CN117453248A

CN117453248A - Gray scale issuing method, device, computer equipment and storage medium

Info

Publication number: CN117453248A
Application number: CN202311431133.XA
Authority: CN
Inventors: 钟毅; 杨蔚然; 伍慧彬
Original assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Current assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Priority date: 2023-10-31
Filing date: 2023-10-31
Publication date: 2024-01-26

Abstract

The application relates to a gray level publishing method, a gray level publishing device, computer equipment, a storage medium and a computer program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: the method comprises the steps of running a new version application instance, collecting application instance parameter characteristics of the new version application instance in the running process, predicting the running state of the new version application instance according to the application instance parameter characteristics, determining whether to cut off the flow of the new version application instance according to whether the running state is an abnormal state, rolling back the new version application instance to an old version application instance, and ending the gray level release process of an updated version package if all the old version application instances on a target server are detected to be updated to the new version application instance.

Description

Gray scale issuing method, device, computer equipment and storage medium

Technical Field

The present application relates to the field of artificial intelligence technology, and in particular, to a gray scale publishing method, apparatus, computer device, storage medium, and computer program product.

Background

With the development of information technology, the product updating iteration speed in the computer industry is also accelerated, and various product updating methods such as bluish green release and A/B test release exist, but the resource redundancy is large in the product release in the methods, and the transition is not smooth enough.

In order to ensure smooth transition between new and old versions of a product, a gray release mode is adopted, when gray release occurs, a new application is started on a new server, after the new application is confirmed to be well operated, more flow is gradually switched into the new application, and during the period, the number of the operated servers of the new application and the old application can be continuously adjusted, so that the new application can bear larger and larger flow pressure until all flow is switched to the new application.

The existing gray level release mode needs to consume manpower to continuously observe the running condition of new applications, continuously adjust the flow distribution weight of new and old applications and the number of instances of the new and old applications, has high manpower cost, long release completion time, long problem rollback time and low gray level release efficiency.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a gradation release method, apparatus, computer device, computer-readable storage medium, and computer program product that can improve gradation release efficiency.

In a first aspect, the present application provides a gray scale distribution method. The method comprises the following steps:

executing an instance updating step: according to an update version package of the application to be updated, selecting an old version application instance of the application to be updated from a target server to update the old version application instance to a new version application instance;

and executing parameter acquisition: running the new version application instance, and collecting the application instance parameter characteristics of the new version application instance in the running process;

predicting the running state of the new version application instance according to the application instance parameter characteristics;

if the running state is an abnormal running state, cutting off the flow of the new version application instance, rolling back the new version application instance to an old version application instance, and returning to execute the instance updating step;

if the running state is a healthy running state, returning to execute the instance updating step;

and if all the old version application instances on the target server are detected to be updated to be new version application instances, ending the gray level release process of the updated version package.

In one embodiment, the running the new version application instance includes: acquiring a preset flow weight; and according to the preset flow weight, controlling the new version application instance to run by introducing corresponding running flow to the new version application instance.

In one embodiment, after the step of predicting the running state of the new version application instance according to the application instance parameter characteristics, the method further comprises: and if the running state is a sub-health running state, adjusting the preset flow weight, and returning to the execution parameter acquisition step.

In one embodiment, the application instance parameter features include a fusion construct feature and a time series construct feature; the collection of the application instance parameter characteristics of the new version application instance in the running process at least comprises one of the following: collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and fusing the original application instance parameters to obtain fusion construction characteristics; and collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and arranging the original application instance parameters according to the sampling time sequence to obtain a time sequence construction characteristic.

In one embodiment, the predicting the running state of the new version application instance according to the application instance parameter features at least includes one of the following: predicting the running state of the new version application instance in the current time step according to the fusion construction characteristics; and predicting the running state of the new version application instance in the next time step according to the time sequence construction characteristics.

In one embodiment, after the step of cutting off the flow of the new version application instance and rolling back the new version application instance to the old version application instance if the running state is the abnormal running state, the method further includes: generating an abnormality prompting message according to the application instance parameter characteristics and the standard characteristics of the application instance parameter characteristics; and pushing the abnormal prompt message to a terminal.

