whose priority is a high occupation level.

Preferably, the resource load level has a corresponding release ratio, and the step of releasing resources of the application cluster of the low occupancy level according to the resource load level includes:

Determining the release ratio corresponding to the resource load level of the application cluster with a low occupancy level;

Release the resources of the low occupancy level application cluster according to the release ratio;

Save the released resource to the resource pool.

Preferably, the step of releasing resources of the low occupancy level application cluster according to a release ratio comprises:

Obtaining a current resource of the application cluster of the low occupancy level;

Obtaining current access traffic of the application cluster of the low occupancy level;

Calculating the demand resource by using the current access traffic and the preset stand-alone limit value;

Determining, by the current resource and the required resource, an idle resource of the application cluster of the low occupancy level;

The idle resource is released according to the release ratio.

Preferably, the step of allocating the released resources to the application cluster of the high occupancy level comprises:

Requesting a resource from the resource pool in which the released resource is saved;

Allocating resources released in the resource pool for the high occupancy level application cluster.

The embodiment of the present application further discloses an apparatus for applying cluster resource allocation in a cloud environment, including:

a resource usage acquisition module, configured to acquire resource usage rates of application clusters in a specified transaction link in a cloud environment;

a resource load level determining module, configured to determine a resource load level of each application cluster according to the resource usage rate; the resource load level includes a high occupancy level and a low occupancy level;

a resource application obtaining module, configured to apply for resources for the application cluster of the high occupancy level;

a resource release module, configured to release resources of an application cluster of a low occupancy level according to a resource load level if the resource application fails for the application cluster of the high occupancy level;

And a resource allocation module, configured to allocate the released resource to the application cluster of the high occupancy level.

Obtain the resource load status of each application cluster in the cloud environment;

Determining, according to the resource load status, a resource load level of the application cluster; the resource load level includes a resource full load level and other levels;

Applying for the resource cluster of the resource full load level;

If the application cluster of the resource full-load level fails to apply for resources, the resources of other levels of the application cluster are released according to the resource load level;

The released resources are allocated to an application cluster of the resource full load level.

Preferably, the step of acquiring the resource load status of each application cluster in the cloud environment includes:

When the access traffic is increased, the resource load status of each application cluster in the cloud environment is obtained.

Preferably, the cloud environment includes a resource pool, and the step of applying for resources for the application cluster of the resource full load level includes:

Applying for resources to the resource pool;

When the resources of the resource pool are sufficient, the resources in the resource pool are allocated to the application cluster of the resource full load level;

When the resources of the resource pool are exhausted, the application cluster for the resource full load level fails to apply for resources.

Preferably, the application cluster has a corresponding priority, and the step of allocating resources in the resource pool to an application cluster of a resource full load level includes:

When the application cluster of the resource full load level is two or more, the resource in the resource pool is allocated according to the application cluster whose priority is the resource full load level.

Preferably, the resource load level has a corresponding release ratio, and the step of releasing resources of other levels of the application cluster according to the resource load level includes:

Determine the release ratio corresponding to the resource load level of the application cluster of other levels;

Release the resources of the other level of the application cluster according to the release ratio;

Save the released resource to the resource pool.

Preferably, the step of releasing resources of the other application cluster according to a release ratio comprises:

Obtaining current resources of the application cluster of the other level;

Obtaining current access traffic of the application cluster of the other level;

Determining, by the current resource and the required resource, an idle resource of the application cluster of the other level;

The idle resource is released according to the release ratio.

Preferably, the step of allocating the released resource to the application cluster of the resource full load level comprises:

Allocating resources released in the resource pool for the application cluster of the resource full load level.

a resource load status obtaining module, configured to acquire a resource load status of each application cluster in a cloud environment;

a resource load level determining module, configured to determine a resource load level of the application cluster according to the resource load status; the resource load level includes a resource full load level and other levels;

a resource application module, configured to apply for resources for an application cluster of the resource full load level;

a resource release module, configured to release resources of other levels of the application cluster according to a resource load level when the application cluster for the resource full load level fails to apply for a resource;

And releasing a resource allocation module, configured to allocate the released resource to an application cluster of the resource full load level.

