CN114189439A - Automatic capacity expansion method and device - Google Patents
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Abstract
The invention discloses a method and a device for automatically expanding capacity, wherein the method comprises the following steps: receiving abnormal information of an abnormal instance, wherein the abnormal information at least comprises an instance IP, an identifier of a subsystem corresponding to the instance, current capacity and a prediction threshold; acquiring configuration information and release information corresponding to the abnormal example based on the example IP and the identifier of the subsystem, and determining capacity expansion configuration information corresponding to the abnormal example based on the configuration information, the release information, the current capacity and a prediction threshold; based on the capacity expansion configuration information and a preset establishment rule, establishing a capacity expansion flow comprising a plurality of subtasks; each subtask has a superior-inferior dependency relationship with its adjacent subtask; and executing an expansion flow comprising a plurality of subtasks based on the upper and lower level dependency relationship, acquiring expansion resources corresponding to the abnormal instances, and sending the expansion resources to the equipment corresponding to the abnormal instances. The method can automatically create and execute the capacity expansion flow according to the capacity expansion configuration information, reduces the manual confirmation steps and improves the capacity expansion efficiency.
Description
Technical Field
The embodiment of the invention relates to the field of financial technology (Fintech), in particular to a method and a device for automatically expanding capacity.
Background
With the development of computer technology, more and more technologies are applied in the financial field, and the traditional financial industry is gradually changing to financial technology, but due to the requirements of the financial industry on safety and real-time performance, higher requirements are also put forward on the technologies. Specifically, with the continuous development of various services, the capacity of the service corresponding to the service needs to be expanded.
However, in the prior art, when performing capacity expansion processing, it is generally determined whether capacity expansion needs to be performed based on a predetermined threshold or manually by operation and maintenance personnel, that is, capacity expansion requirements cannot be timely and accurately found and determined. In addition, the flow of resource application and deployment application is split, the completion of the previous step needs to be waited for and the manual timing confirmation needs to be carried out, and the resource information synchronization is carried out through a timing rule, so that the time-efficiency requirement is difficult to meet in an emergency.
Therefore, when the capacity expansion processing is performed in the prior art, the manual participation of the whole processing flow is too high, and the capacity expansion step is complicated, so that the capacity expansion efficiency is low.
Disclosure of Invention
The invention provides an automatic capacity expansion method and device, which are used for solving the problems that in the prior art, the whole processing flow has overhigh manual participation and the capacity expansion step is complicated, so that the capacity expansion efficiency is low.
In a first aspect, the present invention provides a method for automatic capacity expansion, the method comprising: receiving abnormal information of an abnormal instance, wherein the abnormal information at least comprises an instance IP, an identifier of a subsystem corresponding to the instance, current capacity and a prediction threshold; acquiring configuration information and release information corresponding to the abnormal instance based on the instance IP and the identifier of the subsystem, and determining capacity expansion configuration information corresponding to the abnormal instance based on the configuration information, the release information, the current capacity and a prediction threshold; based on the capacity expansion configuration information and a preset establishment rule, establishing a capacity expansion flow comprising a plurality of subtasks; each subtask has a superior-subordinate dependency relationship with its adjacent subtask; and executing the capacity expansion flow comprising the plurality of subtasks based on the upper and lower level dependency relationship, acquiring capacity expansion resources corresponding to the abnormal example, and sending the capacity expansion resources to equipment corresponding to the abnormal example.
Based on the method, the abnormal information can be automatically received, a user does not need to actively check whether the subsystem needs capacity expansion, and operation steps are reduced. In addition, according to the capacity expansion configuration information, a full-flow adaptive capacity expansion flow can be automatically created, the execution condition of each capacity expansion node, namely a subtask, can be automatically confirmed, the time and steps of flow transmission and manual confirmation are reduced, and the capacity expansion efficiency is improved.
Optionally, the abnormal information is determined based on the following manner: determining historical capacity data corresponding to the subsystem, and fitting a regression equation based on the historical capacity data; predicting a prediction threshold corresponding to the subsystem based on the regression equation; wherein the prediction threshold is the sum of an upper limit value and a preset value of the historical capacity data; and triggering the abnormal information when the current capacity corresponding to the subsystem is determined to be not less than the prediction threshold.
Based on the method, the predicted capacity can be updated based on the updated historical capacity data, namely the predicted capacity is not a fixed and unchangeable value, so that the to-be-expanded example, namely the abnormal example, can be determined more accurately and efficiently, active checking by a user is not needed, and the operation steps of the expansion process are reduced.
Optionally, based on the expansion configuration information and the preset creation rule, a capacity expansion flow including a plurality of subtasks is created, including: determining the requirements of a plurality of subtasks to be created based on the capacity expansion configuration information; determining setting information corresponding to each subtask based on the requirements of the plurality of subtasks to be created, wherein the setting information at least comprises an operation type supporting operation, and input parameters and output parameters corresponding to the executed subtasks, and the input parameters are provided based on the subtasks of the upper stage corresponding to the subtasks; and creating an expansion flow comprising a plurality of subtasks based on the setting information and a preset creation rule.
Based on the method, the requirements of the plurality of subtasks to be created can be determined based on the capacity expansion configuration information, so that the setting information corresponding to each subtask can be determined based on the requirements of the plurality of subtasks to be created, that is, the input parameter and the output parameter required by each subtask can be determined, and because the subsequent input parameter is provided by the previous-level subtask corresponding to the subtask, the capacity expansion flow which can be automatically executed in sequence by each subtask can be created based on the setting information corresponding to each subtask and the preset creation rule, so that the capacity expansion flow which does not need to be manually confirmed by a user can be simply and efficiently created, and the intelligence of the capacity expansion flow is enhanced.
Optionally, the plurality of subtasks at least include: the method comprises a capacity expansion configuration inquiry subtask, a resource application subtask, a resource information synchronization subtask and a service release subtask.