In a second aspect, the present application further provides a gray scale issuing device. The device comprises:

an update module, configured to perform an instance update step: according to an update version package of the application to be updated, selecting an old version application instance of the application to be updated from a target server to update the old version application instance to a new version application instance;

the acquisition module is used for executing the parameter acquisition steps: running the new version application instance, and collecting the application instance parameter characteristics of the new version application instance in the running process;

the prediction module is used for predicting the running state of the new version application instance according to the application instance parameter characteristics;

the exception return module is used for cutting off the flow of the new version application instance if the running state is an abnormal running state, rolling back the new version application instance to an old version application instance, and returning to execute the instance update step;

The health return module is used for returning to execute the instance updating step if the running state is a health running state;

and the release module is used for ending the gray level release process of the updated version package if detecting that all the old version application instances on the target server are updated to the new version application instances.

In one embodiment, the collecting module is further configured to obtain a preset flow weight; and according to the preset flow weight, controlling the new version application instance to run by introducing corresponding running flow to the new version application instance.

In one embodiment, the apparatus further comprises: and the sub-health return module is used for adjusting the preset flow weight and returning to the execution parameter acquisition step if the running state is a sub-health running state.

In one embodiment, the collecting module is further configured to collect a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and fuse the original application instance parameters to obtain a fused structural feature; and collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and arranging the original application instance parameters according to the sampling time sequence to obtain a time sequence construction characteristic.

In one embodiment, the prediction module is further configured to predict, according to the fusion construct feature, an operation state of the new version application instance at a current time step; and predicting the running state of the new version application instance in the next time step according to the time sequence construction characteristics.

In one embodiment, the apparatus further comprises: the prompt module is used for generating an abnormal prompt message according to the application instance parameter characteristics and the standard characteristics of the application instance parameter characteristics; and pushing the abnormal prompt message to a terminal.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:

In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:

In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:

The gray level distribution method, the gray level distribution device, the computer equipment, the storage medium and the computer program product execute the example update steps: according to the update version package of the application to be updated, the old version application instance of the application to be updated is selected on the target server to be updated into the new version application instance, and part of the old version application instance is selected from the server deployed by the version package to be updated, so that the transition smoothness of the new and old version application instances is ensured, and the problem of resource redundancy is avoided. And executing parameter acquisition: and running the new version application instance, collecting the parameter characteristics of the application instance of the new version application instance in the running process, and collecting the parameter characteristics of the application instance in real time, so that the running state of the new version application instance can be predicted in the subsequent steps conveniently. According to the parameter characteristics of the application instance, the running state of the new version application instance is predicted, if the running state is an abnormal running state, the flow of the new version application instance is cut off, the new version application instance is rolled back to an old version application instance, and an execution instance updating step is returned, if the running state is a healthy running state, the execution instance updating step is returned, and if all the old version application instances on the target server are detected to be updated to the new version application instance, the gray level release process of the updated version package is ended.

Drawings

FIG. 1 is an application environment diagram of a gray scale distribution method in one embodiment;

FIG. 2 is a flowchart of a gray scale distribution method according to an embodiment;

FIG. 3 is a flow diagram of a method of collecting application instance parameter characteristics in one embodiment;

FIG. 4 is a schematic diagram of a prediction of the running state of a new version application instance in one embodiment;

FIG. 5 is a flow diagram of an artificial intelligence based gray scale distribution method in one embodiment;

FIG. 6 is a block diagram showing a configuration of a gradation issuing apparatus in one embodiment;

fig. 7 is an internal structural diagram of a computer device in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.

The gray level publishing method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server.

The server 104 performs an instance update step: according to the update version package of the application to be updated uploaded by the terminal 102, an old version application instance of the application to be updated is selected on the target server to update into a new version application instance, and the parameter acquisition step is executed: the new version application instance is operated, the application instance parameter characteristics of the new version application instance in the operation process are collected, the server 104 predicts the operation state of the new version application instance according to the application instance parameter characteristics, if the operation state is an abnormal operation state, the server 104 cuts off the flow of the new version application instance, rolls back the new version application instance to an old version application instance, returns to an execution instance updating step, if the operation state is a healthy operation state, the server 104 returns to the execution instance updating step, if all the old version application instances on the target server are detected to be updated to the new version application instance, and the server 104 ends the gray level release process of the updated new version package.

The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.

In one embodiment, as shown in fig. 2, a gray level distribution method is provided, and the method is applied to the server in fig. 1 for illustration, and includes:

s202, executing an instance updating step: and according to the update version package of the application to be updated, selecting the old version application instance of the application to be updated from the target server to update the old version application instance.