Embodiments of the present application include the following advantages:

In the embodiment of the present application, the cluster is applied in a cloud environment, and the resource load level of the application cluster is determined according to the load state of each application cluster. If the application cluster with the full load level exists, the resource is applied for the application cluster. If it fails, it requests other levels of application clusters to release resources, and finally allocates the released resources to the application cluster at the resource full level. It should be noted that, in the embodiment of the present application, resources are released according to the resource full load level of the application cluster. Therefore, if the above steps are repeatedly performed, the resource load of the application cluster in the entire cloud environment is finally flattened, and the existing resources can be fully utilized to improve. Application cluster resource utilization efficiency enables application clusters to support higher access traffic.

DRAWINGS

1 is a flow chart of the steps of Embodiment 1 of a method for applying cluster resource allocation in a cloud environment according to the present application;

2 is a schematic diagram of resource allocation of an application cluster in a cloud environment according to the present application;

3 is a flow chart of steps in Embodiment 2 of a method for applying cluster resource allocation in a cloud environment according to the present application;

4 is a structural block diagram of an apparatus for applying cluster resource allocation in a cloud environment according to the present application;

FIG. 5 is a schematic structural diagram of a server embodiment of the present application.

detailed description

The above described objects, features and advantages of the present application will become more apparent and understood.

In the cloud environment, one or more application clusters can be used to complete specific services independently, and the application clusters can form a transaction link to complete a complete transaction. For example, in an application cluster of a supply link, including four application systems of commodity, logistics, certification, and marketing, a series of tasks such as commodity, logistics, certification, and marketing are completed in the application cluster of the supply chain. Transaction link pressure measurement can be used to test the processing efficiency of the transaction link of the application cluster.

The transaction link pressure test simulates various performance indicators of the application cluster through automatic test tools to simulate various normal, peak and abnormal load conditions, detects the performance bottleneck of the application cluster, and improves the processing capability of the application cluster.

However, the current transaction link pressure test only tests the performance bottleneck of the application cluster, and when the access traffic reaches the target value, for example, when the access traffic reaches 15 W, the processing capability of the application cluster is no longer detected, and the resources cannot be fully utilized. .

For the above problem, the embodiment of the present application is applied to a cluster in a cloud environment, and is in the process of pressure measurement on a specified platform. Leveling the resource load of the application cluster as a whole, and making full use of existing resources to balance the resource load of each application cluster and improve the processing capacity of the application cluster, so that the application cluster can support higher access traffic. .

Referring to FIG. 1 , a flow chart of steps of a method for applying cluster resource allocation in a cloud environment according to the present application is shown, which may specifically include the following steps:

Step 101: Obtain a resource load status of each application cluster in a cloud environment.

In the embodiment of the present application, an application cluster of a transaction link is first combed, and the application cluster is connected to a specified platform to form a cloud environment. In this cloud environment, it is possible to plan the application cluster.

As shown in FIG. 2, an application cluster includes an application A cluster, an application B cluster, and an application C cluster. The application clusters are connected to a specified platform, and the application clusters on the platform can share resources.

The resource load status of the application cluster refers to the resource usage rate of the application cluster. Specifically, the resources may include capacity, CPU, memory, disk I/O, network bandwidth, and the like. In some cases, the resource usage of the application cluster can reach 100%.

In an embodiment of the present application, the step 101 may include the following sub-steps:

Sub-step S11, when the access traffic is increased, the resource load status of each application cluster in the cloud environment is obtained.

The embodiment of the present application detects the performance bottleneck of the current application cluster by performing a pressure test on the transaction link. The performance bottleneck, that is, the resource usage rate reaches a dangerous threshold, the application cluster will not work properly.

During the process of compressing the transaction link, the user's access traffic to the application cluster can be continuously improved. In one case, when detecting the increase of the access traffic, the embodiment of the present application acquires the resource load status of each application cluster in the cloud environment.

Step 102: Determine, according to the resource load status, a resource load level of the application cluster; the resource load level includes a resource full load level and other levels;

In the embodiment of the present application, for each application cluster, the resource load level of the application cluster is determined according to its resource load status, and the resource load level can be used to represent the resource usage rate of the application cluster.

The resource load level can include the resource full load level and other levels. The resource full load level means that the resource usage of the application cluster has reached a dangerous threshold. For example, the CPU usage may reach 90%.

