WO2013120243A1

WO2013120243A1 - System and method for processing virtual machine in cloud platform

Info

Publication number: WO2013120243A1
Application number: PCT/CN2012/071063
Authority: WO
Inventors: 王烽; 王军
Original assignee: 华为技术有限公司
Priority date: 2012-02-13
Filing date: 2012-02-13
Publication date: 2013-08-22
Also published as: CN103354990A; CN103354990B

Abstract

The present invention provides a system and a method for processing a virtual machine in a cloud platform. The system comprises: an acquisition module, for acquiring an automation rule, the automation rule comprising an automatic processing strategy; and a processing module, for generating, according to the automatic processing strategy, an automatic processing parameter when executing the automation rule, and invoking the cloud platform to automatically process a set of virtual machines according to the automatic processing parameter. According to the embodiment of the present invention, the automatic processing parameter can be generated according to the automatic processing strategy when the automation rule is executed, and the cloud platform is invoked to automatically process a set of virtual machines according to the automatic processing parameter, thereby improving the flexibility in the automatic processing on the virtual machine cluster.

Description

System and method for processing virtual machines in a cloud platform

Embodiments of the present invention relate to the field of computer information, and more particularly, to a system and method for processing a virtual machine in a cloud platform. Background technique

With the development of cloud computing, the need for technologies based on automated rules to manage virtual machine clusters in cloud environments is growing. The life cycle of a virtual machine cluster goes through phases of deployment, operation, destruction, and failure, and the deployed policy is generally determined when the virtual machine cluster is created. For example, by templating a virtual machine cluster, large-scale deployment without human intervention can be achieved, which greatly speeds up the deployment of virtual machine clusters.

Currently, users can deploy virtual machine clusters using virtual machine cluster deployment templates provided by cloud providers. The virtual machine cluster deployment template provides a small number of defined automatic processing rules for automating virtual machine clusters.

However, after the virtual machine cluster is deployed, the virtual machine cluster can only work with fixed automated processing rules. Therefore, the automated processing of virtual machine clusters is very inflexible. Summary of the invention

Embodiments of the present invention provide a system and method for processing a virtual machine in a cloud platform, which can improve the flexibility of automatic processing of a virtual machine cluster.

In one aspect, a system for processing a virtual machine in a cloud platform is provided, including: an obtaining module, configured to obtain an automation rule, the automation rule includes an automatic processing policy; and a processing module, configured to perform the The automatic processing strategy generates automatic processing parameters, and invokes the cloud platform to automatically process a group of virtual machines according to the automatic processing parameters.

In another aspect, a method for processing a virtual machine in a cloud platform is provided, including: acquiring an automation rule, the automation rule including an automatic processing policy; generating an automatic processing parameter according to the automatic processing policy when executing the automation rule, and according to The automatic processing parameter invokes the cloud platform to automatically process a group of virtual machines.

The technical solution may generate an automatic processing parameter according to an automatic processing policy when executing the automation rule, and invoke the cloud platform to automatically perform a set of virtual machines according to the automatic processing parameter. This improves the flexibility of automated processing of virtual machine clusters. DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings to be used in the embodiments or the description of the prior art will be briefly described below. Obviously, the drawings in the following description are only the present invention. For some embodiments, other drawings may be obtained from those of ordinary skill in the art without departing from the drawings.

1 is a block diagram of a system for processing a virtual machine in a cloud platform according to an embodiment of the present invention.

2 is a schematic diagram of a system architecture for processing a virtual machine in a cloud platform according to an embodiment of the present invention.

3 is a block diagram of a rule execution unit of one embodiment of the present invention.

4 is a block diagram of a policy management unit of one embodiment of the present invention.

FIG. 5 is a schematic flow chart of a method for processing a virtual machine in a cloud platform according to an embodiment of the present invention.

Figure 6 is a schematic flow diagram of an automation rule in accordance with one embodiment of the present invention. Detailed ways

The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without making creative labor are within the scope of the present invention.

A general virtual machine (VM) cluster deployment template can only provide a few defined automatic processing policies. For example, a regular VM cluster deployment template can define a capacity reduction policy, but cannot define a VM to be deleted when volume reduction. Select a policy that removes a VM and cannot specify which VM to delete. There are only three expansion and reduction strategies (absolute increments, percentage increments, and absolute numbers) that cannot be extended, and users cannot set or customize automatic processing policies during VM cluster operation. In addition, the conventional VM cluster deployment template generally only describes the static information and structure of the VM cluster, and lacks description and definition of the automation rules of the VM cluster in the cloud environment. For example, the conventional VM cluster deployment template does not consider how to increase during the operation of the VM cluster. And reduce the VM, therefore, the defined rules are not flexible and cannot be extended.

A set of processing VMs that can be customized based on automated rules is proposed in accordance with an embodiment of the present invention. Group systems and methods. An automated rule for automating processing of a VM cluster can be set up in an automation template in accordance with an embodiment of the present invention, and the user can set the automation rules (eg, the automatic processing policies contained therein) during the operation of the VM cluster, Thereby, the automatic processing capability and flexibility of the VM cluster can be greatly improved.

1 is a block diagram of a system for processing a VM in a cloud platform according to an embodiment of the present invention. The system of Figure 1 includes: an acquisition module 110 and a processing module 120.

The acquisition module 110 obtains an automation rule that includes an automatic processing policy. The processing module 120 generates an automatic processing parameter according to the automatic processing policy when executing the automation rule, and invokes the cloud platform to automatically process a group of virtual machines according to the automatic processing parameter.

For example, cloud platforms can provide cloud-based services for developers to create applications. In other words, cloud platforms allow developers to run programs written in the cloud and/or use the cloud. service provided. For example, the cloud platform 250 shown in Figure 2 can provide cloud-based services. Automated processing of a set of VMs in cloud platform 250 involves automatic capacity tuning (Auto Scaling) of a set of VMs in various cloud platforms (eg, cloud platforms such as Amazon, Galax, or vCenter), in accordance with an embodiment of the present invention Not limited to this, for example, a group of VMs may be a VM cluster, and automatic processing may also involve resource adjustments between VM clusters. A VM cluster can include one or more sets of VMs. For example, cloud platform 250 can provide processing or management actions for a group of VMs in a VM cluster, such as adding, deleting, modifying, querying, and the like. It should be understood that when the above virtual machine cluster is a composite virtual machine cluster (members are virtual machine clusters), the above set of VMs may also refer to a VM cluster.