The application to be updated can be an application passing through an internal test, the internal test is performed on a version package of the application, the updated version package of the application to be updated passing through the test is deployed on a server, and the initial work of release of the version package is completed.

The internal test mode may be to verify whether each service function of the updated application is normal or not and whether the newly developed function is correct or not when the version package is running by calling an interface test tool. The servers may be screened, and the target server may be determined from the plurality of servers, and specifically, the plurality of servers may be screened according to the usage rate of the servers and the remaining capacity of the servers, and the servers with the front server usage rate ranking and the front server remaining capacity ranking may be preferentially selected as the target servers. A server may also be randomly assigned as the target server directly from the server cluster.

The selection process may be performed randomly, where an old version application instance of the application to be updated is selected to be updated to a new version application instance. The selection may also be sequentially performed according to the arrangement order of the application instances of the old version, for example, the selection may be performed according to the sequence of the application instances of the old version in the version package of the old version, where the application instances of the old version are to be updated. The number of old version application instances from which an application to be updated is selected may be one or more.

S204, executing a parameter acquisition step: and running the new version application instance, and collecting the parameter characteristics of the application instance of the new version application instance in the running process.

The application instance parameter characteristics comprise characteristics of the application instance parameter and characteristics of a server deployed by the application instance. Specifically, the new version application instance can be run in a virtual running environment in the target server, and in the running process of the new version application instance, the characteristics of the new version application instance can be collected, and the characteristics of the target server deployed by the new version application instance can also be collected.

For example, the features of the new version application instance may include features such as a service success rate feature of the new version application instance, a system success rate feature of the new version application instance, a transaction processing duration of the new version application instance, and a transaction request number of the new version application instance. The characteristics of the target server deployed by the new version application instance can comprise characteristics of CPU service condition, memory service condition, disk service condition, network delay, IO service condition and the like of the target server. By starting from the dimension of the new version application instance and the environment dimension of the target server deployed by the new version application instance, the characteristics related to the new version application instance are comprehensively acquired, and the accuracy of the follow-up prediction of the running state of the new version application instance is improved.

The method for collecting the application instance parameter characteristics of the new version application instance in the running process comprises the following steps: the method comprises the steps of firstly collecting application instance parameters of a new version application instance in the running process, and then constructing application instance parameter characteristics according to the application instance parameters.

It should be noted that the application instance parameters may be obtained by collecting and analyzing logs of the system and the server.

Specifically, multiple original application instance parameters of the new version application instance in the running process can be collected according to a preset sampling period, and the original application instance parameters are fused to obtain fusion construction features.

And acquiring a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and arranging the original application instance parameters according to the sampling time sequence to obtain a time sequence construction characteristic.

The fusion structural features and the time series structural features can be further processed to obtain new fusion features. It should be noted that the fusion construct feature, the time series construct feature, and the new fusion feature all belong to application instance parameter features. The manner of constructing the features is not limited herein, and may be selected according to actual circumstances.

S206, predicting the running state of the new version application instance according to the application instance parameter characteristics.

The running state of the new version application instance can be predicted according to the type of the application instance parameter characteristic and the application instance parameter characteristic.

Specifically, when the type of the parameter feature of the application instance is a fusion construction feature, the running state of the new version application instance at the current time step is predicted according to the fusion construction feature.

Specifically, when the type of the application instance parameter feature is a time series construction feature, the running state of the new version application instance at the next time step is predicted according to the time series construction feature.

Specifically, when the type of the application instance parameter feature is a new fusion feature, the running states of the new version application instance in the current time step and the next time step can be predicted according to the new fusion feature.

The running state of the new version application instance may include: abnormal operating conditions, healthy operating conditions, sub-healthy operating conditions, and the like.

If the running state is an abnormal running state, indicating that at least one of the application instance parameters is abnormal, and adjusting the new version application instance is needed, wherein the adjusting mode comprises the following steps: cutting off the flow of the new version application instance, rolling back the new version application instance to the old version application instance, generating a prompt message according to the new version application instance, and the like.

If the running state is a healthy running state, the fact that the application instance parameter is not abnormal is indicated, and the release process of the application instance can be continued. If the running state is a sub-health running state, it is indicated that the application instance parameter is not abnormal, but is close to the abnormality, and further monitoring is needed for the new version application instance, where the monitoring mode includes: and further monitoring the new version application instance by adjusting the flow of the new version application instance, so as to determine the real running state of the new version application instance.

