CN114020352B - Cloud platform configuration method, device, equipment and storage medium - Google Patents
Cloud platform configuration method, device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a cloud platform configuration method, a device, equipment and a medium, which comprise the following steps: determining whether a target function code of the cloud platform has a target abnormal event corresponding to a preset abnormal type in the operation process by using an abnormal discrimination parameter corresponding to the preset abnormal type; if yes, throwing out the target abnormal event, and determining corresponding abnormal throwing-out statistical information; judging whether the abnormal ejection statistical information meets a preset adjustment condition or not; if yes, the abnormal judging parameters are adjusted, and the adjusted parameters are loaded to the target function codes, so that the abnormal throwing statistical information of the target function codes loaded with the adjusted parameters in the running process does not meet the preset adjusting conditions. According to the method, the abnormal discrimination parameters corresponding to the preset abnormal types in the abnormal events are adjusted, the throwing of the target abnormal events in the running process of the target functional codes is reduced, the hardware adaptability and stability of the cloud platform are improved, and meanwhile, the working pressure of operation and maintenance personnel is reduced.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a cloud platform configuration method, device, equipment, and storage medium.
Background
Currently, cloud computing has fallen to the ground in various industries of China, and by virtue of core advantages such as high resource utilization rate, flexible configuration as required, multi-tenant self-service and the like, a cloud computing mode gradually replaces a traditional IT (Internet Technology ) mode. Cloud computing is a novel convenient resource providing mode, under the aggregation of hardware such as traditional computing, storage and network, software in the cloud computing field is added, so that the dispatching management of hardware resources is unified, the allocation of the resources is more reasonable, the IT operation cost of a data center is reduced, and the operation efficiency of IT is increased.
The cloud computing also has the advantages of high efficiency, flexibility and openness, and has the capability of decoupling the binding of hardware manufacturers, but because of the advantages, the bottom layer of the cloud computing is complex, and because the cloud computing comprehensively integrates computing servers of different manufacturers, storage devices of different manufacturers and network switches of different manufacturers, the cloud computing has a plurality of adaptability problems to different hardware.
The current cloud platform software has the capability of capturing an abnormal event, namely when the function code of the cloud platform throws out the abnormal event, the cloud platform captures the abnormal event, but after the cloud platform captures the abnormal event, the cloud platform only returns to the upper layer to be called, and no subsequent solving operation is performed on the abnormal event, so that the subsequent function cannot be normally used. For example, when some time-consuming operations or concurrent requests of resources or abnormal events such as poor storage performance of the back end, slow processing, continuous fluctuation of the network and the like occur, when the timeout time is set too short, the upper layer logic cannot process the completion often because of timeout abnormality of the timeout exception, if the timeout time configuration parameter is not adjusted in time, the subsequent function code operation still cannot process the completion, so that the function cannot be normally used all the time.
In summary, how to make cloud computing easier to use and stable is a problem to be solved in the technical field of cloud computing.
Disclosure of Invention
Accordingly, the present invention is directed to a cloud platform configuration method, device, apparatus, and storage medium, which can automatically adjust abnormality determination parameters to reduce occurrence of a target abnormal event. The specific scheme is as follows:
In a first aspect, the present application discloses a cloud platform configuration method, including:
determining whether a target function code of the cloud platform has a target abnormal event corresponding to a preset abnormal type in the operation process by using an abnormal discrimination parameter corresponding to the preset abnormal type;
if the target function code has the target abnormal event in the running process, throwing the target abnormal event, and determining corresponding abnormal throwing statistical information;
judging whether the abnormal ejection statistical information meets a preset adjustment condition or not;
and if the abnormal ejection statistical information meets the preset adjustment condition, adjusting the abnormal judgment parameter, and loading the adjusted parameter to the target function code so that the abnormal ejection statistical information of the target function code loaded with the adjusted parameter in the operation process does not meet the preset adjustment condition.
Optionally, the determining whether the target function code of the cloud platform has the target abnormal event corresponding to the preset abnormal type in the operation process by using the abnormal discrimination parameter corresponding to the preset abnormal type includes:
monitoring target state parameters of target function codes of the cloud platform in the running process in real time;
Comparing a predetermined abnormality discrimination parameter corresponding to a preset abnormality type with the target state parameter to determine whether a target abnormality event corresponding to the preset abnormality type occurs in the running process of the target function code.