For example, automatic processing can include operations such as capacity expansion, volume reduction, or resource adjustment between VM clusters. For example, the automatic processing parameter can be an automatic capacity adjustment parameter or a resource adjustment parameter between VM clusters. In other words, the above automatic processing parameters are parameters required for automatic processing of a group of VMs, and the automatic processing parameters can be generated according to the selected automatic processing strategy when the automation rules are executed.

Automation rules can take the form of plugins, for example, automation plugins 1, 2 n. An automation rule corresponds to a group of VMs in a VM cluster. An automated rule is performed for each set of VMs for automatic processing according to an embodiment of the present invention, and an automatically processed action, a name of an automatic process, a cooling time window, a trigger condition, an automatic processing policy, and the like can be defined for each VM group. According to an embodiment of the present invention, the obtaining module 110 may directly acquire an automation rule in the form of a plug-in, may also parse a preset template of the user, and convert the automation template into an automation rule. Then the plugin.

For example, the above automation rule is a rule or logic for determining automatic capacity adjustment of a VM cluster during operation of the VM cluster. For example, the automation rule may involve judgment and triggering of capacity expansion or volume reduction, automatic processing policy selection, and automatic The acquisition of the capacity adjustment parameters and the call to the external cloud platform 250, etc., are not limited in this way according to an embodiment of the present invention, for example, the user may add more judgments and other rules as needed.

For example, the automatic processing policy described above indicates a policy of how to perform automatic capacity adjustment on a group of VMs or groups of VMs in a VM cluster during operation of the VM cluster, for example, how to expand or reduce capacity, according to an embodiment of the present invention. For this reason, for example, the above automatic processing policy may also indicate how to perform resource adjustment between clusters.

For example, during the operation of the VM cluster, the automation module is obtained by the acquisition module 110 of the system of FIG. 1, wherein the automation rule includes an automatic processing policy; and the processing module 120 generates an automatic processing parameter according to the automatic processing policy, and according to the automatic processing The parameter invokes the cloud platform to automatically process a set of virtual machines. Since the automatic processing parameters according to the embodiment of the present invention are generated when the automation rules are executed, the user can conveniently set the automatic processing policy during the running of the VM cluster, so as to modify these automatic processing parameters, thereby greatly Improve the automatic processing power and flexibility of VM clusters.

The above automatic processing strategy may be used to select a VM when performing automatic capacity adjustment, which may be preset in an automation template, and optionally, may also invoke an automatic processing policy setting or request a user to at least during the above-mentioned one VM running process. An automatic processing policy is set up so that the system invokes the cloud platform 250 to perform automatic capacity adjustment on the set of VMs according to the needs of the user. For example, the processing module 120 may obtain an automatic processing policy according to the user when it is determined according to an automation rule that a set of VMs (for example, a VM instance group or a VM cluster) needs to be expanded (Auto Scale Out) or reduced (Auto Scale In). Set the resulting automatic processing parameters, and according to these automatic processing parameters to expand or reduce the capacity of a group of VMs in the VM cluster or the entire VM cluster.

According to an embodiment of the present invention, an automatic processing parameter may be generated according to an automatic processing policy when the automation rule is executed, and the cloud platform is invoked to automatically process a group of virtual machines according to the automatic processing parameter, thereby improving automatic processing of the virtual machine cluster. flexibility.

2 is a schematic diagram of a system architecture for processing a VM in a cloud platform according to an embodiment of the present invention. The system of FIG. 2 includes: an acquisition module 210 and a processing module 220, similar to the acquisition module 110 and the processing module 120 of FIG. 1, and detailed descriptions are omitted as appropriate herein. The acquisition module 210 obtains an automation rule that includes an automatic processing policy. The processing module 220 generates an automatic processing parameter according to the automatic processing policy when executing the automation rule, and invokes the cloud platform 250 to automatically process a group of virtual machines according to the automatic processing parameter.

According to an embodiment of the present invention, the processing module 220 includes: a rule execution unit 222 and a policy management unit 223. The rule execution unit 222 selects an automatic processing policy when executing the automation rule, requests the automatic processing parameter according to the selected automatic processing policy, and automatically processes the set of virtual machines according to the automatic processing parameter calling cloud platform 250. The policy management unit 223 manages the setting of the automatic processing policy, generates the automatic processing parameter according to the setting of the selected automatic processing policy, and provides the automatic processing parameter to the rule execution unit. Figure 3 is a block diagram of a rule execution unit in accordance with one embodiment of the present invention. 4 is a block diagram of a policy management unit of one embodiment of the present invention.

For example, when executing an automation rule, an automatic processing policy can be selected according to the triggered automatic processing action (for example, expansion or volume reduction). For example, when the expansion is triggered, the VM type selection policy and the VM number selection policy can be selected and triggered. When volume reduction is performed, the VM instance selection policy and the VM number selection policy can be selected. The automatic processing parameters corresponding to the automatic processing policy can then be requested or invoked according to the selected automatic processing policy. Finally, the cloud platform 250 can be invoked to automatically process the set of virtual machines according to the automatic processing parameters.

According to another embodiment of the present invention, the processing module 220 further includes: a rule management unit 221. The rules management unit 221 receives the automation rules from the acquisition module 210 and is used to add or delete the automation rules.