S208, if the running state is the abnormal running state, the flow of the new version application instance is cut off, the new version application instance is rolled back to the old version application instance, and the execution instance updating step is returned.

If the running state is an abnormal running state, the traffic of the new version application instance is cut off, namely, the traffic weight of the new version application instance is set to 0, the new version application instance is rolled back to the old version application instance, and the execution instance updating step is returned.

Specifically, if the running state is an abnormal running state, an abnormal prompt message may be generated according to the application instance parameters of the new version application instance that needs to be rolled back, and the abnormal prompt message may be sent to the terminal. The exception prompt message is used for reminding an operation and maintenance person to check the new version application instance and the updated version package.

As an example, if the running state of the new version application instance is an abnormal running state, the traffic of the new version application instance is cut off, the new version application instance is rolled back to the old version application instance, and an abnormal prompt message is generated according to the application instance parameters in the application snapshot (the application instance parameters are stored in the application snapshot) of the new version application instance with the traffic cut off.

S210, if the running state is the healthy running state, returning to the step of updating the execution instance.

If the running state is a healthy running state, the parameter representing the application instance is not abnormal, and the release process of the application instance can be continued, namely, the step of updating the execution instance is returned.

S212, if all the old version application instances on the target server are detected to be updated to new version application instances, ending the gray level release process of the updated version package.

And traversing all old-version application after predicting the running state of the new-version application instance each time, and ending the gray level release process of the updated-version package if detecting that all old-version application instances on the target server are updated to the new-version application instance.

In the above gray scale distribution method, an instance update step is performed: according to the update version package of the application to be updated, the old version application instance of the application to be updated is selected on the target server to be updated into the new version application instance, and part of the old version application instance is selected from the server deployed by the version package to be updated, so that the transition smoothness of the new and old version application instances is ensured, and the problem of resource redundancy is avoided. And executing parameter acquisition: and running the new version application instance, collecting the application instance parameters of the new version application instance in the running process, and collecting the application instance parameters in real time, so that the running state of the new version application instance can be predicted in the subsequent steps conveniently. According to the application instance parameters, the running state of a new version application instance is predicted, if the running state is an abnormal running state, the flow of the new version application instance is cut off, the new version application instance is rolled back to an old version application instance, and an execution instance updating step is returned, if the running state is a healthy running state, the execution instance updating step is returned, and if all the old version application instances on a target server are detected to be updated to new version application instances, the gray level release process of the updated version package is ended

In one embodiment, running a new version application instance includes: and acquiring a preset flow weight, and controlling the operation of the new version application instance by introducing corresponding operation flow to the new version application instance according to the preset flow weight.

The preset traffic weight may be a traffic duty ratio distributed for the new version application instance. The preset flow weight can be determined according to the total flow required for updating the new version package and the preset flow of the new version application instance. And further, according to the preset flow weight, controlling the operation of the new version application instance by introducing corresponding operation flow to the new version application instance.

The running state of the new version application instance can be controlled by adjusting the preset flow weight, specifically, the preset flow weight can be adjusted to be large, the running flow of the new version application instance is improved, the running stability of the application instance is improved, and further, the accurate running state of the new version application instance is obtained. The method can also reduce the magnitude of the preset flow weight, reduce the running flow of the new version application instance, conduct small sample test, reduce the possible abnormal risk of the new version application instance, even if the new version application instance is abnormal, the new version application instance is beneficial to rollback with the application instance and the version package due to the smaller running flow, and improve the gray level release efficiency.

The preset weight flow can be determined according to the number of the new version application examples. The number of instances can be the number of new version application instances at the current time, and the more the number of instances, the closer the gray level release degree of the current update version package is to the release completion, and the more stable the new version application instances running earlier. The preset weight flow is determined according to the number of the new version application instances, so that the new version application instances are more matched with the gray level release process of the update version package, and the trust degree of the new version application instances is enhanced.

In this embodiment, by acquiring the preset flow weight and controlling the running of the new version application instance according to the preset flow weight, the running flow of each new version application instance can be controlled by controlling the new version application instance, so that the flow control precision is improved, and the prediction effect of the running state of the subsequent new version application instance is further improved.

In one embodiment, after the step of predicting the running state of the new version application instance based on the application instance parameter characteristics, the method further comprises: if the running state is a sub-health running state, adjusting preset flow weight, and returning to the step of executing parameter acquisition.