Optionally, the comparing the predetermined abnormality discrimination parameter corresponding to the preset abnormality type with the target state parameter to determine whether the target function code has a target abnormal event corresponding to the preset abnormality type in the running process includes:
judging whether the target state parameter is greater than or equal to the abnormality judgment parameter;
if the target state parameter is greater than or equal to the abnormality discrimination parameter, judging that a target abnormal event corresponding to the preset abnormal type occurs in the running process of the target functional code;
and if the target state parameter is smaller than the abnormality discrimination parameter, judging that the target function code does not have a target abnormal event corresponding to the preset abnormal type in the operation process.
Optionally, the adjusting the abnormality discrimination parameter and loading the adjusted parameter to the target function code includes:
Acquiring a preset parameter adjustment multiple, and multiplying the preset parameter adjustment multiple by the abnormality discrimination parameter to obtain an adjusted parameter;
the abnormal ejection statistical information is redetermined by calling the adjusted parameters, and whether the redetermined abnormal ejection statistical information meets the preset adjustment conditions is judged;
if the abnormal throwing statistical information does not meet the preset adjustment conditions, thermally loading the adjusted parameters to the target function codes and ending;
if the abnormal ejection statistical information meets the preset adjustment condition, updating the preset parameter adjustment multiple to obtain the updated preset parameter adjustment multiple, and re-entering the step of multiplying the preset parameter adjustment multiple with the abnormal judgment parameter.
Optionally, if the target function code has the target abnormal event in the running process, the target abnormal event is thrown, and corresponding abnormal throwing statistical information is determined, including:
if the target function code has the target abnormal event in the running process, the target abnormal event is thrown out, and the total throwing times of the target abnormal event in a preset time range are counted;
Correspondingly, the judging whether the abnormal ejection statistical information meets the preset adjustment condition comprises the following steps:
judging whether the total throwing times reach a preset throwing times threshold value, and if the total throwing times reach the preset throwing times threshold value, judging that the abnormal throwing statistical information meets the preset adjustment condition.
Optionally, before the throwing the target abnormal event, the method further includes:
creating an exception rejection tool aiming at the preset exception type, and configuring the exception rejection tool for the target function code so as to reject the target exception event by using the exception rejection tool when the target function code has the target exception event in the running process.
Optionally, before counting the total number of times of throwing the target abnormal event in the preset time range, the method includes:
creating a counter for the target abnormal event, and configuring the counter for the target function code so as to count the target abnormal event by using the counter to obtain the total throwing times of the target abnormal event in a preset time range.
In a second aspect, a cloud platform configuration apparatus includes:
The abnormal event determining module is used for determining whether a target function code of the cloud platform has a target abnormal event corresponding to a preset abnormal type in the operation process by utilizing an abnormal discrimination parameter corresponding to the preset abnormal type;
the abnormal event throwing module is used for throwing the target abnormal event when the target function code generates the target abnormal event in the running process;
the information determining module is used for determining corresponding abnormal throwing statistical information;
the condition judgment module is used for judging whether the abnormal ejection statistical information meets a preset adjustment condition or not;
and the parameter adjustment module is used for adjusting the abnormal judgment parameters when the abnormal ejection statistical information meets the preset adjustment conditions, and loading the adjusted parameters to the target function codes so that the abnormal ejection statistical information of the target function codes loaded with the adjusted parameters in the operation process does not meet the preset adjustment conditions.
In a third aspect, the present application discloses an electronic device, comprising:
a memory for storing a computer program;
and the processor is used for executing the computer program to realize the steps of the cloud platform configuration method disclosed above.
In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the steps of the cloud platform configuration method disclosed above.