Based on the plurality of subtasks, the automatic capacity expansion process can be completely and efficiently realized, the user operation steps are reduced, and the capacity expansion efficiency is improved.
Optionally, executing the capacity expansion flow including the multiple subtasks, and acquiring the capacity expansion resource corresponding to the abnormal instance includes: executing a capacity expansion configuration query subtask in the capacity expansion flow, and sending resource template checking information to a cloud management platform when capacity expansion resources are determined to be virtual machine resources; the resource template checking information at least comprises parameters and attribute information corresponding to the host; after receiving the task identifier sent by the cloud management platform, sending an acquisition request of capacity expansion resources to the cloud computing platform based on the task identifier; the task identifier is fed back after the cloud management platform passes the verification of the parameter information and the attribute information corresponding to the host; and receiving the network resources fed back by the cloud computing platform, and taking the network resources as capacity expansion resources corresponding to the abnormal instances.
Based on the method, the resource when the capacity expansion resource is the virtual machine resource can be determined efficiently, a good implementation basis is provided for the execution of the subsequent capacity expansion process, and the execution of the capacity expansion process is accelerated.
Optionally, the network resource is determined by the cloud computing platform based on the resource template acquisition request and the resource after the initialization of the created resource is allocated; the method comprises the steps of establishing a shared storage pool, initializing the established resources, and performing polling check on the established resources based on a preset time interval.
In the method, the POD architecture is adopted to allocate resources during resource allocation, namely, the resource allocation efficiency is optimized, the time for copying the resources is reduced, the serial initialization of the previous components is optimized to be parallel, the resource initialization time is greatly improved, and the efficiency for acquiring the capacity expansion resources corresponding to the abnormal instances is improved.
Optionally, executing the capacity expansion flow including the multiple subtasks, and acquiring the capacity expansion resource corresponding to the abnormal instance includes: executing a capacity expansion configuration query subtask in the capacity expansion flow, and sending the capacity expansion configuration information to a general container platform when capacity expansion resources are determined to be container resources; and receiving the expansion configuration information of the general container platform, screening the resources meeting the high availability principle from the corresponding resource pool, and taking the resources as the expansion resources corresponding to the abnormal instances.
Based on the method, the resource when the capacity expansion resource is the container resource can be determined efficiently, a good implementation basis is provided for the execution of the subsequent capacity expansion process, and the execution of the capacity expansion process is accelerated.
Optionally, after obtaining the capacity expansion resource corresponding to the abnormal instance, the method further includes: triggering the capacity expansion resources to be automatically synchronized to a configuration information system and an application release system so that the application release system automatically issues a release plug-in through an automatic operation platform script; and sending a confirmation issuing request, and triggering the automatic operation platform to deploy application after receiving confirmation issuing information so as to complete the expansion of the abnormal instance.
Based on the method, the information of the expansion case can be preferentially and automatically pulled for the application release system, whether the case is available or not can be actively detected, the plug-in is automatically issued, the release efficiency is improved, the execution of the expansion flow is accelerated, and the expansion efficiency is improved.
In a second aspect, the present invention provides an automatic capacity expansion device, where the device includes: the system comprises a receiving unit, a judging unit and a judging unit, wherein the receiving unit is used for receiving abnormal information of an abnormal instance, and the abnormal information at least comprises an instance IP, an identifier of a subsystem corresponding to the instance, a current capacity and a prediction threshold; a determining unit, configured to obtain configuration information and release information corresponding to the abnormal instance based on the instance IP and the identifier of the subsystem, and determine capacity expansion configuration information corresponding to the abnormal instance based on the configuration information, the release information, the current capacity, and a prediction threshold; the creating unit is used for creating a capacity expansion flow comprising a plurality of subtasks based on the capacity expansion configuration information and a preset creating rule; each subtask has a superior-subordinate dependency relationship with its adjacent subtask; and the first processing unit is used for executing the capacity expansion flow comprising the plurality of subtasks based on the upper-level and lower-level dependency relationships, acquiring capacity expansion resources corresponding to the abnormal instances, and sending the capacity expansion resources to the equipment corresponding to the abnormal instances.
Optionally, the abnormal information is determined based on the following manner: determining historical capacity data corresponding to the subsystem, and fitting a regression equation based on the historical capacity data; predicting a prediction threshold corresponding to the subsystem based on the regression equation; wherein the prediction threshold is the sum of an upper limit value and a preset value of the historical capacity data; and triggering the abnormal information when the current capacity corresponding to the subsystem is determined to be not less than the prediction threshold.
Optionally, the creating unit is specifically configured to: determining the requirements of a plurality of subtasks to be created based on the capacity expansion configuration information; determining setting information corresponding to each subtask based on the requirements of the plurality of subtasks to be created, wherein the setting information at least comprises an operation type supporting operation, and input parameters and output parameters corresponding to the executed subtasks, and the input parameters are provided based on the subtasks of the upper stage corresponding to the subtasks; and creating an expansion flow comprising a plurality of subtasks based on the setting information and a preset creation rule.
Optionally, the plurality of subtasks at least include: the method comprises a capacity expansion configuration inquiry subtask, a resource application subtask, a resource information synchronization subtask and a service release subtask.
Optionally, the first processing unit is specifically configured to: executing a capacity expansion configuration query subtask in the capacity expansion flow, and sending resource template checking information to a cloud management platform when capacity expansion resources are determined to be virtual machine resources; the resource template checking information at least comprises parameters and attribute information corresponding to the host; after receiving the task identifier sent by the cloud management platform, sending an acquisition request of capacity expansion resources to the cloud computing platform based on the task identifier; the task identifier is fed back after the cloud management platform passes the verification of the parameter information and the attribute information corresponding to the host; and receiving the network resources fed back by the cloud computing platform, and taking the network resources as capacity expansion resources corresponding to the abnormal instances.
Optionally, the network resource is determined by the cloud computing platform based on the resource template acquisition request and the resource after the initialization of the created resource is allocated; the method comprises the steps of establishing a shared storage pool, initializing the established resources, and performing polling check on the established resources based on a preset time interval.
