CN111884826A - Elastic scaling processing method, system and device for strategy and execution of isomerization - Google Patents
Elastic scaling processing method, system and device for strategy and execution of isomerization Download PDFInfo
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Abstract
The invention provides a strategy and isomerization execution elastic scaling processing method, system and device. The method comprises setting a plurality of types of timing tasks; and when the triggering mechanism of the timing task is reached, adjusting the number of expected cloud hosts of the telescopic group based on a telescopic rule. According to the method, the timing task is set, when a triggering mechanism of the task is achieved, the number of expected cloud hosts in the telescopic group is adjusted based on the telescopic rule, the telescopic strategy and the telescopic execution process are completely isomerized, direct business exchange does not exist between the telescopic strategy and the telescopic execution process, and an indirect close relation is established only through the attribute of the number of the expected cloud hosts. And when the timing task is executed, the timing task is not influenced by other third-party tasks or operations in the system, the elastic expansion and contraction functionality is stronger, the service application is more popular and easy to understand, and the reliability is greatly improved.
Description
Technical Field
The invention relates to the technical field of cloud computing, in particular to a flexible scaling processing method, system and device for strategy and execution isomerization.
Background
The cloud computing has the characteristics of high flexibility, expandability and the like, and the virtualization capability becomes a more prominent advantage under the service type of infrastructure as a service. The user can establish various cloud components of resources such as networks, storage, calculation and the like with approximately similar service functions to corresponding entity physical components in the cloud through simple configuration, the components support specific setting, the resources can be distributed as required, the resources also have various complex service operations, and the user can flexibly realize reasonable utilization of the resources in the process of virtualizing the physical resources. Due to the existence of the characteristic, the horizontal resource allocation and recovery of the host cluster for realizing the bearing service, which is automated according to the specific execution scene and the operation needs, become no longer as complicated as the traditional mode. The design of elastic expansion is an important component in the cloud construction, the basic function of the design is to manage a group of cloud hosts, and through a certain strategy, a new cloud host is automatically expanded to the group of cloud hosts or a group of cloud hosts is released from the group of cloud hosts according to needs, so that the optimal and reasonable utilization of resources is realized.
The implementation of the elastic scaling function by each organization in the industry has different directions, but from the structural point of view, there are three essential basic parts, namely, the concept of logically managing scaling groups of a batch of cloud hosts, and the criteria for automatically creating cloud hosts, such as various cloud host configurations, triggering expansion or contraction (referred to as scaling rules, the same below). The scaling group comprises one or more scaling rules, and the scaling group comprises a complete cloud host configuration, and the theoretical components have many variations or different names, but the implementation of the theoretical components comprises corresponding mapping.
The traditional elastic expansion function is monotonous to realize, after the expansion rule of expansion or contraction is triggered, a preset number of cloud hosts are established to expand the capacity or the cloud hosts are unoccupied to contract the capacity, and the whole process forms the basic service requirement of elastic expansion. The design is a single-line flow strategy which is closely connected end to end, once a single flow is triggered, the single flow tries to finish all execution once without being interfered by other services, namely the flow is executed completely or not. However, capacity expansion or capacity reduction is a process that cannot be completed in a short time, especially when a scalable group binds load balancing, a listener, and a resource pool, execution time becomes a more time-consuming project, and in this period of time, if other scalable rules reach a trigger condition, a scalable policy to be executed by the rule and a policy being executed are an effect of mutual exclusion, and even more, if a batch of rules exist in the scalable group, the rules are respectively triggered in a certain period of time and the requested policy is a fluctuation effect with a larger amplitude, then a service dispute problem occurs in the above situation, which affects reliability of the whole system.
Disclosure of Invention
The invention provides a strategy and isomerization execution elastic scaling processing method, system and device, which are used for solving the problem that the conventional elastic scaling mechanism is easy to cause service dispute.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a strategy and elastic scaling processing method for executing isomerization, which comprises the following steps:
setting a plurality of types of timing tasks;
and when the triggering mechanism of the timing task is reached, adjusting the number of expected cloud hosts of the telescopic group based on a telescopic rule.
Further, the types of the timing tasks comprise an alarm task, a timing task and a timing inspection task; the alarm task is associated with an alarm scaling rule, and the timing task is associated with a timing scaling rule.