This automation rule can take the form of an automated rule plugin (for example, it can be implemented by a script or by a drl file generated by Drools or DocObject software). The rule management unit 221 can dynamically modify, add, or delete an automated rule plugin. The rule execution unit 222 triggers automatic processing when the trigger condition is met, for example, triggering expansion or volume reduction. Automatic processing may include automatic capacity adjustment of a set of VMs (or VM clusters) according to the automatic processing parameters described above and/or resource adjustments between VM clusters. In addition, the rule execution unit 222 may request the automatic management parameter from the policy management unit 223, for example, when the automatic capacity adjustment is performed, the automatic capacity adjustment parameter may be requested. The policy management unit 223 can manage the settings of the at least one automatic processing policy, and after receiving the request of the rule execution unit 222, feed back the automatic processing parameters that need to be taken to the rule execution unit 222, for example, provide automatic capacity adjustment parameters (for example, expand the VM). The number and specifications, etc., or the number and examples of volume reduction). The setting of the above automatic processing policy may refer to a related parameter set by the user for calculating an automatic processing parameter, for example, a ratio value of volume reduction (or capacity expansion). Take For example, the ratio value is set to 0.05 and the ratio base value is 100. The automatic processing parameter can be calculated as 5, that is, the capacity reduction (or expansion) is 5. The setting of the automatic processing policy by the user may be implemented by a form of a plug-in (for example, a script or a rule engine), and the embodiment according to the present invention is not limited thereto, for example, may also be implemented by prompting a user input form, for example, In the pop-up window, the user is prompted to enter a ratio of the ratio of the volume reduction (or expansion).

Optionally, as another embodiment, the processing module 220 further includes: a parameter obtaining unit 224. The parameter obtaining unit 224 obtains the real-time feature parameters of the set of VMs, and provides the real-time feature parameters to the rule execution unit 221, wherein the rule execution unit 222 further determines, according to the preset threshold of the trigger condition, the real-time feature parameter The trigger condition selects the automatic processing strategy to trigger automatic processing of the above set of VMs.

For example, the parameter acquisition unit 224 can be configured to calculate real-time characteristic parameters of the object based on the VM cluster parameters, such as the average CPU utilization of the above-described set of VMs (or VM clusters) and the like.

Optionally, as another embodiment, the system of FIG. 1 further includes: a monitoring module 230. The monitoring module 230 monitors the first parameter of the set of VMs in real time, and provides the first parameter to the parameter obtaining unit 224, wherein the parameter obtaining unit 224 further calculates the real-time feature parameter according to the first parameter; and/or for real-time monitoring The second parameter of the set of VMs is provided, and the second parameter is provided to the policy management unit 223, wherein the policy management unit 223 further calculates the automatic processing parameter according to the second parameter and the user's setting of the selected automatic processing policy. For example, parameter acquisition unit 224 can perform statistical calculations on data for a set of VMs or VM clusters obtained from monitoring module 230 to obtain an average, variance, etc. of a set of VMs or VM clusters.

For example, the monitoring module 230 can be an external monitoring agent, for example, a Watch module in an Automatic Management Engine (AME) for providing parameters or other monitoring parameters for a group of VMs (or VM clusters). For example, the first parameter may be a real-time parameter such as the CPU utilization of each VM, and the second parameter may also be a real-time parameter of the VM, for example, when it is required to jointly determine according to a certain real-time parameter of a group of VMs and an automatic processing policy setting. When the parameters are automatically processed, the policy management unit 223 can acquire the corresponding second parameter through the monitoring module 230.

Referring to FIG. 3, in the scheme for implementing automatic capacity adjustment, the rule execution unit 222 includes: a trigger subunit 310 and an invocation subunit 320. The triggering sub-unit 310 selects the automatic processing policy according to the triggering condition, and compares the real-time feature parameter with the preset threshold of the triggering condition. When the real-time feature parameter satisfies the preset threshold of the triggering condition, the triggering sub-unit 320 is triggered. One of the above The group VM performs automatic capacity adjustment. The calling sub-unit 320 requests the policy management unit 223 to return the automatic capacity adjustment parameter according to the selected automatic processing policy, and invokes the cloud platform 250 to perform automatic capacity adjustment on the set of VMs according to the automatic capacity adjustment parameter.

For example, the trigger sub-unit 310 matches the real-time feature parameters and trigger conditions of the VM, and triggers automatic processing. The calling sub-unit 320 selects an automatic processing policy corresponding to the above-mentioned automatic processing of the trigger from the automatic processing policy configured in the automation rule according to the triggered automatic processing (for example, expansion or volume reduction), and requests the policy management unit 223 to The automatic capacity adjustment parameter corresponding to the selected automatic processing strategy, and the returned result is used as an automatic capacity adjustment parameter (or an action parameter) to invoke the drive module 240 of the external cloud platform. For example, there are three automatic processing strategies that may be adopted, a VM number selection policy, a VM type selection policy, and a VM instance selection policy. Then, the rule execution unit 222 invokes the external cloud platform 250 to perform an action of deleting or creating a VM according to the policies and the corresponding automatic capacity adjustment parameters.

Referring to FIG. 4, the policy management unit 223 includes: a management subunit 410 and a policy subunit 420. The management sub-unit 410 diverts the request based on the identification of the automatic processing policy selected from the request from the calling sub-unit 320, and/or the setting for dynamically adding a new automatic processing policy. After receiving the request from the management subunit 410, the policy subunit 420 calculates the automatic capacity adjustment parameter according to the setting of the selected automatic processing policy by the user and the second parameter of the set of VMs, and the automatic capacity adjustment parameter Returned to the calling subunit 320. Of course, the automatic capacity adjustment parameter can also be calculated according to the user's setting of the automatic processing policy selected above, and the automatic capacity adjustment parameter is returned to the calling subunit 320.

The management sub-unit 410 is responsible for offloading the request to set the plug-in of the automatic processing policy, and can dynamically add a new plug-in for the automatic processing policy. If the newly added auto-processing policy setting plug-in (for example, script or rule engine) can be placed in the specified directory, the management sub-unit 410 can call this script according to the standard rules. The policy sub-unit 420 can automatically determine the automatic processing parameters according to the predetermined automatic processing policy and the parameters input by the user, for example, the number of VMs to be expanded, VM specifications, and the like. The settings plug-in for the automatic processing policy can be dynamically added by the user as needed. The policy subunit 420 can be at least one, for example, the policy subunits 1, 2n, respectively corresponding to at least one automatic processing policy setting. For example, the identifier of the automatic processing policy may be the name of the automatic processing policy, and the management sub-unit 410 determines the setting of the automatic processing policy corresponding to the identifier according to the identifier.