As shown in Figure 2, if the application cluster A has a CPU usage greater than 70% (CPU>70%), or if the relationship between the average load and the number of CPU cores in 1 minute is load1>core_num*1.5, then the application can be considered as an application. The A cluster is at the resource full level, and the application B cluster and the application C cluster are at other levels.

It should be noted that, in the embodiment of the present application, the division of the number of levels of the resource load level may be specifically The requirement division is not limited by the embodiment of the present application. In addition, the method for dividing the resource load level in the embodiment of the present application is not limited to the CPU usage rate, and may be divided into other resource parameters, such as memory, disk I/O, network bandwidth, etc., or according to other methods. The division may be any, and the embodiment of the present application also does not limit this.

Step 103: Apply for resources for the application cluster of the resource full load level;

A resource pool may be included in the cloud environment, and idle resources are placed in the resource pool. When the application cluster needs to apply for resources, the resource pool is first requested.

In an embodiment of the present application, the step 103 may include the following sub-steps:

Sub-step S21, applying for resources to the resource pool;

Sub-step S22, when the resources of the resource pool are sufficient, allocate resources in the resource pool to the application cluster of the resource full-load level;

In sub-step S23, when the resources of the resource pool are exhausted, the application cluster for the resource full-load level fails to apply for resources.

It can be understood that if the resources in the resource pool are sufficient, the resources in the resource pool can be directly allocated to the application cluster at the resource full level. However, if the resources in the resource pool are exhausted, the application clusters at the resource full level cannot be applied for resources. At this time, the application for the resource failed.

In an embodiment of the present application, the application cluster may have a corresponding priority, and the sub-step S22 may include the following sub-steps:

Sub-step S221: When the application clusters of the resource full load level are two or more, the resources in the resource pool are allocated according to the application cluster whose priority is the resource full load level.

When there are allocable resources in the resource pool, the resources can be prioritized to the high priority application cluster. Specifically, if multiple application clusters have performance bottlenecks at the same time, that is, application clusters with multiple resource full-load levels appear at the same time, resources are allocated in order for each application cluster according to priority, and high-priority application clusters preferentially apply for resources. Then, apply for resources for the low-priority application cluster. Through such an allocation principle, the host resources of the application cluster can be fully utilized.

Conversely, if the resources are not allocated in order for each application cluster according to the priority, the resources in the resource pool will be exhausted after applying for resources for the low-priority application cluster, and finally the high-priority application cluster will not be able to be clustered. resource allocation.

It should be noted that, in the embodiment of the present application, the priority of the application cluster may be set by the relevant personnel. Of course, the priority of the application cluster may also be set according to other aspects such as the importance degree or processing sequence of the application cluster. The embodiment of the present invention does not limit this.

Step 104: If the application cluster of the resource full load level fails to apply for the resource, the resources of the application cluster of other levels are released according to the resource load level.

When the application for resources from the resource pool fails, the resources of other application clusters are released to make up for the shortage of current resources.

In an embodiment of the present application, the resource load level may have a corresponding release ratio, and the step 104 may include the following sub-steps:

Sub-step S31, determining a release ratio corresponding to a resource load level of the application cluster of another level;

Sub-step S32, releasing resources of the application cluster of the other level according to the release ratio;

Sub-step S33, saving the released resource to the resource pool.

In the embodiment of the present application, when the resource of the application cluster is released, the resource is released according to the resource load level of the application cluster, that is, if the application cluster is loaded with a large load, less resources are released, and if the load is small, Applying a cluster releases more resources.

In an embodiment of the present application, the sub-step S32, that is, the releasing the resources of the other application cluster according to a release ratio, includes:

Sub-step S321, acquiring current resources of the application cluster of the other level;

Sub-step S322, obtaining current access traffic of the application cluster of the other level;

Sub-step S323, calculating the demand resource by using the current access traffic and the preset single-machine limit value;

Sub-step S324, determining, by using the current resource and the required resource, an idle resource of the application cluster of the other level;

Sub-step S325, releasing the idle resource according to the release ratio.