Further, when the triggering mechanism of the alarm type task is reached, based on the alarm type scaling rule, the specific process of adjusting the number of expected cloud hosts of the scaling group is as follows:
and monitoring the performance of system resources, triggering an alarm when the monitored data reaches a preset threshold value, and increasing or decreasing the number of the cloud hosts so as to adjust the number of the expected cloud hosts.
Further, when the triggering mechanism of the timing type task is reached, based on the timing type scaling rule, the specific process of adjusting the number of expected cloud hosts of the scaling group is as follows:
asynchronously and parallelly binding the timing type scaling rules to scaling groups;
and increasing or decreasing the number of the cloud hosts according to the expansion rule so as to adjust the number of the expected cloud hosts.
Further, the execution process of the timing check task is as follows:
traversing all the started expansion sets which are not subjected to capacity expansion or capacity contraction and exceed the cooling time, clearing abnormal cloud hosts in the expansion sets, and checking whether the number of the current actual cloud hosts is consistent with the number of the expected cloud hosts;
if the actual number of the cloud hosts is smaller than the expected number of the cloud hosts, creating the cloud hosts with the amount of difference according to the expansion configuration of the current expansion group, and moving the cloud hosts into the current expansion group;
and if the actual number of the cloud hosts is larger than the expected number of the cloud hosts, releasing the cloud hosts with the amount of difference according to the telescopic configuration of the current telescopic group, wherein the releasing sequence is that the automatically expanded cloud hosts are released firstly, and then the manually moved cloud hosts are released.
Further, if the load balancer and the monitor are bound to the scalable group, corresponding adjustment operations are performed on the load balancer and the monitor when the expected number of cloud hosts is adjusted.
Further, the configuration information of the scalable group includes the maximum number of cloud hosts and the minimum number of cloud hosts, and the number of the expected cloud hosts is adjusted within a range defined by the maximum number of cloud hosts and the minimum number of cloud hosts.
In a second aspect, the present invention provides a policy and resilient scaling processing system for performing isomerization, the system comprising:
the task setting unit is used for setting a plurality of types of timing tasks;
and the trigger execution unit is used for adjusting the number of expected cloud hosts of the telescopic group based on a telescopic rule when the trigger mechanism of the timing task is reached.
The third aspect of the present invention provides a policy and heterogeneous flexible scaling processing apparatus, where the apparatus includes a scaling group, a scaling rule, and the flexible scaling processing system, and the trigger execution unit establishes an association relationship between a timing task and the scaling rule, and when a trigger mechanism of the timing task is reached, adjusts, based on the scaling rule, the number of expected cloud hosts in the scaling group bound to the scaling rule.
A fourth aspect of the present invention provides a computer storage medium, in which computer instructions are stored, and when the computer instructions are executed on the elastic stretch processing system, the computer instructions cause the elastic stretch processing system to execute the steps of the elastic stretch processing method.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
according to the method, the timing task is set, when a triggering mechanism of the task is achieved, the number of expected cloud hosts in the telescopic group is adjusted based on the telescopic rule (rule), the telescopic strategy (rule) and the process of executing telescopic operation are completely isomerized, direct business communication does not exist between the telescopic strategy (rule) and the process of executing telescopic operation, and an indirect close relation is established only through the attribute of the number of the expected cloud hosts. And when the timing task is executed, the timing task is not influenced by other third-party tasks or operations in the system, the elastic expansion and contraction functionality is stronger, the service application is more popular and easy to understand, and the reliability is greatly improved.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow diagram of an embodiment of the method of the present invention;
fig. 3 is a schematic diagram of the system of the present invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
On the premise of having the basic elements of elastic expansion and contraction, the invention provides a novel elastic expansion and contraction execution method which is more scientific and reliable and carries out the strategy (namely the 'expansion and contraction rule' mentioned in the invention) and the execution isomerization. The method has the advantages that the method has an industry-recognized basic theory system, namely a telescopic group, a telescopic rule and a telescopic configuration, and the three systems have different application schemes in implementation, and in addition, a core timing task is added in a background to drive the execution of the whole system.
In the invention, the expansion rule supports the maintenance of alarm class and timing class, and meets various expansion means applied in different operation scenes.