Optionally, the first processing unit is specifically configured to: executing a capacity expansion configuration query subtask in the capacity expansion flow, and sending the capacity expansion configuration information to a general container platform when capacity expansion resources are determined to be container resources; and receiving the expansion configuration information of the general container platform, screening the resources meeting the high availability principle from the corresponding resource pool, and taking the resources as the expansion resources corresponding to the abnormal instances.
Optionally, the apparatus further includes a second processing unit, configured to: triggering the capacity expansion resources to be automatically synchronized to a configuration information system and an application release system so that the application release system automatically issues a release plug-in through an automatic operation platform script; and sending a confirmation issuing request, and triggering the automatic operation platform to deploy application after receiving confirmation issuing information so as to complete the expansion of the abnormal instance.
The advantageous effects of the second aspect and the various optional apparatuses of the second aspect may refer to the advantageous effects of the first aspect and the various optional methods of the first aspect, and are not described herein again.
In a third aspect, the present invention provides a computer device comprising a program or instructions for performing the method of the first aspect and the alternatives of the first aspect when the program or instructions are executed.
In a fourth aspect, the present invention provides a storage medium comprising a program or instructions which, when executed, is adapted to perform the method of the first aspect and the alternatives of the first aspect.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings that are required to be used in the description of the embodiments will be briefly described below.
Fig. 1 is a schematic diagram of a capacity expansion system according to an embodiment of the present invention;
fig. 2 is a schematic flowchart illustrating steps of a method for automatically expanding capacity according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an automatic capacity expansion device according to an embodiment of the present invention.
Detailed Description
In order to better understand the technical solutions, the technical solutions will be described in detail below with reference to the drawings and the specific embodiments of the specification, and it should be understood that the embodiments and specific features of the embodiments of the present invention are detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features of the embodiments and examples of the present invention may be combined with each other without conflict.
It is noted that the terms first, second and the like in the description and in the claims of the present invention are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the images so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The following briefly introduces the design concept of the embodiment of the present invention:
at present, in a conventional service capacity expansion process, a user generally performs capacity expansion manually. Specifically, the manual capacity expansion of the user is probably the following flow: firstly, a user can check current system indexes and alarms, when capacity expansion is determined to be needed, the subsystem needing capacity expansion and a physical area where the subsystem needs capacity expansion are confirmed, then resource Configuration information corresponding to the subsystem needing capacity expansion is checked in a Configuration Management Database (CMDB), and the quantity and the type of resources required to be applied are determined. Then, the user can go to the host resource management platform or the container resource management platform to fill in the resource application form according to the type of the resource required to be applied, and submit the approval. After the approval is passed, the user can obtain a resource Internet Protocol (IP) through the configuration management platform or the resource management platform. After the user confirms that the resource synchronization is ready, the user can prepare before releasing the expanded capacity resource on an Automated Operation and Maintenance Platform (AOMP), and when the preparation is determined to be completed, the user performs the releasing operation, and when the verification function and the indicator are normal after the application is released, the user confirms that the expansion is completed.
It can be seen that in the capacity expansion process in the prior art, the processes of resource application and deployment application are driven by requirements, and need to wait for the completion of the previous step and manual timing confirmation, and resource information synchronization and resource readiness are executed by a timing rule, and in an emergency, it is difficult to meet the resource required by time efficiency.
In view of this, embodiments of the present invention provide an automatic capacity expansion method, by which whether capacity is abnormal or not can be actively predicted, so that an example with a capacity expansion requirement can be early warned in advance, and an application warning policy does not need to be configured by a user, that is, user operation steps are reduced. In addition, the capacity expansion process can be automatically established according to the automatically determined capacity expansion configuration information, the execution condition of each capacity expansion node is automatically confirmed in the capacity expansion process, the steps of process transmission and manual confirmation are reduced, and the capacity expansion process is improved.
After the design idea of the embodiment of the present invention is introduced, some brief descriptions are made below on a system corresponding to the automatic capacity expansion technical scheme in the embodiment of the present invention, it should be noted that the system described in the embodiment of the present invention is for more clearly describing the technical scheme of the embodiment of the present invention, and does not form a limitation on the technical scheme provided in the embodiment of the present invention, and it is known by those skilled in the art that the technical scheme provided in the embodiment of the present invention is also applicable to similar technical problems.
Referring to fig. 1, the capacity expansion System in the embodiment of the present invention includes an automation Operation platform (Smart Operation System, Smart ops), an intelligent prediction System, a configuration management platform, an automation Operation and maintenance platform, a general container platform, a cloud management platform, a cloud computing platform, and an application release platform. The automatic operation platform is connected with the intelligent prediction system, the configuration management platform, the automatic operation and maintenance platform, the general container platform, the cloud management platform, the cloud computing platform and the application release platform. It should be noted that, the respective platforms or the platform and the system are directly or indirectly connected through, for example, wired or wireless communication, and the present invention is not limited thereto.
Specifically, the intelligent prediction system is used for predicting whether each subsystem of the application program needs capacity expansion; the configuration management platform is used for inquiring various configuration information corresponding to the subsystem, such as node type, memory size, disk capacity and other information; the universal container resource is used for determining the container resource required by application and distribution capacity expansion; the automatic operation and maintenance platform is used for acquiring the applied capacity expansion resources and performing subsequent processing based on the resources; the cloud management platform is used for verifying the resource template; the cloud computing platform is used for determining host resources required by the applied and distributed expansion; and the application publishing platform is used for confirming the unique identifier used in the publishing request, and whether the version of the application and the publishing template are accurate.
In a specific implementation process, the automation operation platform may cooperate with each platform or system in the capacity expansion system to implement automatic capacity expansion.