If the running state is a sub-health running state, it is indicated that the application instance parameter feature is not abnormal, but is close to the abnormality, and further monitoring is needed for the new version application instance, where the monitoring mode includes: the method comprises the steps of adjusting the preset flow weight of the new version application instance, further adjusting the running flow of the new version application instance, and further monitoring the new version application instance to determine the real running state of the new version application instance.

The method and the device can increase the weight of the preset flow, improve the running flow of the new version application instance, improve the running stability of the application instance and obtain the accurate running state of the new version application instance.

Specifically, according to the preset flow weight and the flow weight adjustment value, the flow weight after the adjustment is obtained, and then the flow weight after the adjustment is introduced into the new version application instance, so that the running stability of the application instance is improved.

Because the running state of the new version application instance and the flow environment where the new version application instance is located are associated, if multiple sub-health running states occur for a certain new version application instance, the running flow corresponding to the new version application instance may be too small, specifically, multiple sub-health running states occur for a certain new version application instance, and the preset flow weight can be adjusted according to the times of occurrence of the sub-health running states.

The method can also reduce the magnitude of the preset flow weight, reduce the running flow of the new version application instance, conduct small sample test, reduce the possible abnormal risk of the new version application instance, and even if the new version application instance is abnormal, the new version application instance is beneficial to rollback with the application instance and the version package due to the smaller running flow, so that the gray level release efficiency is improved.

Specifically, when a small sample test is performed on the new version application instance, if the running state of the new version application instance is an abnormal state, the preset flow weight is increased, and then a test is performed again, if the running state of the new version application instance is determined to be an abnormal state, the flow of the new version application instance is cut off, the new version application instance is rolled back to be the old version application instance, and an instance update executing step is returned to perform a retest, so that inaccurate running state prediction of the new version application instance due to accidental factors is avoided.

In this embodiment, if the running state is a sub-health running state, an accurate running state of an accurate new version application instance may be obtained by increasing a preset weight, and further, by decreasing a preset flow weight, the possible abnormal risk of the new version application instance may be reduced, and in a subsequent process, the running state of the new version application instance may be abnormal, so that rollback with the application instance and the version package is facilitated due to a smaller running flow, and the gray level release efficiency is improved.

In one embodiment, as shown in the flowchart of the method for collecting application instance parameter features in fig. 3, the application instance parameter features include a fusion construct feature and a time sequence construct feature, and collecting the application instance parameter features of the new version application instance in the running process at least includes one of the following:

s302, collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and fusing the original application instance parameters to obtain fusion construction features.

The original application instance parameters comprise service success rate, system success rate, transaction processing time length, transaction request quantity, CPU utilization rate of the target server, memory utilization rate, disk utilization rate, network delay, IO use condition and the like.

And acquiring a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and fusing vectors corresponding to the original application instance parameters to obtain fusion construction features.

Specifically, after the fusion structural feature is obtained, the running state of the current time step can be predicted according to the fusion structural feature.

S304, collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and arranging the original application instance parameters according to the sampling time sequence to obtain a time sequence construction feature.

The method comprises the steps of setting up a sampling time sequence according to the sampling time sequence, wherein the parameters of each original application instance in the running process of the new version application instance are considered to be dynamically changed, so that the time construction characteristics are obtained.

Specifically, after the time construction feature is obtained, the running state of the new version application instance in the next time step can be predicted according to the time sequence construction feature, that is, the running state of the next time step is obtained according to the time sequence construction feature corresponding to the current time of the new version application instance, instead of just predicting the running state of the current time step.

In this embodiment, by collecting multiple original application instance parameters of a new version application instance in the running process, the parameters of each original application instance are fused to obtain a fusion construction feature, so as to provide a basis for the subsequent prediction of the running state of the current time step of the new version application instance, and the parameters of each original application instance are arranged according to the sequence of sampling time to obtain a time sequence construction feature, so as to provide a basis for the subsequent prediction of the running state of the next time step of the new version application instance.

In one embodiment, predicting the running state of the new version application instance based on the application instance parameter characteristics includes at least one of: predicting the running state of the new version application instance in the current time step according to the fusion construction characteristics; and predicting the running state of the new version application instance at the next time step according to the time sequence construction characteristics.

The prediction schematic diagram of the running state of the new version application example shown in fig. 4, for predicting the running state of the current time step, includes: and constructing a fusion construction feature according to the multiple original application instance parameters, and predicting the running state of the new version application instance of the current time step according to the fusion construction feature to obtain a prediction result, wherein the prediction result comprises one of abnormality, health and sub-health.