According to an embodiment of the invention, the processing module 220 can also be triggered according to conditions, timing or events. The above set of VMs are automatically processed.

Accordingly, the trigger can be divided into a condition trigger, an event trigger, or a timer trigger.

The conditional trigger is obtained by the monitoring module 230 to obtain external parameters (eg, the CPU utilization of a group of VMs or the CPU utilization of the VM cluster) to the parameter acquisition unit 224 of the processing module 220, and then to the triggering subunit 310 of the rule execution unit 222. To determine if the condition is met (for example, if the CPU utilization is greater than 90%).

Triggered by a similar condition, event triggering triggers automatic processing when a previously defined event is detected (for example, when a VM in a set of VMs has an error in capacity expansion, or the VM cannot be created successfully) (for example, inter-cluster resource adjustment) ).

Time triggering means that the trigger subunit 310 detects the specified time (predefined time) and triggers automatic processing.

According to an embodiment of the present invention, the above automatic processing policy includes: at least one of a policy for selecting a type of a VM, a policy for selecting a number of VMs, and a policy for selecting an instance of a VM, wherein the rule execution unit The at least one of the above-described policies for selecting the type of the virtual machine, the above-described policy for selecting the number of virtual machines, and the above-described policy for selecting an instance of the virtual machine are selected.

For example, an automatic processing policy includes a VM quantity selection policy, a VM type selection policy, or a VM instance selection policy.

The VM number selection policy (VMCountPolicy) refers to the number of VMs that are added or deleted when expanding or reducing capacity. There are many rules for adding or deleting VMs. Users can customize them. For example, users can be set to pre-defined. The value indicates that several units are added; it can be set to prompt the user to enter the number of VMs themselves; it can be set to a random number; or it can be set to a user-defined script.

VM type selection policy (VMClassPolicy) means that when expanding capacity, it is decided to increase the type of VM (that is, the specification parameters of the VM, such as the number of CPUs, the size of the disk, the size of the memory, etc.), similar to the number selection policy, the user can set Predefined templates (which can define VM specifications), the same type as other VMs in the set of VMs, or templates that prompt the user for input, and so on.

The VM Instance Selection Policy (VMInstancePolicy) is an instance of the VM that was decided to delete when the volume is reduced. The user can set the predetermined script file (pre-defined deletion rules, such as deleting the VM with the lowest CPU utilization, the VM created at the beginning, etc.), prompting the user to input the deletion. VM, or a pre-configured VM, etc.

In accordance with an embodiment of the present invention, the acquisition module 210 obtains an automated rule by parsing a predetermined automation template, the automated template including an extensible markup language file.

For example, when a user needs to use the services provided by the cloud service provider's VM cluster, the automation template can be configured as needed, that is, the automation rules are configured in the automation template, and the configured automatic template is uploaded to the system of FIG. In addition, the setting of trigger conditions in the automation rules and the setting of the automatic processing policy can be placed in the specified directory in the form of plug-ins (for example, implemented by scripts or rule engines). The system of Figure 1 deploys a VM cluster based on the above automated template. The acquisition module 210 can parse the user-preset automation template and convert it into at least one automation rule, and the automation rule can take the form of a plug-in, for example, the automation plug-in 1, 2 n . An automation rule corresponds to a group of VMs in a VM cluster. For example, when deploying a VM cluster, the acquisition module 210 parses the automation template, translates the automation rules into implementation rules, and adds the implementation rules to the rule execution unit 222 of the processing module 220.

According to an embodiment of the present invention, the above-described automation rules can be set by a script or a rule engine or by inputting parameters in a pop-up window.

According to an embodiment of the present invention, the above automatic processing policy may be set by a script or a rule engine or by inputting a parameter in a popup window.

For example, the setting related parameters of the automatic processing policy (related parameters for generating automatic processing parameters, for example, ratio values) or the thresholds of the triggering conditions of the automatic rules may be set in the plug-in implemented by the script rule engine, or prompt the user to Enter in the pop-up window.

For example, the automation template may take the form of an XML file, in which case the XML file may also be verified so that the XML file is a valid file, which is not based on embodiments of the present invention, for example, an automated template. You can also use the format of a json (JavaScript Object Notation) file or the format of an ovf (Open Virtualization Format) file.

Optionally, as another embodiment, the processing module 220 invokes the internal platform to invoke the cloud platform 250 to automatically process the set of VMs. The system of FIG. 2 further includes: a driving module 240. The driver module 240 translates the call to the internal interface to the application interface that invokes the cloud platform 250.

The driver module 240 is configured to adapt an external platform for converting the internal unified interface call into an API (Application Programming Interface) call of the external platform, that is, by converting the calling internal unified interface to calling the underlying platform. Interface, for example, will convert the call to the AddVM interface for creating VMs in AME to the creation of an external Galax platform. Create a call to the mnlnstance interface of the VM.

Optionally, in another embodiment, the processing module 220 is further configured to allow automatic processing on the set of VMs when a time interval between two automatic processes exceeds a preset threshold.

For example, if the time interval between two automatic processes is less than the preset threshold, even if the trigger condition satisfies the preset threshold, the above-mentioned VMs are not allowed to be automatically processed, only if the trigger condition satisfies the preset threshold and two automatic processes are performed. The automatic processing of the above set of VMs is allowed only when the time interval exceeds the preset threshold.

According to the embodiment of the present invention, the upper and lower limits of the available resources of the set of VMs are set in the above-mentioned automatic template, and the processing module is further configured to determine whether to allow the foregoing one according to the upper and lower limits of the available resources of the set of virtual machines. The group VM performs automatic capacity adjustment.