To further explain the scheme of the automatic capacity expansion method provided by the embodiment of the present invention, details are described below with reference to the accompanying drawings and the specific embodiments. Although embodiments of the present invention provide method steps as shown in the following embodiments or figures, more or fewer steps may be included in the method based on conventional or non-inventive efforts. In steps where no necessary causal relationship exists logically, the order of execution of the steps is not limited to that provided by embodiments of the present invention. The method can be executed in sequence or in parallel according to the method shown in the embodiment or the figures when the method is executed in an actual processing procedure or a device (for example, a parallel processor or an application environment of multi-thread processing).
The method for automatically expanding the capacity in the embodiment of the present invention is described below with reference to the method flowchart illustrated in fig. 2, and the method flow in the embodiment of the present invention is described below.
Step 201: and receiving abnormal information of the abnormal instance, wherein the abnormal information at least comprises an instance IP, an identifier of a subsystem corresponding to the instance, the current capacity and a prediction threshold value.
In the embodiment of the invention, the intelligent prediction system can predict the capacity of each subsystem corresponding to the application program, namely, determine whether the capacity value of the subsystem reaches or approaches the prediction threshold value, thereby determining whether each subsystem needs capacity expansion. Wherein the prediction threshold is determined based on a linear regression process performed on the updated historical capacity data.
In the embodiment of the present invention, the abnormality information may be determined based on the following manner: determining historical capacity data corresponding to the subsystem, and fitting a regression equation based on the historical capacity data; predicting a corresponding prediction threshold value of the subsystem based on the regression equation; the prediction threshold is the sum of an upper limit value of historical capacity data and a preset value; and triggering abnormal information when the current capacity corresponding to the subsystem is not smaller than the prediction threshold value.
Specifically, the regression equation is fitted to the historical capacity data, then the prediction threshold of the next stage is predicted, and the prediction threshold is set to be higher than the upper limit value of the historical capacity data by a preset value, for example, 20%, where the preset value may be determined based on actual implementation, and is not limited in the embodiment of the present invention. Further, the intelligent prediction system may determine the alarm information corresponding to exceeding the upper limit and the instance IP of the specific instance according to whether the prediction threshold is exceeded.
Illustratively, the manner of determining the prediction threshold, i.e., the prediction capacity, is: assuming that Y is used to represent the predicted capacity, Y is used to represent the current capacity, and x is used to represent the maximum capacity value of k days before, n independent observations of Y and x1, x2, and xk are made simultaneously to obtain n sets of observations (xt1, xt2, and xtk), t1, 2, and n (n > k +1)
Setting y and x to satisfy the following relationship:
y＝β0+β1xt1+β2xt2+…+βkxtk+εt
then, a solution is obtained based on a least square method:
the predicted capacity can thus be determined as: y ═ X β + epsilon.
Further, based on the above relation for determining the predicted capacity, the predicted capacity Y may be determined, and when { Y (current capacity) -Y (predicted capacity) }/Y (current capacity) > is a preset value, it is determined that the instance corresponding to the current subsystem is an abnormal instance, and abnormal information of the abnormal instance is determined, where the abnormal information at least includes an instance IP, an identifier of the subsystem corresponding to the instance, the current capacity, and a prediction threshold, so that the abnormal information is sent to the automation operation platform, and the automation operation platform may receive the abnormal information.
Step 202: and acquiring configuration information and release information corresponding to the abnormal instance based on the instance IP and the identifier of the subsystem, and determining expansion configuration information corresponding to the abnormal instance based on the configuration information, the release information, the current capacity and the prediction threshold.
In the embodiment of the present invention, after receiving the abnormal information sent by the intelligent prediction system, the automation operation platform may send confirmation information to the user in order to ensure that the corresponding resource expansion of the application program is effective and prevent resource waste, and after receiving the confirmation information triggered by the user, determine the expansion configuration information corresponding to the abnormal instance. In addition, the automation operation platform may also determine, directly based on the exception information, capacity expansion configuration information corresponding to the exception instance, which is not limited in the embodiment of the present invention.
In the embodiment of the present invention, the automation operation platform may obtain, from the configuration management system, configuration information corresponding to the abnormal instance based on an instance IP and an identifier of the subsystem in the abnormal information sent by the intelligent prediction system, where the configuration information at least includes a current version of the abnormal instance, a Data Center Node (DCN) where the abnormal instance is located, and an instance number of the abnormal DCN. The identification of the subsystem may be understood as the subsystem ID, and the subsystem ID is the unique ID of the subsystem, e.g. 3019 for the ID of subsystem 1.
In the embodiment of the present invention, the automation operation platform may obtain release information from the automation operation and maintenance platform based on the instance IP and the identifier of the subsystem in the abnormal information sent by the intelligent prediction system, where the release information at least includes a release packet of the subsystem and a release template with the latest instance.
In the embodiment of the present invention, after the automation operation platform obtains the configuration information and the release information corresponding to the abnormal instance, the capacity expansion configuration information corresponding to the abnormal instance may be determined based on the configuration information, the release information, the current capacity, and the prediction threshold.
In the embodiment of the invention, the automatic operation platform can determine the recommended capacity expansion instance number corresponding to the abnormal instance based on the current capacity and the prediction threshold; determining node configuration information and release configuration information corresponding to the abnormal instances based on the configuration information and the release information; and acquiring the capacity expansion configuration information corresponding to the abnormal instances based on the recommended capacity expansion instance number, the node configuration information and the release configuration information.
For example, the recommended capacity expansion instance number in the capacity expansion configuration information may be determined in the following manner:
recommended capacity expansion instance number (current capacity-predicted capacity) current DCN instance number/predicted capacity.
For example, assuming that the current capacity is 4G, the predicted capacity is 2G, and the number of current DCN instances is 2, the recommended number of expansion instances, that is, (4G-2G) × 2/2G ═ 2, may be determined based on the foregoing manner.
It should be understood that the recommended capacity expansion instance number is determined in the above manner, so that the recommended capacity expansion instance number is accurately calculated, and then the capacity expansion configuration information corresponding to the abnormal instance is determined according to the recommended capacity expansion instance number, the node configuration information and the release configuration information, so that accurate automatic capacity expansion is realized.