A prediction of an operational state for a next time step, comprising: obtaining time sequence construction features according to the plurality of original application instance parameters and the arrangement result of the sampling time sequence of the original application instance parameters, and predicting the running state of the new version application instance of the next time step according to the time sequence construction features to obtain a prediction result.

In this embodiment, the running state of the new version application instance of the current time step is predicted based on the fusion construction feature, and the running state of the new version application instance of the next time step is predicted based on the time sequence construction feature, so that the accurate running state of the new version application instance under each time step can be obtained, and further the efficiency of gray level release is improved.

In one embodiment, if the running state is an abnormal running state, the method further includes, after the steps of cutting off the traffic of the new version application instance and rolling back the new version application instance to the old version application instance: generating an abnormality prompt message according to the application instance parameters and the standard parameters of the application instance parameters; and pushing the abnormality prompt message to the terminal.

The application instance parameter characteristics can be constructed according to the application instance parameters, and the standard characteristics of the application instance parameter characteristics can be constructed according to the standard parameters corresponding to the application instance parameters.

Specifically, taking an application instance parameter as an example of a service success rate, the standard parameter corresponding to the service success rate may be that the service success rate is 80%, and further, the feature constructed according to the current application instance parameter is matched with the standard feature constructed by the standard parameter, if the feature is successfully matched with the standard feature, the application instance parameter is represented to be lower than the standard parameter, that is, the running state of the application instance is abnormal.

When the running state of the application instance is determined to be abnormal, an abnormality prompt message may be generated according to the application instance parameter and the standard parameter, for example, the service success rate of a certain application instance is 75%, and is lower than the standard value by 80%.

In this embodiment, by using the standard feature and the standard parameter corresponding to the standard feature as the judgment basis, the abnormal prompt message is generated, which is helpful for the operation and maintenance personnel to repair or update the update version package corresponding to the application instance according to the abnormal prompt message.

In one embodiment, as shown in fig. 5, there is provided an artificial intelligence based gray scale distribution method, including:

s502, executing an instance updating step: and according to the update version package of the application to be updated, selecting the old version application instance of the application to be updated from the target server to update the old version application instance.

The application to be updated may be a financial application or other applications in the financial field.

S504, executing a parameter acquisition step: and obtaining a preset flow weight.

S506, according to the preset flow weight, the new version application instance is controlled to run by introducing corresponding running flow to the new version application instance.

S508, collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and fusing the original application instance parameters to obtain fusion construction features.

S510, predicting the running state of the new version application instance in the current time step according to the fusion construction characteristics.

S512, collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and arranging the original application instance parameters according to the sampling time sequence to obtain a time sequence construction feature.

S514, predicting the running state of the new version application instance in the next time step according to the time sequence construction characteristics.

The fusion construction features and/or the time sequence construction features can be input into a pre-trained neural network model to respectively predict the running states of new version application instances of the current time step and the next time step.

S516, judging the running state of the new version application instance.

And if one of the running states of the new version application instance in the current step and the next time step is an abnormal state, determining the running state of the new version application instance as the abnormal running state.

And if the running states of the new version application instance in the current step and the next time step are healthy running states, determining the running states of the new version application instance as healthy running states.

And if one of the running states of the new version application instance in the current step and the next time step is in a sub-health state and the other running state is in a sub-health running state or a health running state, determining the running state of the new version application instance as the sub-health running state.

S518, if the running state is the abnormal running state, cutting off the flow of the new version application instance, rolling back the new version application instance to the old version application instance, and returning to the step of executing instance update.

And generating an abnormal prompt message according to the application instance parameters and the standard parameters of the application instance parameters, and pushing the abnormal prompt message to the terminal.

If the running state is an abnormal running state, the traffic of the new version application instance can be cut off, and after the application snapshot and the log are saved, the new version application instance is rolled back to the old version application instance, and traffic is redistributed for the old version application instance, so that the situation that the original transaction amount cannot be responded because the number of the old application instance is small is avoided.

S520, if the running state is the healthy running state, returning to the step of updating the execution instance.

S522, if the running state is a sub-health running state, adjusting preset flow weight, and returning to the step of executing parameter acquisition.

S524, judging whether all the old version application instances on the target server are updated to new version application instances.