In the embodiment of the present application, the resources required for the current application cluster may be: the total traffic per second of the current application cluster/the absolute value of the daily pressure measurement, wherein the absolute value of the daily pressure measurement refers to a single virtual machine in the application cluster. The traffic that can withstand. According to the current resource and the required resource of the application cluster, the idle resources that the application cluster can release can be determined.

In another embodiment, the resources required for the current application cluster may also be: total access per second of the current application cluster / daily pressure measurement stand-alone extreme value / safe water level. The security water level is a buffer (buffer), and may be set according to actual requirements or not used. This embodiment of the present invention does not limit this.

As shown in Figure 2, the application cluster B has a higher resource load level and a higher resource usage rate. If the CPU usage is greater than 50% and less than 60%, the application cluster B only needs to release 5% of resources and apply the resources of the C cluster. Load level Low, resource usage is low, CPU usage is greater than 10%, less than 20%, then the application cluster needs to release 15% of resources. The released resources can be saved to the T4 resource pool. Through the resource gradient release, the resources of each application cluster can be kept at a certain level instead of across the board.

Step 105: Allocate the released resource to an application cluster of the resource full load level.

In an embodiment of the present application, the step 105 may include the following sub-steps:

Sub-step S41, applying for a resource to the resource pool in which the released resource is saved;

Sub-step S42, allocating resources released in the resource pool for the application cluster of the resource full load level.

After the released resources are saved in the resource pool, the application cluster at the resource full level can continuously apply for resources from the resource pool until the user needs to meet the requirements.

Specifically, the resource requirement of the application cluster at the resource full load level may be that the number of predicted instances (virtual machine resources) is determined according to the access traffic, and then the number of predicted instances is subtracted from the current number of instances (current virtual machine resources), that is, The resource requirements for the application cluster.

Of course, the resource requirement of the application cluster at the resource full load level can be to reduce the application cluster from the resource full load level to other levels.

After the resources of the application cluster of the resource full load level are allocated, the user access traffic can be continuously increased, and steps 101 to 105 are performed again. If it is repeated, the resource load of the application cluster of the entire transaction link is finally flattened. The state makes the resources of each application cluster fully utilized.

Referring to FIG. 3, a flow chart of the steps of the method for applying the cluster resource allocation in the cloud environment of the present application is shown in the following steps.

Step 201: Obtain a resource usage rate of each application cluster in a specified transaction link in a cloud environment.

Step 202: Determine, according to the resource usage rate, a resource load level of each application cluster; the resource load level includes a high occupancy level and a low occupation level;

Step 203: Apply for resources for the application cluster of the high occupancy level.

Step 204: If the application for the high-occupancy application cluster fails, the resources of the application cluster with a low occupation level are released according to the resource load level.

Step 205: Allocate the released resource to the application cluster of the high occupancy level.

In a preferred embodiment of the present application, the step 201 may include:

In a preferred embodiment of the present application, the cloud environment includes a resource pool, and the step 203 may include:

Applying for resources to the resource pool;

In a preferred embodiment of the present application, the application cluster has a corresponding priority, and the step of allocating resources in the resource pool to an application cluster with a high occupancy level includes:

In a preferred embodiment of the present application, the resource load level has a corresponding release ratio, and the step 204 may include:

Save the released resource to the resource pool.

In a preferred embodiment of the present application, the step of releasing resources of the low occupancy level application cluster according to a release ratio includes:

The idle resource is released according to the release ratio.

In a preferred embodiment of the present application, the step 205 includes:

In a specific implementation, a plurality of transaction links can be divided in a cloud environment to deal with various transactions in a targeted manner. The embodiment of the present invention can be applied in a cloud environment to perform overall planning for resources of an application cluster.

In the embodiment of the present application, for applying a cluster in a cloud environment, the resource load status of the application cluster is known according to the resource usage rate of each application cluster, so that the resource load level at which the resource cluster is located can be determined. If the application cluster of the high-occupancy level exists, the resource is applied to the application cluster. If the application fails, the application cluster of the low-occupancy level is requested to release the resources. Finally, the released resources are allocated to the application cluster of the high-occupancy level. It should be noted that, in the embodiment of the present application, resources are released according to the resource full load level of the application cluster. Therefore, if the above steps are repeatedly performed, the resource load of the application cluster in the entire cloud environment is finally flattened, and the existing resources can be fully utilized to improve. Application cluster resource utilization efficiency enables application clusters to support higher access traffic.