The alarm type expansion rule is based on monitoring of service resources in an operating environment, the elastic expansion can trigger an expansion strategy configured under the expansion rule by taking monitoring data as a basis, so that the resources are used, the resources are reasonably utilized after the expansion is executed, and the monitoring data can be kept at the optimal level. In the type, the whole telescopic process is completely in a telescopic back wave and monitoring feedback, the monitoring feedback influences the closed running chain of telescopic execution, and the full-system automatic design enables various systems running in the system to efficiently process overload and resource surplus under the condition of separating from manual intervention, and automatically optimizes resources to provide services for the outside with the running effect of the optimal resource utilization rate.
The timing type telescopic rule enables the elastic telescopic device to execute a set telescopic strategy at a fixed time point, can make up the perceived surface defect after the telescopic device is executed, allows a telescopic group to master the initiative of resource integration, can automatically execute the elastic telescopic device according to the rule after a set time without considering the monitoring result. Many application systems always have relatively regular operation characteristics at a fixed time point in a certain time period, and the application shows the change degree of resource consumption and equipment dependence which tend to be consistent from the moment to the moment, or needs to be increased or reduced, or can achieve the operation effect of the optimal resource utilization rate only by a fixed amount of cloud host resources.
Through the elastic characteristic of the cloud host service, the alarm class and timing class tasks are customized, the cloud host examples are automatically increased when the service is increased, the cloud host examples are automatically reduced when the service is decreased, and the high-availability architecture is realized by matching with load balance.
The function of the scaling configuration is relatively unique, and the scaling configuration is an indispensable scale for defining what components, what performances and specifications should be provided for the extended cloud host, and the components of the scaling configuration can be flexibly designed according to the cloud host configuration supported by the cloud computing platform. Because the types of cloud computing platforms are complicated and cannot be generalized, the flexible configuration is used as a part of the theoretical system, cannot be abstracted in design, and should be closely combined with the actual system to achieve complete and scientific flexible configuration. Conventionally, cloud host configuration includes, but is not limited to, mirroring, cloud host specification, network, and the like, and through good design, the scaling configuration will be a component with higher complexity in elastic scaling as an element component directly influencing the performance and configuration of the created cloud host, and this category will become a decisive constituent of the service quality of the scaling group. Depending on which cloud computing platform is to be utilized, the idea advocates that the practical business close to the specification is maximized, and all the various cloud host configuration parameters which are closely related to the specific needs and supported by the cloud computing are put into a telescopic configuration, so that the function optimization is realized. The design of the telescopic configuration in this patent is not a focus, and the design can be made with reference to the prior art.
The flexible groups are an assembly logic which integrates more resources and has certain macroscopic characteristics, form a set of system, are basic units of elastic flexibility, are independently and parallelly executed in elastic capacity expansion, elastic capacity contraction and elastic self-healing activities, do not need sensing and influence each other, directly perform respective control on cloud hosts inside the flexible groups through the three activities, and show automatic resource allocation to the outside.
The flexible group comprises basic information, flexible characteristics, a group of flexible rules, a group of managed cloud hosts, flexible activities, flexible history, operation logs and resource auditing. The basic information mainly includes individual information that the various kinds of the drawing telescopic groups themselves have, such as the organization, name, and description. The telescopic characteristics comprise expected cloud host number, maximum cloud host number, minimum cloud host number, load balance, a listener, cloud host reduction strategies, cooling time and other various kinds of customized telescopic characteristics. The number of cloud hosts is expected to play an important role in the paradigm, and this field assumes the role of a hub, whose existence is the root for implementing policies and performing isomerization.
The rules in the scalability groups are alarm class and timing class rules that are thermally configurable, and no matter what state the scalability groups are in, new rules can be moved in or existing rules can be deleted from them. The cloud hosts moved into the scalable group are generally a group of cloud hosts providing common services to the outside in a common scene, and the sources of the cloud hosts are mainly four, namely: if load balancing and a monitor are bound when a telescopic group is created, all internal members of a resource pool in the monitor need to be loaded to the telescopic group; directly and manually moving the cloud host which is already established and normally runs into the telescopic group; under the condition of binding the load balancing and the monitor, synchronously moving the cloud host into the resource pool of the monitor and then moving the cloud host into the bound telescopic group; and after triggering automatic elastic expansion, creating a new cloud host according to the telescopic configuration and then automatically moving into the telescopic group.