Step 203: based on the capacity expansion configuration information and a preset establishment rule, establishing a capacity expansion flow comprising a plurality of subtasks; wherein, each subtask has a dependency relationship between the upper level and the lower level with the adjacent subtask.
In the embodiment of the present invention, the automation operation platform may determine, based on the capacity expansion configuration information, requirements of a plurality of subtasks to be created, and then determine, based on the requirements of the plurality of subtasks to be created, setting information corresponding to each subtask, where the setting information at least includes an operation type supporting an operation, an input parameter and an output parameter corresponding to the execution subtask, and the input parameter is provided based on a higher-level subtask corresponding to the subtask. Further, based on the setting information and the preset creation rule, a capacity expansion flow comprising a plurality of subtasks is created. Specifically, the plurality of subtasks at least include: the method comprises a capacity expansion configuration inquiry subtask, a resource application subtask, a resource information synchronization subtask and a service release subtask.
In the embodiment of the present invention, after the automatic operation platform obtains the capacity expansion configuration information, based on the setting information and the preset creation rule, a workflow with an upper-level dependency relationship and a lower-level dependency relationship is created in combination with the open-source distributed task scheduling framework Airflow, and assembled into a Directed Acyclic Graph (DAG), that is, a capacity expansion flow including a plurality of subtasks is created. In the Airflow framework, each step in the actual flow needs to be embodied as an executable task, and for each subtask, setting information defining the following parts is needed:
a. the operations corresponding to the steps support various operation types, such as initiating a hypertext Transfer Protocol (Http) request, executing a Structured Query Language (SQL) statement, executing a Linux Shell interpreter to perform translation to a kernel and transmitting a user/program instruction, i.e., Linux Shell, and the like.
b. Dynamic parameters required for operation execution, such as Http Post parameters, SQL statements, and the like; and the dynamic parameters required for operation execution are usually provided by the upper level subtasks relied on;
c. parameters of operation execution output, such as Http Response, SQL execution result, and the like; and the parameters output by operation execution are usually used for providing for next-level subtasks for use, and can also be used as judgment conditions in the process of flow execution to determine the branch trend of the flow.
For example, the capacity expansion configuration query subtask in the capacity expansion process may search, from the configuration management system, on-line node configuration information corresponding to the subsystem to be currently expanded, that is, the subsystem corresponding to the abnormal instance and the physical area where the subsystem is located. The node configuration information at least includes a node type, for example, the node type is a virtual machine type or a container type, a Central Processing Unit (CPU) core number, a memory size, and a disk capacity. For example, if the ID of the subsystem to be expanded is 3019, the node configuration information corresponding to the subsystem to be expanded may be searched and confirmed from the configuration management system as follows: in 2 virtual machine instances with DCN of 1a1, and memory of 4G, CPU of 2 cores, and disk of 80G.
And the input parameters of the capacity expansion configuration inquiry subtask are the identification of the subsystem in the abnormal information sent by the intelligent prediction system and the physical region where the subsystem is located, and the output parameters are as follows: the reference resource configuration information is the aforementioned node configuration information, and the reference resource configuration information at least includes node type information of the capacity expansion resource.
Illustratively, the resource application subtasks include a host resource application subtask and a container resource application subtask. The host resource application subtask is a task for initiating a virtual machine resource creation request to the host resource management platform, and the input parameter of the host resource application subtask is an output parameter corresponding to the capacity expansion configuration query subtask, that is: applying for the resource quantity and the resource configuration information, and outputting parameters as follows: the applied virtual machine IP. And the container resource application task is a task for initiating a container resource creation request to the container resource management platform, and the input parameters of the host resource application subtask are output parameters corresponding to the capacity expansion configuration query subtask, namely: applying for the resource quantity and the resource configuration information, and outputting parameters as the applied container IP.
Illustratively, the resource information synchronization subtask is a task that initiates a resource synchronization request to other systems or platforms on which service publishing depends, quickly inputs a newly applied resource into the other systems or platforms, and generates a right and configuration required by service operation. Wherein the other system or platform may be an application publishing system or a configuration management platform. Specifically, the resource information synchronization subtask may query, based on the capacity expansion configuration information, whether the abnormal instance in the configuration management platform has the corresponding network security policy and the TGW information, and if so, add the required permission configuration through the relevant interface. And the input parameter of the subtask is the output parameter of the resource application subtask, namely the resource IP.
Illustratively, the service publishing subtask is a task of initiating a service application synchronization request to the application publishing system, acquiring publishing material version information and publishing configuration information from an application instance running on the current line, and executing application publishing operation on a new application resource. And the input parameter of the subtask is the output parameter of the resource application subtask, namely the resource IP.
In addition, in order to improve the availability of the system and the user experience, the capacity expansion process may further include a service dial test subtask, where the service dial test subtask is used to detect whether the capacity expansion application is working normally, and a capacity check subtask, and the capacity check subtask is used to actively detect the actual capacity after capacity expansion, and if the actual capacity is within the lower limit range of the predicted value of the capacity, it is determined that the system capacity after capacity expansion meets expectations, and the system capacity is within the system safety line. Of course, other subtasks may also be created, and are not described in detail in the embodiment of the present invention.
Therefore, in the embodiment of the present invention, after the intelligent prediction system initiates a new automatic capacity expansion request, that is, after the abnormal information, the automatic operation platform may obtain the identifier of the subsystem to be expanded and the physical area where the subsystem is located from the abnormal information, determine capacity expansion configuration information based on the obtained identifier, and automatically generate the Airflow workflow in combination with the preset task dependency relationship, that is, create the capacity expansion flow.
Step 204: and executing an expansion flow comprising a plurality of subtasks based on the upper and lower level dependency relationship, acquiring expansion resources corresponding to the abnormal instances, and sending the expansion resources to the equipment corresponding to the abnormal instances.