In order to enable a person skilled in the art to better understand the embodiments of the present application, the pressure measurement process of the transaction link in the embodiment of the present application is illustrated by a specific example, as shown in FIG. 2 .

1. Combine the application clusters of the entire transaction link and access the platform; access the application A cluster, the application B cluster, and the application C cluster under the platform, and these application clusters form a transaction link.

2. Perform a pressure test on the transaction link to detect the performance bottleneck of the application cluster under the transaction link. The resource load level is divided according to the resource load status for each application cluster, and the application cluster with performance bottleneck is divided into resource full load levels.

3. When a performance bottleneck occurs in an application cluster, for example, if the application cluster A is applied, the resource is applied to the resource pool. If the resource is successfully applied from the resource pool, the pressure is continuously increased, that is, the application of the cluster resource user under the transaction link is improved. Views.

There is a principle when applying for a machine (resource): if multiple application clusters have performance bottlenecks at the same time, resources are allocated according to the priority of each application cluster. First, resources are applied for the application cluster with higher priority, followed by low priority. Application clusters apply for resources to achieve full utilization of host resources; if this principle is not followed, it may cause low-priority application clusters to allocate resources, resource pool resources are exhausted, and finally can no longer be high priority. Level application clusters allocate resources.

4. As the application cluster of the transaction link continues to be under high pressure, and when the pressure is raised, the resources are applied in real time to support the current access traffic of the application cluster. Until a performance bottleneck occurs in an application cluster, and the application for resources fails (the resource pool is exhausted). The platform automatically traverses the resource load status of the application clusters on other links. Each application cluster releases resources according to its own resource load state to level the resource load water level.

5. After the water level is leveled, the resources are released and saved to the resource pool. The application cluster A that has performance bottleneck continuously requests resources from the resource pool until it receives its own resource requirements, and stops other low-load application clusters from releasing idle resources. At this point, continue to touch the high pressure, that is, increase the user's access traffic, and iterate again from step 1.

6, through the above five steps of iteration, and finally to reach the existing resources of the application cluster that fully utilizes the transaction link, so that the transaction link can support the access traffic of a larger user.

It should be noted that, for the method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should understand that the embodiments of the present application are not limited by the described action sequence, because In accordance with embodiments of the present application, certain steps may be performed in other sequences or concurrently. In the following, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required in the embodiments of the present application.

Referring to FIG. 4, a structural block diagram of an apparatus embodiment for applying cluster resource allocation in a cloud environment according to the present application is shown, which may specifically include the following modules:

The resource load status obtaining module 301 is configured to acquire a resource load status of each application cluster in the cloud environment;

In an embodiment of the present application, the resource load state obtaining module 301 may include the following submodules:

The resource load status acquisition sub-module is configured to obtain the resource load status of each application cluster in the cloud environment when the access traffic is increased.

The resource load level determining module 302 is configured to determine a resource load level of the application cluster according to the resource load status; the resource load level includes a resource full load level and other levels;

The resource application module 303 is configured to apply for resources for the application cluster of the resource full load level;

In an embodiment of the present application, the cloud environment may include a resource pool, and the resource application module 303 may include the following sub-modules:

a first resource pool resource application submodule, configured to apply for a resource to the resource pool;

a first resource processing sub-module, configured to: when the resources of the resource pool are sufficient, allocate resources in the resource pool to an application cluster of the resource full-load level;

The second resource processing sub-module is configured to: when the resource of the resource pool is exhausted, requesting the resource for the application cluster of the resource full-load level fails.

In an embodiment of the present application, the application cluster may have a corresponding priority, and the first resource processing submodule includes:

The priority processing unit is configured to allocate resources in the resource pool according to the application cluster whose priority is the resource full load level when the application cluster of the resource full load level is two or more.

The resource release module 304 is configured to release resources of other levels of the application cluster according to the resource load level when the application cluster for the resource full load level fails to apply for the resource;

In an embodiment of the present application, the resource load level may have a corresponding release ratio, the resource The release module 304 can include the following sub-modules:

The release ratio determining submodule is configured to determine a release ratio corresponding to a resource load level of the application cluster of another level;

a resource ratio release submodule, configured to release resources of the application cluster of the other level according to the release ratio;

The resource saving submodule is released, and the released resource is saved in the resource pool.