According to an embodiment of the present invention, an upper limit and a lower limit of available resources of a group of VMs in a VM cluster may be set or defined in an automation template, and a set of VMs of the VM cluster is a set of VM instances, which may be scaled, that is, an VM instance is added or Reduce the VM instance. A group of VMs has a maximum number of instances and a minimum number of instances, and the number of scaled VMs cannot exceed the range of available resources of a group of VMs (or the upper and lower limits of available resources of the VM cluster). A group of VMs can also be called an instance group. For a node that cannot be scaled (such as a control node), it is considered to be a VM cluster with the same resource upper and lower limits and 1 being the same. For example, you can define the upper and lower limits of the cluster's available resources and the current number of instances through the cluster properties. For example, an example of defining the upper and lower limits of a cluster using an automated template in XML can be expressed as: <cluster nameprefix="Processor" templateid="ServerTemplate#2" maxinstcount=" 100" mininstcount="l" initinstcount="10"> .

The following is an example of the expansion and decompression sections of an automation template in XML form. The tag <Automations> defines the automated processing actions. The tag attributes include: name (automated name), for example, AutoScaleOut or AutoScaleOut; timeWindow, in which no other automatic expansion or decompression operations can be performed, in seconds. . Automated processing involves the following problem: the trigger condition is defined by <1 18861; the automatic processing action is defined by <(^011>tag; the automatic processing strategy is defined by <1 1116>. Igger typ - ⁱ co^dl ioi"i">cp Loa.d sgr. SC< Trigger >

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< .·■'' Au oro. on> The following describes the expansion and decompression sections of the above automation template: 1. Automatic expansion of the VM instance group (corresponding to the expansion of the expansion module in the above automation template example) (label Automation)

Name: Auto CapacityOut ( AutoScaleOut )

Cooling time window: 2 minutes

Trigger (label Trigger ):

Trigger type: conditional trigger ( condition )

Trigger condition: CPU utilization is greater than 90% (cpuload>90)

Action (label Action):

Trigger Action: Create VM ( AddVM )

Automatic Processing Strategy 1: VM Type Selection Policy (VMClassPolicy) Policy Name: Assigned ( Assigned )

Specified value: ServerTemplate

Automatic Processing Strategy 2: VM Quantity Selection Policy (VMCountPolicy) Policy Name: Ratio (Ratio)

Scale basis value: Maximum number (MaxCount)

Ratio value: 0.05 That is, an automatic expansion mode template is defined according to an embodiment of the present invention:

1. When the average CPU usage of the instance group is greater than 90%, the VM of the instance group is expanded (the trigger rule is triggered by the condition).

2. The VM type selection policy is set to increase the VM type to a pre-configured template ServerTemplate (for example, using a custom VM type) to expand.

3. The number of VMs is selected. The ratio is expanded according to the ratio of the instance groups: 5% of the upper limit of the instance group (or cluster) is expanded each time. (For example, the instance group upper limit can be pre-configured or defined in the template)

4. Define the cooling time window as 2 minutes, that is, after this automatic expansion operation, no other automatic actions can be performed within 2 minutes.

Second, the VM instance group automatically reduces the volume (corresponding to the volume reduction part in the above automation template example):

Automation (label Automation)

Name: Automatic Capacity Expansion ( AutoScaleln )

Cooling time window: 2 minutes

Trigger (label Trigger ):

Trigger type: conditional trigger ( condition )

Trigger condition: CPU utilization is less than 20% ( cpuload < 20 )

Action (label Action ):

Trigger Action: Delete VM ( RemoveVM )

Automatic Processing Strategy 1: VM Instance Selection Policy (VMInstancePolicy) Policy Name: Minimum (Minimum)

Attribute value: CPU utilization ( cpuload )

Automatic Processing Strategy 2: VM Number Selection Policy (VMCountPolicy) Policy Name: Script (Script)

Script type: javascript

Script content: MaxCount*0.05

That is, such an automatic volume reduction mode template is defined according to an embodiment of the present invention:

1. When it is detected that the average CPU utilization of the instance group is less than 20%, the volume reduction of the VM of the instance group is triggered.

2. The VM instance selection policy specifies that the VM with the lowest CPU utilization is deleted. 3. VM number selection strategy, which specifies the number of VMs to be reduced by using a script, and reduces the capacity according to the ratio of the instance group (for example, the number of reductions is 5% of the upper limit of the instance group).

4. Define the cooling time window as 2 minutes, that is, after this automatic volume reduction operation, no other automatic actions can be performed within 2 minutes.

The following details the contents of the trigger condition section in the above automation template: Conditional trigger:

Parameters: type="condition"

Description: Responsible for detecting whether the attribute value of the instance group satisfies the condition, such as whether it is greater or less than the threshold. Can support combination conditions (and, or, not)

Example: <Trigger type="condition">cpuLoad > 90</Trigger> indicates that when the cpuload attribute is greater than 90, automatic processing is triggered (for example, expansion)

The above template only shows the conditional triggering. The embodiment according to the present invention is not limited thereto. For example, event triggering and time triggering can be set in the template, as follows:

Event trigger:

Parameters: type="event"

Description: Automatic processing is triggered when an event is detected. Events can be pre-defined

Example: <Trigger type="event,,>MemberScaleOutError</Trigger>. Indicates that automatic processing (for example, intercluster resource adjustment) is triggered when a MemberScaleOutError event occurs (with member expansion failure).

Time trigger:

Parameters: type="timer"

Description: The specified time is detected and the automatic processing is triggered. This is a predefined example: <Trigger type="timer">2012-12-20:01:02:03</Trigger>.

The following section describes the automatic processing strategy in the above template:

Tags: <Rule>

Description: Defines an automatic processing strategy, ie how to handle it. Different automatic processing categories and methods for different automatic processing

VM number selection policy (VMCountPolicy): Determines the number of VMs to be added or deleted when expanding or reducing capacity.

Class: VMCountPolicy

Name: Script: determined by a user-defined script

Prompted: Prompt user input

Assigned: a value that has been previously defined

Random: The system randomly chooses

Ratio: Proportional increase according to the base number, the base number can be set to the maximum VM number of the group or the current VM number, etc.

VM type selection policy (VMClassPolicy): When expanding, decide to increase the type of VM class: VMClassPolicy

Name:

Script: Determined by a user-defined script.

Prompted: Prompt user input

Assigned: a value that has been previously defined

Random: The system randomly chooses

Same: uses the same type as the other VMs in the instance group

VM instance selection policy (VMInstancePolicy): When descaling, decide to delete the VM instance class: VMInstancePolicy.