In this embodiment, an instance update step is performed: according to the update version package of the application to be updated, the old version application instance of the application to be updated is selected on the target server to be updated into the new version application instance, and part of the old version application instance is selected from the server deployed by the version package to be updated, so that the transition smoothness of the new and old version application instances is ensured, and the problem of resource redundancy is avoided. And executing parameter acquisition: and running the new version application instance, collecting the application instance parameters of the new version application instance in the running process, and collecting the application instance parameters in real time, so that the running state of the new version application instance can be predicted in the subsequent steps conveniently. According to the application instance parameters, the running state of the new version application instance is predicted, if the running state is an abnormal running state, the flow of the new version application instance is cut off, the new version application instance is rolled back to an old version application instance, and an execution instance updating step is returned, if the running state is a healthy running state, the execution instance updating step is returned, and if all the old version application instances on the target server are detected to be updated to new version application instances, the gray level release process of the updated version package is ended.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a gray scale distribution device for realizing the gray scale distribution method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the gray scale distribution device or devices provided below may refer to the limitation of the gray scale distribution method hereinabove, and will not be repeated here.

In one embodiment, as shown in fig. 6, there is provided a gradation issuing apparatus including: update module 602, acquisition module 604, prediction module 606, exception return module 608, health return module 610, and publication module 612, wherein:

an update module 602, configured to perform an instance update step: according to the update version package of the application to be updated, selecting an old version application instance of the application to be updated from the target server to update the old version application instance;

the acquisition module 604 is configured to perform the parameter acquisition step: running a new version application instance, and collecting the parameter characteristics of the application instance of the new version application instance in the running process;

a prediction module 606, configured to predict an operation state of the new version application instance according to the application instance parameter characteristics;

the exception return module 608 is configured to cut off the flow of the new version application instance if the running state is an abnormal running state, roll back the new version application instance to the old version application instance, and return to the execution instance update step;

the health return module 610 is configured to return to the execution instance update step if the running state is a healthy running state;

and the release module 612 is configured to end the gray level release process of the updated version package if it is detected that all the old version application instances on the target server have been updated to new version application instances.

In one embodiment, the acquisition module 604 is further configured to acquire a preset flow weight; and according to the preset flow weight, controlling the operation of the new version application instance by introducing corresponding operation flow to the new version application instance.

In one embodiment, the gray scale distribution device further includes: and the sub-health return module is used for adjusting preset flow weight and returning to the execution parameter acquisition step if the operation state is the sub-health operation state.

In one embodiment, the collection module 604 is further configured to collect a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and fuse the original application instance parameters to obtain a fusion construction feature; and collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and arranging the original application instance parameters according to the sampling time sequence to obtain a time sequence construction characteristic.

In one embodiment, the prediction module 606 is further configured to predict an operation state of the new version application instance at the current time step according to the fusion construct feature; and predicting the running state of the new version application instance at the next time step according to the time sequence construction characteristics.

In one embodiment, the gray scale distribution device further includes: the prompt module is used for generating an abnormal prompt message according to the application instance parameters and the standard parameters of the application instance parameters; and pushing the abnormality prompt message to the terminal.

The respective modules in the gradation issuing apparatus described above may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data such as the running state of the new version application instance. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a gray scale distribution method.

It will be appreciated by those skilled in the art that the structure shown in fig. 7 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:

executing an instance updating step: according to the update version package of the application to be updated, selecting an old version application instance of the application to be updated from the target server to update the old version application instance;

and executing parameter acquisition: running a new version application instance, and collecting the parameter characteristics of the application instance of the new version application instance in the running process;

predicting the running state of the new version application instance according to the parameter characteristics of the application instance;

if the running state is the abnormal running state, cutting off the flow of the new version application instance, rolling back the new version application instance to the old version application instance, and returning to the step of executing instance update;

If the running state is a healthy running state, returning to the step of updating the execution instance;

In one embodiment, the processor when executing the computer program further performs the steps of: acquiring a preset flow weight; and according to the preset flow weight, controlling the operation of the new version application instance by introducing corresponding operation flow to the new version application instance.

In one embodiment, the processor when executing the computer program further performs the steps of: if the running state is a sub-health running state, adjusting preset flow weight, and returning to the step of executing parameter acquisition.

In one embodiment, the processor when executing the computer program further performs the steps of: collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and fusing the original application instance parameters to obtain fusion construction characteristics; and collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and arranging the original application instance parameters according to the sampling time sequence to obtain a time sequence construction characteristic.