In an embodiment of the present application, the resource ratio release submodule may include the following units:

a current resource obtaining unit, configured to acquire a current resource of the application cluster of the other level;

The current access traffic unit is configured to obtain current access traffic of the application cluster of the other level;

a demand resource calculation unit, configured to calculate a demand resource by using the current access traffic and a preset single-machine limit value;

An idle resource determining unit, configured to determine, by using the current resource and the required resource, an idle resource of the application cluster of the other level;

And an idle resource release unit, configured to release the idle resource according to the release ratio.

The release resource allocation module 305 is configured to allocate the released resource to the application cluster of the resource full load level.

In an embodiment of the present application, the release resource allocation module 205 may include the following sub-modules:

a second resource pool resource application submodule, configured to apply for a resource to the resource pool in which the released resource is saved;

And a third resource processing submodule, configured to allocate resources released in the resource pool to the application cluster of the resource full load level.

For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment.

The embodiment of the present application further provides a system for applying cluster resource allocation in a cloud environment, where the system includes:

One or more processors;

Memory; and

Applying for the resource cluster of the resource full load level;

If the application cluster of the resource full load level fails to apply for resources, the other levels are released according to the resource load level. Resources of other application clusters;

Optionally, the one or more modules may have the following functions:

Optionally, the cloud environment includes a resource pool, and the one or more modules may have the following functions:

Applying for resources to the resource pool;

Optionally, the application cluster has a corresponding priority, and the one or more modules may have the following functions:

Optionally, the resource load level has a corresponding release ratio, and the one or more modules may have the following functions:

Save the released resource to the resource pool.

Optionally, the one or more modules may have the following functions:

Obtaining current resources of the application cluster of the other level;

Obtaining current access traffic of the application cluster of the other level;

The idle resource is released according to the release ratio.

Optionally, the one or more modules may have the following functions:

The embodiment of the present application further provides an apparatus for applying cluster resource allocation in a cloud environment, and the apparatus may include the following modules:

FIG. 5 is a schematic structural diagram of a server provided by an embodiment of the present application. The server 400 can vary considerably depending on configuration or performance, and can include one or more central processing units (CPUs) 422 (eg, one or more processors) and memory 432, one or one The storage medium 430 (for example, one or one storage device in Shanghai) that stores the application 442 or the data 444 above. Among them, the memory 432 and the storage medium 430 may be temporarily stored or persistently stored. The program stored on storage medium 430 may include one or more modules (not shown), each of which may include a series of instruction operations in the server. Still further, central processor 422 can be configured to communicate with storage medium 430, executing a series of instruction operations in storage medium 430 on server 400.

Server 400 may also include one or more power sources 426, one or more wired or wireless network interfaces 450, one or more input and output interfaces 758, one or more keyboards 456, and/or one or more operating systems 441. For example, Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

The central processor 422 can execute instructions on the server 400 for:

Applying for the resource cluster of the resource full load level;

Optionally, the one or more modules may have the following functions:

Applying for resources to the resource pool;

Save the released resource to the resource pool.

Optionally, the one or more modules may have the following functions:

Obtaining current resources of the application cluster of the other level;

Obtaining current access traffic of the application cluster of the other level;

The idle resource is released according to the release ratio.

Optionally, the one or more modules may have the following functions:

The various embodiments in the present specification are described in a progressive manner, and each embodiment focuses on differences from other embodiments, and the same similar parts between the various embodiments can be referred to each other.

Those skilled in the art will appreciate that embodiments of the embodiments of the present application can be provided as a method, apparatus, or computer program product. Therefore, the embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Moreover, embodiments of the present application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.

In a typical configuration, the computer device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The memory may include non-persistent memory, random access memory (RAM), and/or non-volatile memory in a computer readable medium, such as read only memory (ROM) or flash memory. RAM Is an example of a computer readable medium. Computer readable media includes both permanent and non-persistent, removable and non-removable media. Information storage can be implemented by any method or technology. The information can be computer readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory. (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, Magnetic tape cartridges, magnetic tape storage or other magnetic storage devices or any other non-transportable media can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-persistent computer readable media, such as modulated data signals and carrier waves.