The expansion and contraction activities are used for recording all intentions of all rules in the current expansion and contraction group when triggered, and are specifically represented as the changing process of the expected number of the cloud hosts during expansion and contraction. The expansion history records specific execution activities, and sequentially records expansion processes of all cloud hosts by taking the cloud hosts as a center and taking time as an axis, wherein the expansion processes comprise expansion and contraction conditions, success or failure results, specific execution descriptions and the like of the cloud hosts. The operation log and the resource audit are specific content records when the administrator user operates the telescopic group, the operation log records the user operation content, and the resource audit records the data change condition, so that the trace can be followed and found.
In addition to the several key elements set forth above, the elastic scaling architecture also includes several types of timing tasks, which are the engines on which elastic scaling relies for external services and are also an important component of the present invention.
The first type of timing task is a task to which an alarm type expansion rule is attached, the alarm type expansion rule comprises a trigger condition set based on performance monitoring of various system resources such as a CPU (central processing unit), a memory and the like and a corresponding capacity expansion or capacity reduction quantity, and the influence of the expansion rule on the expansion group is realized by utilizing a binding relationship between the trigger condition and the capacity expansion or capacity reduction quantity, calling monitoring data through the timing task and calculating an execution result and then correspondingly adjusting a specific value of the number of expected cloud hosts in the expansion group.
The second type of timing task is a task to which the timing type scaling rule is attached, and mainly comprises a repetition period (once, daily, weekly and monthly), an effective time range, a specific triggering date and time point, an adjusting mode and an adjusting quantity besides some self attributes and organization belonging information. And when a timing rule is created, a timing task is immediately and correspondingly generated, and the timing task scans all the bound telescopic groups once the timing task is executed, and changes the expected number of the cloud hosts in the telescopic groups in parallel according to the capacity expansion or capacity reduction strategy configured in the rule.
The tasks of the two types are only associated with the rules, the expected number of the cloud hosts of the telescopic group is only changed after the conditions are met, whether the capacity of the cloud hosts is really expanded or reduced is not involved, and the steps are skillfully and independently distributed to be executed by the customized independent timing tasks. The newly customized timing task service is completely and independently differentiated, and only concerns whether the number of expected cloud hosts in the telescopic group is equal to the number of actual cloud hosts in the telescopic group.
The third type of timing task is created based on elastic self-healing, if abnormal cloud hosts exist in the telescopic group, the telescopic group can be subjected to elastic self-healing to clear abnormal data and re-create new cloud hosts for replacement, when the number of the cloud hosts is expected to be larger than the number of the actual cloud hosts, the difference number of the cloud hosts can be created and moved into the telescopic group, and if the telescopic group is bound with the load balancing and the monitor, the telescopic group can be synchronously moved into a resource pool; when the expected number of the cloud hosts is smaller than the actual number of the cloud hosts, screening out a part of cloud hosts from the telescopic group according to the set reduction strategy to release the cloud hosts, and if the telescopic group is bound with the load balancing and the monitor, removing corresponding data from a resource pool in the telescopic group.
The following description is made with reference to the accompanying drawings.
As shown in fig. 1, the method for processing elastic scalability by policy and execution of isomerization in the present invention includes the following steps:
s1, setting a plurality of types of timing tasks;
and S2, when the trigger mechanism of the timing task is reached, adjusting the number of expected cloud hosts of the telescopic group based on the telescopic rule.
The types of the timing tasks in the step S1 include an alarm task, a timing task, and a timing inspection task; the alarm task is associated with an alarm scaling rule, and the timing task is associated with a timing scaling rule.
As shown in fig. 2, in step S2, when the trigger mechanism of the alarm type task is reached, based on the alarm type scaling rule, the specific process of adjusting the expected number of cloud hosts in the scaling group is as follows: and monitoring the performance of system resources, triggering an alarm when the monitored data reaches a preset threshold value, and increasing or decreasing the number of the cloud hosts so as to adjust the number of the expected cloud hosts. The preset threshold value is set according to the actual performance level requirements of system resources, for example, the utilization rate of the CPU is monitored, when the utilization rate is lower than 30%, the number of the cloud hosts is reduced, and when the utilization rate is higher than 80%, the number of the cloud hosts is increased. Wherein 30% and 80% of the CPU usage are respectively used as preset thresholds.