In the embodiment of the present invention, the capacity expansion configuration query subtask, the resource application subtask, the resource information synchronization subtask, and the service release subtask in the capacity expansion flow may be executed based on the upper-level and lower-level dependency relationships, so as to obtain the capacity expansion resource corresponding to the abnormal instance, and send the capacity expansion resource to the device corresponding to the abnormal instance.
In a possible implementation manner, executing a capacity expansion configuration query subtask in a capacity expansion flow, and sending resource template check information to a cloud management platform when capacity expansion resources are determined to be virtual machine resources; the resource template checking information at least comprises parameters and attribute information corresponding to the host.
Specifically, while ensuring the consistency of the newly-expanded instance and the source instance resources, high availability and security of financial services need to be ensured. Therefore, the cloud management platform may verify the parameters and the attribute information corresponding to the host based on the received resource template verification information, where the parameters and the attribute information corresponding to the host at least include the following information: the target Internet Database connection program (Internet Database Connector, IDC) and the network area, the host type (physical machine/virtual machine), the operating system version, the host configuration, the host usage, the host and subsystem relationship, the number of hosts and the distribution information, specifically, the distribution information can be understood as the information of the physical area and the application area where the host is located.
Specifically, the cloud management platform may determine that the verification is passed based on whether the parameter and the attribute information corresponding to the host in the resource template information satisfy the requirement for the corresponding preset information of the abnormal instance, and if so, determine that the verification is passed. Specifically, after the verification is passed, the cloud management platform may send a task identifier to the automation operation platform, where the task identifier is fed back by the cloud management platform after the verification of the parameter information and the attribute information corresponding to the host passes. Further, after the automatic operation platform receives the task identifier sent by the cloud management platform, an acquisition request for capacity expansion resources is sent to the cloud computing platform based on the task identifier.
In the embodiment of the present invention, the cloud computing platform may obtain, according to the resource template identifier in the request for obtaining the capacity expansion resource, other information such as a physical area and an instance where the corresponding resource template is located, and screen out, according to the deployment condition of the subsystem in the existing network configuration management platform and according to the high-availability allocation rule engine, a wiring manner (Top of Rack, TOR) that satisfies the high-availability rule and has the minimum resource utilization rate, where the wiring manner may be a wiring manner in which an access switch is installed on the uppermost surface of a standard 42U server cabinet.
For example, the high availability rule may be: (1) the IDC number of subsystem instance deployment is 2; (2) instances of a single physical region are deployed on at least two different TORs; (3) when in deployment, the devices are uniformly distributed according to the TOR, the cabinet and the physical machine; (4) different physical areas and application domains can share the physical machine.
Optionally, when it is determined that the applied expansion resources are physical machine resources, the cloud computing platform selects a specific independent RACK or self-supporting enclosure RACK for accommodating electrical or electronic equipment from the TOR with the minimum utilization rate, screens physical machine equipment in a specific machine position from the RACK, obtains a Serial Number (SN) and an IP of the physical machine, and temporarily stores the SN and the IP in a physical host pre-allocation resource pool.
Optionally, when it is determined that the applied capacity expansion resource is a virtual machine resource, the cloud computing platform may calculate the available zone corresponding to the TOR according to the TOR. Specifically, the cloud computing platform calculates a used Delivery Point (POD) according to the distribution of the inventory application instances. The delivery point is a basic physical design unit of the data center, and specifically refers to a standard module which is constructed by a cabinet, a server, a storage device and a network device and meets virtualization requirements. Then, the cloud computing platform can determine a POD set available to a subsystem corresponding to the abnormal instance; if the number of the application examples is not larger than the available POD set, the PODs cannot be reused, so that the PODs with the least resource allocation are preferentially selected for allocation; if the number of application instances is greater than the available set of PODs, the PODs are reused and the PODs to be distributed are selected.
Furthermore, the cloud computing platform allocates IPs corresponding to the number of instances from the IP pool of the virtual network segment, and allocates hostnames in an increasing manner according to HOST HOSTs in a management platform configured under the current network, and temporarily stores the hostnames in a pre-allocation resource pool.
In the embodiment of the invention, the cloud computing platform can deploy and create the hosts to be allocated in the pre-allocation. Further, after the creation is completed, the cloud computing platform may initialize the resource corresponding to the host. When initializing the resource, the stored mirror image can be directly obtained from the shared storage pool based on a preset mode, and polling check is performed based on a preset time interval during initialization. The preset time period is, for example, 10 seconds or 20 seconds, and the embodiment of the present invention is not limited.
For example, the aforementioned default manner may be to create a distributed block device storage pool, i.e., ceph rbd circular pool, in advance in the shared storage pool. The shader, the chunk store service, provides the chunk store for instances. The block storage service provides the infrastructure to manage the volume and through cooperation with the compute service nova, is able to mount the volume onto an instance. Block storage service can manage volume snapshot and volume at the same timeThe backup and the type of volume. And based on the storage mirror image of the storage pool of the distributed block storage device, the nova creates a distributed shared block storage mirror image management storage back end, namely RBD image baseband, and imports the content of the image service, namely image service, and then delivers the content to libvirt to directly obtain the mirror image from the storage pool. Wherein, nova is a component of an open-source cloud computing management platform openstack; libvirt is a method for realizing Linux virtualization function
And the API supports various virtual machine monitoring programs, namely libvirt is a unified virtualization management interface.
Therefore, in the embodiment of the invention, the mirror image copy mode is optimized in the cloud host resource creating part, so that the cloud host resource creating speed can be increased, and the capacity expansion efficiency can be improved. In addition, the initialization checking mode is optimized, the original fixed waiting time is set, the polling checking is optimized at intervals of preset duration, the waiting duration is reduced, and the cloud host resource creating speed is accelerated.