Name:

Script: determined by a user-defined script

Prompted: Prompt user input

Assigned: a value that has been previously defined

Random: The system randomly chooses

Minimum: Select the starting parameters (for example, CPU utilization) from small to large

Maximum: Select from the largest to the smallest of the specified parameters

FIG. 5 is a schematic flow chart of a method for processing a VM in a cloud platform according to an embodiment of the present invention. The method of Figure 5 can be performed by the system of Figure 1.

510. Obtain an automation rule, where the automation rule includes an automatic processing policy.

520. Generate an automatic processing parameter according to the automatic processing policy when executing the automation rule, and invoke the cloud platform to automatically process a group of virtual machines according to the automatic processing parameter.

According to an embodiment of the present invention, an automatic processing parameter may be generated according to an automatic processing policy when an automation rule is executed, and the cloud platform is invoked to automatically perform a group of virtual machines according to the automatic processing parameter. Processing, thereby increasing the flexibility of automated processing of virtual machine clusters.

In 520, an automatic processing policy may be selected when the automation rule is executed; the automatic processing parameter is generated according to the setting of the selected automatic processing policy; and the cloud platform is invoked to automatically process the set of virtual machines according to the automatic processing parameter. .

Optionally, as another embodiment, the method of FIG. 5 further includes: adding or deleting the automation rule.

Optionally, as another embodiment, the real-time feature parameters of the set of VMs may be acquired, and the set of VMs is triggered to be processed if the real-time feature parameters meet the preset threshold of the trigger condition, where When you select an automatic processing policy, you select an automatic processing policy based on the trigger condition.

Optionally, as another embodiment, the first parameter of the set of VMs may be monitored in real time, and the real-time feature parameter is further calculated according to the first parameter; and/or the second parameter of the set of VMs is monitored in real time, And calculating the automatic processing parameter according to the second parameter and the setting of the automatic processing policy selected by the user.

According to an embodiment of the present invention, the automatic processing may be an automatic capacity adjustment. In 520, the preset threshold of the real-time feature parameter and the trigger condition may be compared, and when the real-time feature parameter meets the preset threshold of the trigger condition, Triggering automatic capacity adjustment on the set of VMs; and acquiring the automatic capacity adjustment parameter by requesting, and calling the cloud platform to perform automatic capacity adjustment on the set of VMs according to the automatic capacity adjustment parameter.

According to an embodiment of the present invention, when managing the setting of the automatic processing policy, the request may be offloaded according to the identifier of the selected automatic processing policy included in the request, and/or a setting of a new automatic processing policy may be dynamically added; The automatic capacity adjustment parameter may be calculated according to the setting of the at least one automatic processing policy and the second parameter of the set of VMs by the user after receiving the request, or may be automatically processed according to the user according to the user after receiving the request. The setting of the policy calculates the automatic capacity adjustment parameter.

According to an embodiment of the present invention, the at least one automatic processing policy includes: at least one of a policy of selecting a type of a VM, a policy of selecting a number of VMs, and/or a policy of selecting an instance of a VM, wherein the automatic processing policy may be selected from The above-described policy for carrying the type of the selected virtual machine, the above-described policy for selecting the number of virtual machines, and the above-described policy for selecting an instance of the virtual machine select at least one of the policies.

Optionally, as another embodiment, the method of FIG. 5 further includes: parsing a predetermined automation module by parsing Board, to get the automation rules, the above automated template includes an extensible markup language file.

According to an embodiment of the present invention, the above automatic processing policy is set by a script or a rule engine or is set by inputting a parameter in a popup window.

Optionally, as another embodiment, the cloud platform may be invoked by calling the internal interface to perform automatic capacity adjustment on the set of VMs, and the internal interface may be invoked to be converted into an application interface that invokes the cloud platform.

Alternatively, as another embodiment, automatic processing of the set of VMs is allowed if the minimum time interval between two automatic processes exceeds a preset threshold.

Optionally, as another embodiment, the upper and lower limits of the available resources of the foregoing set of VMs are set in the foregoing auto-template, and the upper limit and the lower limit of the available resources of the set of VMs may be further determined whether to allow the set of VMs. Perform automatic capacity adjustment.

In accordance with an embodiment of the present invention, in 520, the set of VMs may also be automatically processed based on conditions, timing, or events.

6 is a schematic flow chart of an automated processing rule in accordance with an embodiment of the present invention.

605, parsing the template, and configuring the automation rules into the automation engine.

For example, 605 can be executed by a deployment engine and 610 through 680 can be executed by an automation engine.

610. Perform an automation rule and determine a trigger type for automatically capacity adjustment of a group of VMs in the VM cluster according to the automation rule. For example, the trigger type may include condition triggering, time triggering, and event triggering.

615. Wait for the trigger event if the event trigger is set in the template.

620. If the above event occurs, perform step 645.

625. When time triggering is set in the template, timing is performed.

630, if the time expires, step 645 is performed.

635. When conditional triggering is set in the template, real-time feature parameters can be obtained. For example, the data reported by the VM is monitored and the parameters of a group of VMs in the VM cluster are obtained, and the average CPU utilization of a group of VMs in the VM cluster is calculated.

640. For example, when the average CPU utilization of a group of VMs in a VM cluster is greater than 90%, according to the trigger action word of the "trigger" tag in "AutoScaleOut" in the template. The segment "Create VM", and then perform step 645; otherwise, cycle the CPU utilization at regular intervals, no longer proceed to the next step.

For example, when the average CPU utilization of a group of VMs in a VM cluster is less than 20%, the VM is deleted according to the trigger action field of the "trigger" tab in "AutoScaleln" in the automation template. Otherwise, The CPU utilization is calculated cyclically at a certain interval, and the next step is not performed.

645. According to the “cooling time window” field in the template, obtain a cooling time window size, determine whether it is currently in the cooling time window of the last automatic action, and if yes, execute 610, otherwise, execute 650.

650, determine the type of automatic capacity adjustment. For example, according to the above trigger condition, whether the expansion or volume reduction triggers the corresponding automatic processing strategy is selected.