When the triggering mechanism of the timing task is reached, based on the timing scaling rule, the specific process of adjusting the expected number of cloud hosts in a scaling group is as follows: asynchronously and parallelly binding the timing type scaling rules to scaling groups; and increasing or decreasing the number of the cloud hosts according to the expansion rule so as to adjust the number of the expected cloud hosts.
The execution process of the timing check task comprises the following steps: traversing all the started expansion sets which are not subjected to capacity expansion or capacity contraction and exceed the cooling time, clearing abnormal cloud hosts in the expansion sets, and checking whether the number of the current actual cloud hosts is consistent with the number of the expected cloud hosts; if the actual number of the cloud hosts is smaller than the expected number of the cloud hosts, creating the cloud hosts with the amount of difference according to the expansion configuration of the current expansion group, and moving the cloud hosts into the current expansion group; and if the actual number of the cloud hosts is larger than the expected number of the cloud hosts, releasing the cloud hosts with the amount of difference according to the telescopic configuration of the current telescopic group, wherein the releasing sequence is that the automatically expanded cloud hosts are released firstly, and then the manually moved cloud hosts are released.
The execution process of the three types of timing tasks is not influenced by the third-party task or operation in the current system, once triggered, the responsibility service is immediately executed, and the detailed telescopic activities are recorded for the user to read.
And if the load balancer and the monitor are bound to the telescopic group, carrying out corresponding adjustment operation on the load balancer and the monitor when the expected number of the cloud hosts is adjusted.
The configuration information of the telescopic group comprises the number of the maximum cloud hosts and the number of the minimum cloud hosts, and the number of the expected cloud hosts is adjusted within a range limited by the number of the maximum cloud hosts and the number of the minimum cloud hosts.
The processing method further comprises the operation of the cloud host by the user, and specifically comprises the following steps: when a user moves in a cloud host to a telescopic group or moves in an internal cloud host instance member to a resource pool in a bound monitor, the number of expected cloud hosts is increased by corresponding number on the basis of the number of the originally expected cloud hosts, and the number is capped to the maximum number of the cloud hosts; when a user deletes a cloud host from a telescopic group or removes an internal cloud host instance member from a resource pool in a bound listener, the expected number of cloud hosts is reduced on the basis of the original expected number of cloud hosts, and the lower limit is the minimum number of cloud hosts.
As shown in fig. 3, the flexible scaling processing system for policy and execution of heterogeneous applications of the present invention includes a task setting unit 1 and a trigger execution unit 2.
The task setting unit 1 is used for setting a plurality of types of timing tasks; and when the trigger execution unit 2 reaches the trigger mechanism of the timing task, adjusting the number of expected cloud hosts of the telescopic group based on a telescopic rule.
The elastic stretching processing system in this embodiment can realize each step of the elastic stretching processing method and achieve the same technical effect.
The invention also provides an elastic expansion processing device for strategy and execution isomerization, which comprises an expansion group, an expansion rule and an elastic expansion processing system, wherein the trigger execution unit establishes the association relationship between the timing task and the expansion rule, and when the trigger mechanism of the timing task is reached, the expected number of cloud hosts in the expansion group bound with the expansion rule is adjusted based on the expansion rule.
The invention also provides a computer storage medium, wherein the computer storage medium stores computer instructions, and when the computer instructions run on the elastic stretching processing system, the elastic stretching processing system executes the steps of the elastic stretching processing method.
The invention carries out a forward change on the execution flow of elastic expansion, compared with the original thought, the scheme has very outstanding advantages in practicability and flow scientificity, standingly and fully utilizes the modularization thought, divides and treats the whole business, and makes full use of the division thought regardless of various large theoretical or actual components contained under the elastic expansion or the content on the flow of processing the whole business in the background. The business solution scheme with clear thinking avoids the problem of expansion conflict with high possibility and occurrence rate, the number of expected cloud hosts is arbitrarily changed after the requirements of expansion or capacity reduction are triggered by two types of rules, the two types of rules cannot involve the actual operation of the cloud hosts, and the expansion or capacity reduction requirements of all the rules cannot be abandoned without end and are very easy to realize. The process of really executing the elastic self-healing, the capacity expansion or the capacity reduction is given to the task of regularly checking the number of expected cloud hosts, and the accompanying solution does not generate larger service delay, and simultaneously establishes a logically definite precedence relationship with other services to a certain extent, thereby solving the problems that the original process is over-deployed, the scheme is complicated, and the subsequent services cannot be executed due to the problem in one link. The expansion strategy and the expansion execution process are completely isomerized, direct business communication does not exist between the expansion strategy and the expansion execution process, an indirect close relation is established only through the attribute of the expected number of the cloud hosts, the expected number of the cloud hosts is an important pivot, and the key point for realizing business isomerization is achieved. After the scheme is implemented, the elastic expansion and contraction functionality is stronger, the service application is more popular and easy to understand, and the reliability is greatly improved.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A flexible expansion processing method for strategy and execution isomerization is characterized by comprising the following steps:
setting a plurality of types of timing tasks;
and when the triggering mechanism of the timing task is reached, adjusting the number of expected cloud hosts of the telescopic group based on a telescopic rule.