In the embodiment of the invention, after the cloud computing platform completes the resource initialization, the network resources can be fed back to the automatic operation platform, so that the automatic operation platform receives the network resources fed back by the cloud computing platform and takes the network resources as capacity expansion resources corresponding to the abnormal instances. As can be seen, the network resources are established for the cloud computing platform based on the resource template acquisition request, and the resources after the established resources are initialized are allocated and determined; the method comprises the steps of establishing a shared storage pool, initializing the established resources, and performing polling check on the established resources based on a preset time interval.
In a possible implementation manner, the automation operation platform executes a capacity expansion configuration query subtask in the capacity expansion process, and sends capacity expansion configuration information to the general container platform when it is determined that the capacity expansion resource is a container resource. And then receiving the expansion configuration information of the general container platform, screening the resources meeting the high availability principle from the corresponding resource pool, and taking the resources as the expansion resources corresponding to the abnormal instances.
In the embodiment of the invention, after the capacity expansion resources corresponding to the abnormal instances are determined, the automatic operation platform triggers the capacity expansion resources to be automatically synchronized to the configuration information system and the application release system, so that the application release system automatically issues the release plug-in through the automatic operation platform script.
Specifically, the smart ops transfers the subsystem unique key subsystem ID through the interface information, triggers the AOMP to acquire all instances of the subsystem through the CMDB, and writes all the acquired instances of the subsystem into the cache, and keeps a preset time, for example, 5 minutes, from being refreshed to acquire information of the allocated resources. And in order to ensure that the application is normally and quickly issued, the SMARTOPS polls the application instance IP information to detect whether the state of the instance in the client agent is normal through AOMP polling, and specifically, whether the instance IP information exists in a cache memory, namely cache, can be inquired in a script library to determine whether the state of the instance in the client is normal. Further, when the condition of the instance in the client is determined to be normal, issuing the publishing plug-in through the AOMP script.
In the embodiment of the invention, the automatic operation platform can send the release confirmation request through the release plug-in, and after receiving the release confirmation information, the automatic operation platform is triggered to deploy the application so as to complete the expansion of the abnormal instance. Specifically, after the resources are ready, in order to ensure service availability, the smart ops sends a release confirmation request to the user, where the request includes the unique application identifier, the application version, and the release template, which are released this time, and when the smart ops receives release confirmation information, the smart ops triggers the AOMP system to start deploying the application. After deployment is completed, the AOMP system writes the state back to the CMDB, and after deployment application is completed, the SMARTOPS system pushes a message to a user through a communication tool.
As shown in fig. 3, the present invention provides an automatic capacity expansion apparatus, the apparatus including: a receiving unit 301, configured to receive exception information of an exception instance, where the exception information at least includes an instance IP, an identifier of a subsystem corresponding to the instance, a current capacity, and a prediction threshold; a determining unit 302, configured to obtain configuration information and release information corresponding to the abnormal instance based on the instance IP and the identifier of the subsystem, and determine capacity expansion configuration information corresponding to the abnormal instance based on the configuration information, the release information, the current capacity, and a prediction threshold; a creating unit 303, configured to create, based on the expansion configuration information and a preset creating rule, an expansion flow including a plurality of subtasks; each subtask has a superior-subordinate dependency relationship with its adjacent subtask; a first processing unit 304, configured to execute the capacity expansion flow including the multiple sub-tasks based on the upper-level and lower-level dependency relationships, obtain capacity expansion resources corresponding to the abnormal instance, and send the capacity expansion resources to the device corresponding to the abnormal instance.
Optionally, the abnormal information is determined based on the following manner: determining historical capacity data corresponding to the subsystem, and fitting a regression equation based on the historical capacity data; predicting a prediction threshold corresponding to the subsystem based on the regression equation; wherein the prediction threshold is the sum of an upper limit value and a preset value of the historical capacity data; and triggering the abnormal information when the current capacity corresponding to the subsystem is determined to be not less than the prediction threshold.
Optionally, the creating unit 303 is specifically configured to: determining the requirements of a plurality of subtasks to be created based on the capacity expansion configuration information; determining setting information corresponding to each subtask based on the requirements of the plurality of subtasks to be created, wherein the setting information at least comprises an operation type supporting operation, and input parameters and output parameters corresponding to the executed subtasks, and the input parameters are provided based on the subtasks of the upper stage corresponding to the subtasks; and creating an expansion flow comprising a plurality of subtasks based on the setting information and a preset creation rule.
Optionally, the plurality of subtasks at least include: the method comprises a capacity expansion configuration inquiry subtask, a resource application subtask, a resource information synchronization subtask and a service release subtask.
Optionally, the first processing unit 304 is specifically configured to: executing a capacity expansion configuration query subtask in the capacity expansion flow, and sending resource template checking information to a cloud management platform when capacity expansion resources are determined to be virtual machine resources; the resource template checking information at least comprises parameters and attribute information corresponding to the host; after receiving the task identifier sent by the cloud management platform, sending an acquisition request of capacity expansion resources to the cloud computing platform based on the task identifier; the task identifier is fed back after the cloud management platform passes the verification of the parameter information and the attribute information corresponding to the host; and receiving the network resources fed back by the cloud computing platform, and taking the network resources as capacity expansion resources corresponding to the abnormal instances.
Optionally, the network resource is determined by the cloud computing platform based on the resource template acquisition request and the resource after the initialization of the created resource is allocated; the method comprises the steps of establishing a shared storage pool, initializing the established resources, and performing polling check on the established resources based on a preset time interval.
Optionally, the first processing unit 304 is specifically configured to: executing a capacity expansion configuration query subtask in the capacity expansion flow, and sending the capacity expansion configuration information to a general container platform when capacity expansion resources are determined to be container resources; and receiving the expansion configuration information of the general container platform, screening the resources meeting the high availability principle from the corresponding resource pool, and taking the resources as the expansion resources corresponding to the abnormal instances.
Optionally, the apparatus further includes a second processing unit, configured to: triggering the capacity expansion resources to be automatically synchronized to a configuration information system and an application release system so that the application release system automatically issues a release plug-in through an automatic operation platform script; and sending a confirmation issuing request, and triggering the automatic operation platform to deploy application after receiving confirmation issuing information so as to complete the expansion of the abnormal instance.