In one embodiment, the processor when executing the computer program further performs the steps of: predicting the running state of the new version application instance in the current time step according to the fusion construction characteristics; and predicting the running state of the new version application instance at the next time step according to the time sequence construction characteristics.

In one embodiment, the processor when executing the computer program further performs the steps of: generating an abnormality prompt message according to the application instance parameters and the standard parameters of the application instance parameters; and pushing the abnormality prompt message to the terminal.

In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:

In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a preset flow weight; and according to the preset flow weight, controlling the operation of the new version application instance by introducing corresponding operation flow to the new version application instance.

In one embodiment, the computer program when executed by the processor further performs the steps of: if the running state is a sub-health running state, adjusting preset flow weight, and returning to the step of executing parameter acquisition.

In one embodiment, the computer program when executed by the processor further performs the steps of: collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and fusing the original application instance parameters to obtain fusion construction characteristics; and collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and arranging the original application instance parameters according to the sampling time sequence to obtain a time sequence construction characteristic.

In one embodiment, the computer program when executed by the processor further performs the steps of: predicting the running state of the new version application instance in the current time step according to the fusion construction characteristics; and predicting the running state of the new version application instance at the next time step according to the time sequence construction characteristics.

In one embodiment, the computer program when executed by the processor further performs the steps of: generating an abnormality prompt message according to the application instance parameters and the standard parameters of the application instance parameters; and pushing the abnormality prompt message to the terminal.

In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:

It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use, and processing of the related data are required to meet the related regulations.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.

Claims

1. A gray scale distribution method, characterized in that the method comprises:

2. The method of claim 1, wherein the running the new version application instance comprises:

acquiring a preset flow weight;

and according to the preset flow weight, controlling the new version application instance to run by introducing corresponding running flow to the new version application instance.

3. The method of claim 2, wherein after the step of predicting the operating state of the new version application instance based on the application instance parameter characteristics, the method further comprises:

and if the running state is a sub-health running state, adjusting the preset flow weight, and returning to the execution parameter acquisition step.

4. The method of claim 1, wherein the application instance parameter features include a fusion construct feature and a time series construct feature; the collection of the application instance parameter characteristics of the new version application instance in the running process at least comprises one of the following:

collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and fusing the original application instance parameters to obtain fusion construction characteristics;

and collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and arranging the original application instance parameters according to the sampling time sequence to obtain a time sequence construction characteristic.

5. The method of claim 4, wherein predicting the operating state of the new version of the application instance based on the application instance parameter characteristics comprises at least one of:

predicting the running state of the new version application instance in the current time step according to the fusion construction characteristics;

and predicting the running state of the new version application instance in the next time step according to the time sequence construction characteristics.

6. The method according to claim 1, wherein after the step of cutting off the traffic of the new version application instance and rolling back the new version application instance to the old version application instance if the running state is an abnormal running state, further comprising:

generating an abnormality prompt message according to the application instance parameters and the standard parameters of the application instance parameters;

and pushing the abnormal prompt message to a terminal.

7. A gradation issuing apparatus, characterized in that the apparatus comprises:

8. The apparatus of claim 7, wherein the acquisition module is further configured to acquire a preset flow weight; and according to the preset flow weight, controlling the new version application instance to run by introducing corresponding running flow to the new version application instance.

9. The apparatus of claim 8, wherein the apparatus further comprises: and the sub-health return module is used for adjusting the preset flow weight and returning to the execution parameter acquisition step if the running state is a sub-health running state.

10. The apparatus of claim 8, wherein the acquisition module is further configured to acquire a plurality of original application instance parameters of the new version application instance in a running process according to a preset sampling period, and fuse the original application instance parameters to obtain a fused construction feature; and collecting a plurality of original application instance parameters of the new version application instance in the running process according to a preset sampling period, and arranging the original application instance parameters according to the sampling time sequence to obtain a time sequence construction characteristic.

11. The apparatus of claim 10, wherein the prediction module is further configured to predict an operational state of the new version application instance at a current time step based on the fused construct feature; and predicting the running state of the new version application instance in the next time step according to the time sequence construction characteristics.

12. The apparatus of claim 7, wherein the apparatus further comprises: the prompt module is used for generating an abnormal prompt message according to the application instance parameter characteristics and the standard characteristics of the application instance parameter characteristics; and pushing the abnormal prompt message to a terminal.

13. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.

14. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.

15. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.