2. The policy and resilient scaling method for performing isomerization according to claim 1, wherein the types of the timing task include an alarm-like task, a timing-like task, and a timing check task; the alarm task is associated with an alarm scaling rule, and the timing task is associated with a timing scaling rule.
3. The policy and resilient scaling processing method for performing isomerization according to claim 2, wherein when the triggering mechanism of the alert class task is reached, the specific process of adjusting the expected number of cloud hosts in a scaling group based on the alert class scaling rule is as follows:
and monitoring the performance of system resources, triggering an alarm when the monitored data reaches a preset threshold value, and increasing or decreasing the number of the cloud hosts so as to adjust the number of the expected cloud hosts.
4. The policy and resilient scaling processing method for performing isomerization according to claim 2, wherein when the trigger mechanism of the timing class task is reached, based on the timing class scaling rule, the specific process of adjusting the expected number of cloud hosts in the scaling group is as follows:
asynchronously and parallelly binding the timing type scaling rules to scaling groups;
and increasing or decreasing the number of the cloud hosts according to the expansion rule so as to adjust the number of the expected cloud hosts.
5. The method for processing the flexible scaling of the strategy and execution of the isomerization according to claim 2, wherein the execution process of the timing check task is as follows:
traversing all the started expansion sets which are not subjected to capacity expansion or capacity contraction and exceed the cooling time, clearing abnormal cloud hosts in the expansion sets, and checking whether the number of the current actual cloud hosts is consistent with the number of the expected cloud hosts;
if the actual number of the cloud hosts is smaller than the expected number of the cloud hosts, creating the cloud hosts with the amount of difference according to the expansion configuration of the current expansion group, and moving the cloud hosts into the current expansion group;
and if the actual number of the cloud hosts is larger than the expected number of the cloud hosts, releasing the cloud hosts with the amount of difference according to the telescopic configuration of the current telescopic group, wherein the releasing sequence is that the automatically expanded cloud hosts are released firstly, and then the manually moved cloud hosts are released.
6. The method for resilient scaling processing of policies and execution isomerization according to any one of claims 1 to 5, wherein if a load balancer and a listener are bound to the scaling group, when adjusting the expected number of cloud hosts, the load balancer and the listener are correspondingly adjusted.
7. The method for processing flexible scalability according to any of claims 1-5, wherein the configuration information of the scalable group includes a maximum number of cloud hosts and a minimum number of cloud hosts, and the number of desired cloud hosts is adjusted within a range defined by the maximum number of cloud hosts and the minimum number of cloud hosts.
8. A system for policy and resilient scaling processing for performing heterogeneous processes, the system comprising:
the task setting unit is used for setting a plurality of types of timing tasks;
and the trigger execution unit is used for adjusting the number of expected cloud hosts of the telescopic group based on a telescopic rule when the trigger mechanism of the timing task is reached.
9. An elastic scaling processing device for strategy and execution of isomerization, characterized in that the device comprises a scaling group, scaling rules and the elastic scaling processing system of claim 8, the trigger execution unit establishes the association relationship between the timing task and the scaling rules, and when the trigger mechanism of the timing task is reached, the expected number of cloud hosts in the scaling group bound with the scaling rules is adjusted based on the scaling rules.
10. A computer storage medium having computer instructions stored thereon, which, when run on the elastic stretch processing system of claim 8, cause the elastic stretch processing system to perform the steps of the elastic stretch processing method of any one of claims 1 to 5.
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