655. When the capacity expansion needs to be performed, according to the "automatic processing strategy 2" in the "action" label "trigger action" in the expansion part of the template, the policy rule is selected according to the number of VMs, for example, according to the "automatic processing strategy 2" in the template. In the "Ratio Base Value" and "Ratio Value", the decision expands the cluster's upper limit by 5% (MaxCount is the cluster upper limit in the previously defined cluster upper and lower limits). For example, if the cluster upper limit is 100 VMs, then 5 VMs will be added.

660. According to the “automatic processing policy 1” in the “action” label “trigger action” in the expansion part of the template, select a policy rule according to the VM type, for example, according to the policy template specified in the “automatic processing policy one” in the template. ServerTemplate" , the type of the decision expansion VM uses the template ServerTemplate (for example, using a custom VM type, that is, creating VM specifications, etc.). For example, the setting of the automatic processing policy can be implemented by setting the above policy template "ServerTemplate".

When the volume reduction needs to be performed, according to the "Automatic Processing Strategy 2" in the "Action" tab of the volume reduction section of the above template, select the policy rule according to the number of VMs, for example, according to the "automatic processing" in the template. The javascript script "MaxCount*0.05" in Strategy 2", 5% of the upper limit of the decision expansion cluster. If the cluster upper limit is 100 VMs, then 5 VMs are reduced. For example, in this embodiment, the setting of the automatic processing policy can be realized by setting the javascript script.

670. According to the “automatic processing strategy 1” in the “action” label “trigger action” in the volume reduction part of the template, the VM is deleted according to the VM instance selection policy rule, for example, “automatic processing strategy one” The strategy specified in is to remove the smallest VM of CPU utilization. That is, the CPU utilization of the VM is deleted from small to large.

675. Determine whether the capacity of the cluster after the automatic capacity adjustment meets the upper limit and the lower limit of the available resources of the cluster, and if yes, execute 680, otherwise execute 610. For example, if the capacity of the cluster exceeds the upper limit after capacity expansion, capacity expansion is not allowed. If the capacity of the cluster is lower than the lower limit after capacity reduction, volume reduction is not allowed.

680, the automation engine calls the external cloud platform to automatically adjust the cluster.

For example, in the case of capacity expansion, VM creation is performed according to the type of VM to be added (for example, the type of VM set by the user on the ServerTemplate template) and the number (for example, 5). The VM is created by calling an external driver and by an external driver call. For example, the VM can be created via AME's AddVM interface call. The AME creates the VM from the underlying layer via an external platform (for example, Galax) and joins it into the VM cluster.

In the case of volume reduction, the specified VM is removed from the VM cluster based on the list and number of VM instances to be deleted (for example, 5). The VM is deleted by calling an external driver and by an external driver call. For example, the VM can be deleted by AME's Remove VM interface, and AME deletes the VM from the underlying layer through the external Galax platform. In actual implementation, you can notify the service first and wait for a period of time before deleting the VM.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software or a combination of both, in order to clearly illustrate hardware and software. Interchangeability, the composition and steps of the various examples have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the solution. A person skilled in the art can use different methods for implementing the described functions for each particular application, but such implementation should not be considered to be beyond the scope of the present invention.

It will be apparent to those skilled in the art that, for the convenience of the description and the cleaning process, the specific operation of the system, the device and the unit described above may be referred to the corresponding processes in the foregoing method embodiments, and details are not described herein again.

In the several embodiments provided herein, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the unit is only a logical function division. In actual implementation, there may be another division manner, for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling through some interfaces, devices or units. Or a communication connection, which can be electrical, mechanical or other form.

The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solution of the embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software function unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may contribute to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium. A number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: a U disk, a removable hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and the like, which can store program codes. .

The above is only the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of changes or substitutions within the technical scope of the present invention. It should be covered by the scope of the present invention. Therefore, the scope of the invention should be determined by the scope of the claims.

Claims

Rights request

A system for processing a virtual machine in a cloud platform, comprising:

An acquisition module, configured to obtain an automation rule, where the automation rule includes an automatic processing policy; and

And a processing module, configured to generate an automatic processing parameter according to the automatic processing policy when the automatic rule is executed, and invoke the cloud platform to automatically process a group of virtual machines according to the automatic processing parameter.

The system according to claim 1, wherein the processing module comprises: a rule execution unit, configured to select an automatic processing policy when executing the automation rule, and request the automatic processing according to the selected automatic processing policy And invoking the cloud platform to automatically process the set of virtual machines according to the automatic processing parameter;

And a policy management unit, configured to manage settings of the automatic processing policy, generate the automatic processing parameter according to the setting of the selected automatic processing policy, and provide the automatic processing parameter to the rule execution unit.

The system according to claim 2, wherein the processing module further comprises: a rule management unit, configured to receive the automation rule from the obtaining module, and to add or delete the automation rule.

4. System according to claim 2 or 3, characterized in that

The processing module further includes a parameter obtaining unit, configured to acquire real-time feature parameters of the set of virtual machines, and provide the real-time feature parameters to the rule execution unit;

The rule execution unit is further configured to: when the real-time feature parameter meets a preset threshold of a trigger condition, select the automatic processing policy according to the trigger condition to trigger automatic processing on the set of virtual machines.

The system according to claim 4, wherein the system further comprises: a monitoring module, configured to monitor a first parameter of the set of virtual machines in real time, and provide the parameter to the parameter obtaining unit a parameter, wherein the parameter obtaining unit is further configured to calculate the real-time feature parameter according to the first parameter; and/or to monitor a second parameter of the set of virtual machines in real time, and to the policy management unit Providing the second parameter, wherein the policy management unit is further configured to calculate the automatic processing parameter according to the second parameter and a setting of the selected automatic processing policy by a user.

The system according to claim 4 or 5, wherein the rule execution unit comprises:

a triggering subunit, configured to select the automatic processing policy according to the triggering condition, and configured to compare the real-time feature parameter with a preset threshold of the triggering condition, where the real-time feature parameter satisfies the triggering condition When the threshold is set, the triggering call subunit performs automatic capacity adjustment on the set of virtual machines; and

The calling subunit, configured to request the policy management unit to return the automatic capacity adjustment parameter according to the selected automatic processing policy, and invoke the cloud platform to the set of virtual machines according to the automatic capacity adjustment parameter Perform automatic capacity adjustment.