Embodiments of the present application are described with reference to flowcharts and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing terminal device to produce a machine such that instructions are executed by a processor of a computer or other programmable data processing terminal device Means are provided for implementing the functions specified in one or more of the flow or in one or more blocks of the flow chart.

The computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device. The instruction device implements the functions specified in one or more blocks of the flowchart or in a flow or block of the flowchart.

These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device such that a series of operational steps are performed on the computer or other programmable terminal device to produce computer-implemented processing, such that the computer or other programmable terminal device The instructions executed above provide steps for implementing the functions specified in one or more blocks of the flowchart or in a block or blocks of the flowchart.

While a preferred embodiment of the embodiments of the present application has been described, those skilled in the art can make further changes and modifications to the embodiments once they are aware of the basic inventive concept. Therefore, the appended claims are intended to be interpreted as including all the modifications and the modifications

Finally, it should also be noted that in this context, relational terms such as first and second are used merely to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply these entities. There is any such actual relationship or order between operations. Moreover, the terms "including", "comprising" or any of them Other variations are intended to encompass non-exclusive inclusions, such that a process, method, article, or terminal device that comprises a plurality of elements includes not only those elements but also other elements not specifically listed or included The elements inherent in methods, articles, or terminal equipment. An element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that comprises the element, without further limitation.

The method for applying cluster resource allocation in a cloud environment provided by the present application, a device for applying cluster resource allocation in a cloud environment, and a system for applying cluster resource allocation in a cloud environment are described in detail herein. The principles and implementations of the present application are described by using specific examples. The description of the above embodiments is only for helping to understand the method and core ideas of the present application. Meanwhile, for those skilled in the art, according to the idea of the present application The details of the present invention and the scope of application are subject to change. In the above, the contents of the present specification should not be construed as limiting the present application.

Claims

A system for applying cluster resource allocation in a cloud environment, characterized in that the system comprises:

One or more processors;

Memory; and

One or more modules, the one or more modules being stored in the memory and configured to be executed by the one or more processors, the one or more modules having the following functions:

Obtain resource usage rates of application clusters in a specified transaction link in a cloud environment;

Determining, according to the resource usage rate, a resource load level of each application cluster; the resource load level includes a high occupancy level and a low occupancy level;

Applying for the application cluster of the high occupancy level;

If the application for the high-occupancy application cluster fails, the resource of the application cluster with a low occupation level is released according to the resource load level;

Allocating the released resources to the application cluster of the high occupancy level.
A method for applying cluster resource allocation in a cloud environment, characterized in that:

Obtain resource usage rates of application clusters in a specified transaction link in a cloud environment;

Determining, according to the resource usage rate, a resource load level of each application cluster; the resource load level includes a high occupancy level and a low occupancy level;

Applying for the application cluster of the high occupancy level;

If the application for the high-occupancy application cluster fails, the resource of the application cluster with a low occupation level is released according to the resource load level;

Allocating the released resources to the application cluster of the high occupancy level.
The method according to claim 2, wherein the step of acquiring the resource usage rate of each application cluster in the specified transaction link in the cloud environment comprises:

When the access traffic is increased, the resource usage rate of each application cluster in the specified transaction link in the cloud environment is obtained.
The method of claim 2, wherein the cloud environment includes a resource pool, and the step of requesting resources for the application cluster of the high occupancy level comprises:

Applying for resources to the resource pool;

Allocating resources in the resource pool to the application cluster of the high occupancy level when the resources of the resource pool are sufficient;

When the resources of the resource pool are exhausted, the application for the high occupancy level application cluster fails.
The method according to claim 4, wherein the application cluster has a corresponding priority, and the step of allocating resources in the resource pool to the application cluster of the high occupancy level comprises:

When the application cluster of the high-occupancy level is two or more, the resources in the resource pool are allocated according to the application cluster whose priority is a high occupation level.
The method according to claim 2 or 4, wherein the resource load level has a corresponding release ratio, and the step of releasing resources of the application cluster of the low occupancy level according to the resource load level comprises:

Determining the release ratio corresponding to the resource load level of the application cluster with a low occupancy level;

Release the resources of the low occupancy level application cluster according to the release ratio;