An embodiment of the present invention provides a computer device, which includes a program or an instruction, and when the program or the instruction is executed, the program or the instruction is used to execute an automatic capacity expansion method and any optional method provided by the embodiment of the present invention.
An embodiment of the present invention provides a storage medium, which includes a program or an instruction, and when the program or the instruction is executed, the storage medium is used to execute an automatic capacity expansion method and any optional method provided by the embodiment of the present invention.
Finally, it should be noted that: as will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (11)
1. A method of automated capacity expansion, the method comprising:
receiving abnormal information of an abnormal instance, wherein the abnormal information at least comprises an instance IP, an identifier of a subsystem corresponding to the instance, current capacity and a prediction threshold;
acquiring configuration information and release information corresponding to the abnormal instance based on the instance IP and the identifier of the subsystem, and determining capacity expansion configuration information corresponding to the abnormal instance based on the configuration information, the release information, the current capacity and a prediction threshold;
based on the capacity expansion configuration information and a preset establishment rule, establishing a capacity expansion flow comprising a plurality of subtasks; each subtask has a superior-subordinate dependency relationship with its adjacent subtask;
and executing the capacity expansion flow comprising the plurality of subtasks based on the upper and lower level dependency relationship, acquiring capacity expansion resources corresponding to the abnormal example, and sending the capacity expansion resources to equipment corresponding to the abnormal example.
2. The method of claim 1, wherein the anomaly information is determined based on:
determining historical capacity data corresponding to the subsystem, and fitting a regression equation based on the historical capacity data;
predicting a prediction threshold corresponding to the subsystem based on the regression equation; wherein the prediction threshold is the sum of an upper limit value and a preset value of the historical capacity data;
and triggering the abnormal information when the current capacity corresponding to the subsystem is determined to be not less than the prediction threshold.
3. The method of claim 1, wherein creating a capacity expansion flow including a plurality of subtasks based on the capacity expansion configuration information and a preset creation rule comprises:
determining the requirements of a plurality of subtasks to be created based on the capacity expansion configuration information;
determining setting information corresponding to each subtask based on the requirements of the plurality of subtasks to be created, wherein the setting information at least comprises an operation type supporting operation, and input parameters and output parameters corresponding to the executed subtasks, and the input parameters are provided based on the subtasks of the upper stage corresponding to the subtasks;
and creating an expansion flow comprising a plurality of subtasks based on the setting information and a preset creation rule.
4. The method of claim 3, wherein the plurality of subtasks includes at least: the method comprises a capacity expansion configuration inquiry subtask, a resource application subtask, a resource information synchronization subtask and a service release subtask.
5. The method of claim 1, wherein executing the capacity expansion flow including the plurality of subtasks to obtain capacity expansion resources corresponding to the abnormal instance comprises:
executing a capacity expansion configuration query subtask in the capacity expansion flow, and sending resource template checking information to a cloud management platform when capacity expansion resources are determined to be virtual machine resources; the resource template checking information at least comprises parameters and attribute information corresponding to the host;
after receiving the task identifier sent by the cloud management platform, sending an acquisition request of capacity expansion resources to the cloud computing platform based on the task identifier; the task identifier is fed back after the cloud management platform passes the verification of the parameter information and the attribute information corresponding to the host;
and receiving the network resources fed back by the cloud computing platform, and taking the network resources as capacity expansion resources corresponding to the abnormal instances.
6. The method of claim 5, wherein the network resource is created for the cloud computing platform based on the resource template acquisition request, and the resource after the created resource is initialized is allocated for certain;
the method comprises the steps of establishing a shared storage pool, initializing the established resources, and performing polling check on the established resources based on a preset time interval.
7. The method of claim 1, wherein executing the capacity expansion flow including the plurality of subtasks to obtain capacity expansion resources corresponding to the abnormal instance comprises:
executing a capacity expansion configuration query subtask in the capacity expansion flow, and sending the capacity expansion configuration information to a general container platform when capacity expansion resources are determined to be container resources;
and receiving the expansion configuration information of the general container platform, screening the resources meeting the high availability principle from the corresponding resource pool, and taking the resources as the expansion resources corresponding to the abnormal instances.
8. The method of any of claims 1-7, wherein after obtaining capacity expansion resources corresponding to the exception instance, the method further comprises:
triggering the capacity expansion resources to be automatically synchronized to a configuration information system and an application release system so that the application release system automatically issues a release plug-in through an automatic operation platform script;
and sending a confirmation issuing request, and triggering the automatic operation platform to deploy application after receiving confirmation issuing information so as to complete the expansion of the abnormal instance.
9. An apparatus for automatically expanding capacity, the apparatus comprising:
the system comprises a receiving unit, a judging unit and a judging unit, wherein the receiving unit is used for receiving abnormal information of an abnormal instance, and the abnormal information at least comprises an instance IP, an identifier of a subsystem corresponding to the instance, a current capacity and a prediction threshold;
a determining unit, configured to obtain configuration information and release information corresponding to the abnormal instance based on the instance IP and the identifier of the subsystem, and determine capacity expansion configuration information corresponding to the abnormal instance based on the configuration information, the release information, the current capacity, and a prediction threshold;
the creating unit is used for creating a capacity expansion flow comprising a plurality of subtasks based on the capacity expansion configuration information and a preset creating rule; each subtask has a superior-subordinate dependency relationship with its adjacent subtask;
and the first processing unit is used for executing the capacity expansion flow comprising the plurality of subtasks based on the upper-level and lower-level dependency relationships, acquiring capacity expansion resources corresponding to the abnormal instances, and sending the capacity expansion resources to the equipment corresponding to the abnormal instances.
10. A computer device comprising a program or instructions that, when executed, perform the method of any of claims 1 to 8.
11. A storage medium comprising a program or instructions which, when executed, perform the method of any one of claims 1 to 8.
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