The system according to claim 6, wherein the policy management unit comprises: a management subunit, configured to: according to the selected automatic processing policy included in the request from the calling subunit Identifying the request to offload the request, and/or for dynamically adding a new automatic processing policy; and

a policy subunit, configured to calculate the automatic capacity according to a setting of the selected automatic processing policy by the user and the second parameter of the set of virtual machines after receiving the request from the management subunit The parameters are adjusted and the automatic capacity adjustment parameters are returned to the calling subunit.

The system according to any one of claims 1 to 7, wherein the processing module further automatically processes the set of virtual machines according to conditions, timing or events.

The system according to any one of claims 2 to 8, wherein the automatic processing policy comprises: a policy for selecting a type of a virtual machine, a policy for selecting a number of virtual machines, and At least one of a policy of selecting an instance of a virtual machine, wherein the rule execution unit selects a policy from the type for selecting a virtual machine, the policy for selecting a number of virtual machines, and the Select at least one of the policies of the instance of the machine.

The system according to any one of claims 1 to 9, wherein the acquisition module acquires an automation rule by parsing a predetermined automation template, wherein the automation template comprises an extensible markup language file.

The system according to any one of claims 1 to 10, wherein the automation rule is set by a script or a rule engine, or is set by inputting a parameter in a popup window.

The system according to any one of claims 1 to 11, wherein the processing module invokes the internal interface to invoke the cloud platform to automatically perform the set of virtual machines. Processing, the system further includes:

And a driving module, configured to convert the calling the internal interface into an application interface that invokes the cloud platform.

The system according to any one of claims 1 to 12, wherein the processing module is further configured to allow the time interval when the time interval between two automatic processes exceeds a preset threshold A set of virtual machines is automatically processed.

The system according to any one of claims 1 to 13, wherein an upper limit and a lower limit of a capacity of the set of virtual machines are set in the automation template, and the processing module is further configured to The upper and lower limits of the available resources of the set of virtual machines determine whether automatic processing of the set of virtual machines is allowed.

A method for processing a virtual machine in a cloud platform, comprising: acquiring an automation rule, the automation rule including an automatic processing policy;

The automatic processing parameter is generated according to the automatic processing policy when the automation rule is executed, and the cloud platform is invoked to automatically process a group of virtual machines according to the automatic processing parameter.

The method according to claim 15, wherein the generating an automatic processing parameter according to the automatic processing policy when executing the automation rule, and calling the cloud platform pair according to the automatic processing parameter The virtual machine performs automatic processing, including:

Selecting an automatic processing strategy when executing the automated rules;

And generating the automatic processing parameter according to the setting of the selected automatic processing policy; invoking the cloud platform to perform automatic processing on the set of virtual machines according to the automatic processing parameter.

17. The method according to claim 16, further comprising:

Acquiring real-time feature parameters of the set of virtual machines, and triggering automatic processing on the set of virtual machines if the real-time feature parameters meet a preset threshold of a trigger condition, wherein the selecting the automatic processing strategy The method includes: selecting the automatic processing policy according to the trigger condition.

The method according to claim 17, wherein the method further comprises: monitoring a first parameter of the set of virtual machines in real time, and further calculating the real-time feature parameter according to the first parameter; and / or monitoring the second parameter of the set of virtual machines in real time, and calculating the automatic processing parameters according to the second parameter and a setting of the selected automatic processing policy by the user.

The method according to claim 17 or 18, wherein the automatic processing is automatic capacity adjustment, and the real-time characteristic parameter satisfies a preset threshold of a trigger condition. Triggering automatic processing on the set of virtual machines, including:

Comparing the preset threshold of the real-time feature parameter and the trigger condition, and triggering automatic capacity adjustment on the set of virtual machines when the real-time feature parameter meets a preset threshold of the trigger condition, according to the automatic The processing parameter invokes the cloud platform to automatically process the set of virtual machines, including:

And obtaining, according to the selected automatic processing policy, an automatic capacity adjustment parameter by using a request, and invoking the cloud platform to perform automatic capacity adjustment on the set of virtual machines according to the automatic capacity adjustment parameter.

The method according to claim 19, wherein the policy management unit comprises:

The request is offloaded according to the identifier of the selected automatic processing policy included in the request, and/or the setting for dynamically adding a new automatic processing policy.

The method according to any one of claims 15 to 20, further comprising:

Automatic processing of the set of virtual machines is triggered based on conditions, timing, or events.

The method according to any one of claims 15 to 21, wherein the automatic processing policy comprises: a policy for selecting a type of a virtual machine, a policy for selecting a number of virtual machines, and At least one of the policies for selecting an instance of the virtual machine, wherein the selecting an automatic processing policy includes:

At least one of the policies is selected from the policy for selecting a type of virtual machine, the policy for selecting a number of virtual machines, and the policy for selecting an instance of a virtual machine.

The method according to any one of claims 15 to 22, wherein the obtaining an automation rule comprises:

The automated rules are obtained by parsing a predetermined automation template, wherein the automated template includes an extensible markup language file.

The method according to any one of claims 15 to 23, characterized in that the automation rule is set by a script or a rule engine or by inputting a parameter in a popup window.

The method according to any one of claims 15 to 24, wherein the invoking the cloud platform to automatically process the set of virtual machines comprises: Invoking the cloud platform to call the cloud platform to perform automatic capacity adjustment on the set of virtual machines, the method further includes:

The internal interface is invoked to be converted into an application interface that invokes the cloud platform.

The method according to any one of claims 15 to 25, further comprising: allowing the set of virtual machines to be performed if a time interval between two automatic processes exceeds a preset threshold Perform automatic processing.

The method according to any one of claims 15 to 26, wherein the upper and lower limits of available resources of the set of virtual machines are set in the automation template, the method further comprising:

Whether to allow automatic processing of the set of virtual machines is determined based on upper and lower limits of available resources of the set of virtual machines.