Save the released resource to the resource pool.
The method according to claim 6, wherein the step of releasing resources of the low occupancy level application cluster according to a release ratio comprises:

Obtaining a current resource of the application cluster of the low occupancy level;

Obtaining current access traffic of the application cluster of the low occupancy level;

Calculating the demand resource by using the current access traffic and the preset stand-alone limit value;

Determining, by the current resource and the required resource, an idle resource of the application cluster of the low occupancy level;

The idle resource is released according to the release ratio.
The method according to claim 2 or 4, wherein the step of allocating the released resource to the application cluster of the high occupancy level comprises:

Request a resource from a resource pool that holds the released resources;

Allocating resources released in the resource pool for the high occupancy level application cluster.
A device for applying cluster resource allocation in a cloud environment, characterized in that:

a resource usage acquisition module, configured to acquire resource usage rates of application clusters in a specified transaction link in a cloud environment;

a resource load level determining module, configured to determine a resource load level of each application cluster according to the resource usage rate; the resource load level includes a high occupancy level and a low occupancy level;

a resource application obtaining module, configured to apply for resources for the application cluster of the high occupancy level;

a resource release module, configured to release resources of an application cluster of a low occupancy level according to a resource load level if the resource application fails for the application cluster of the high occupancy level;

And a resource allocation module, configured to allocate the released resource to the application cluster of the high occupancy level.
A method for applying cluster resource allocation in a cloud environment, characterized in that:

Obtain the resource load status of each application cluster in the cloud environment;

Determining, according to the resource load status, a resource load level of the application cluster; the resource load level includes a resource full load level and other levels;

Applying for the resource cluster of the resource full load level;

If the application cluster of the resource full-load level fails to apply for resources, the resources of other levels of the application cluster are released according to the resource load level;

The released resources are allocated to an application cluster of the resource full load level.
The method according to claim 10, wherein the step of acquiring the resource load status of each application cluster in the cloud environment comprises:

When the access traffic is increased, the resource load status of each application cluster in the cloud environment is obtained.
The method according to claim 10, wherein the cloud environment includes a resource pool, and the step of applying for resources for the application cluster of the resource full load level comprises:

Applying for resources to the resource pool;

When the resources of the resource pool are sufficient, the resources in the resource pool are allocated to the application cluster of the resource full load level;

When the resources of the resource pool are exhausted, the application cluster for the resource full load level fails to apply for resources.
The method according to claim 12, wherein the application cluster has a corresponding priority, and the step of allocating resources in the resource pool to the application cluster of the resource full load level comprises:

When the application cluster of the resource full load level is two or more, the resource in the resource pool is allocated according to the application cluster whose priority is the resource full load level.
The method according to claim 10 or 12, wherein the resource load level has a corresponding release ratio, and the step of releasing resources of other levels of the application cluster according to the resource load level comprises:

Determine the release ratio corresponding to the resource load level of the application cluster of other levels;

Release the resources of the other level of the application cluster according to the release ratio;

Save the released resource to the resource pool.
The method according to claim 14, wherein the step of releasing resources of the other application cluster according to a release ratio comprises:

Obtaining current resources of the application cluster of the other level;

Obtaining current access traffic of the application cluster of the other level;

Calculating the demand resource by using the current access traffic and the preset stand-alone limit value;

Determining, by the current resource and the required resource, an idle resource of the application cluster of the other level;

The idle resource is released according to the release ratio.
The method according to claim 10 or 12, wherein the step of allocating the released resource to the application cluster of the resource full load level comprises:

Request a resource from a resource pool that holds the released resources;

Allocating resources released in the resource pool for the application cluster of the resource full load level.
A device for applying cluster resource allocation in a cloud environment, characterized in that:

a resource load status obtaining module, configured to acquire a resource load status of each application cluster in a cloud environment;

a resource load level determining module, configured to determine a resource load level of the application cluster according to the resource load status; the resource load level includes a resource full load level and other levels;

a resource application module, configured to apply for resources for an application cluster of the resource full load level;

a resource release module, configured to release resources of other levels of the application cluster according to a resource load level when the application cluster for the resource full load level fails to apply for a resource;

And releasing a resource allocation module, configured to allocate the released resource to an application cluster of the resource full load level.