It can be seen that the application provides a cloud platform configuration method, which includes determining whether a target function code of a cloud platform has a target abnormal event corresponding to a preset abnormal type in an operation process by using an abnormal discrimination parameter corresponding to the preset abnormal type; if the target function code has the target abnormal event in the running process, throwing the target abnormal event, and determining corresponding abnormal throwing statistical information; judging whether the abnormal ejection statistical information meets a preset adjustment condition or not; and if the abnormal ejection statistical information meets the preset adjustment condition, adjusting the abnormal judgment parameter, and loading the adjusted parameter to the target function code so that the abnormal ejection statistical information of the target function code loaded with the adjusted parameter in the operation process does not meet the preset adjustment condition. Therefore, the method and the device automatically adjust the abnormal judgment parameters used for determining whether to throw the target abnormal event when the abnormal throwing statistical information meets the preset adjustment conditions, reduce throwing of the target abnormal event of the target function code loaded with the adjusted parameters in the operation process, further solve the problem of the adaptability of cloud platform software to the diversity brought by different hardware performances, namely, the cloud platform can automatically evolve and adjust the abnormal judgment parameters corresponding to the preset abnormal types in the abnormal event so as to adapt to the diversity of hardware equipment, and simultaneously improve the usability and stability of the cloud platform function, so that the cloud platform function operation is smoother, and the working pressure of operation and maintenance personnel is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a cloud platform configuration method disclosed by the application;
FIG. 2 is a flowchart of a cloud platform configuration method for timeout exception disclosed in the present application;
FIG. 3 is a flowchart of a specific method for configuring a cloud platform according to the present disclosure;
FIG. 4 is a flowchart of a specific cloud platform configuration method disclosed in the present application;
fig. 5 is a schematic structural diagram of a cloud platform configuration device disclosed in the present application;
fig. 6 is a block diagram of an electronic device according to the present disclosure.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Currently, although cloud computing has fallen to the ground in various industries of China, and core advantages such as flexible configuration as required, multi-tenant self-service and the like are utilized by virtue of high resource utilization, the cloud computing mode gradually replaces the traditional IT mode, but the advantages also lead to the fact that the underlying logic for building the cloud computing becomes more complex, so that a lot of adaptability problems of the cloud computing to different hardware exist. Therefore, the application correspondingly provides a cloud platform configuration method, solves the problem of the adaptability of the cloud platform software to the diversity of different hardware performances, and can further reduce the throwing of abnormal events of the function codes loaded with the adjusted parameters in the operation process, so that the cloud platform function operation is smoother, and the usability and stability of the cloud platform function are improved.
The embodiment of the application discloses a cloud platform configuration method, which is shown in fig. 1 and comprises the following steps:
step S11: and determining whether a target function code of the cloud platform has a target abnormal event corresponding to the preset abnormal type in the operation process by using an abnormal discrimination parameter corresponding to the preset abnormal type.
In this embodiment, through the preset abnormality discrimination parameters corresponding to the preset abnormality type, it may be determined whether a target function code of the cloud platform has a target abnormality event corresponding to the preset abnormality type in the operation process. It may be understood that the foregoing abnormality determination parameter corresponds to a preset abnormality type, for example, when the preset abnormality type is a timeout abnormality type, the foregoing abnormality determination parameter corresponding to the timeout abnormality type may be a timeout determination parameter, so that whether a timeout abnormality event corresponding to the timeout abnormality type occurs in an operation process of an object code of the cloud platform may be determined by using the foregoing timeout determination parameter. Specifically, referring to fig. 2, the timeout discrimination parameter may be a timeout discrimination parameter of timeout or a timeout discrimination parameter RPC _response_timeout of some RPCs (Remote Procedure Call, remote procedure calls), and the timeout parameter timeout or RPC _response_timeout may be preset to 60 seconds. It is noted that the above-described object function code may include, but is not limited to, API (Application Programming Interface, application program interface) function code.
Step S12: and if the target function code has the target abnormal event in the running process, throwing the target abnormal event, and determining corresponding abnormal throwing statistical information.
In the implementation, after determining that a target abnormal event corresponding to the preset abnormal type occurs in the running process of the target function code of the cloud platform, throwing the target abnormal event, and determining corresponding abnormal throwing statistical information. It can be understood that when the target abnormal event occurs in the running process of the target functional code, the target abnormal event is immediately thrown out. However, in order to more accurately throw the target abnormal event, an abnormal throwing tool for the preset abnormal type is created in advance, and the abnormal throwing tool is configured for the target function code, so that when the target abnormal event occurs in the running process of the target function code, the target abnormal event is thrown by using the abnormal throwing tool.
Because the cloud platform software has the capability of capturing the abnormal event, the cloud platform can capture the target abnormal event thrown by the target function code. For example, in the cloud platform software code, there is often a capture code of TimeoutException, so that the cloud platform can automatically capture an abnormal event thrown by the function code in the running process, thereby determining corresponding abnormal throwing statistical information.
It should be noted that, the determining of the corresponding abnormal statistical information may specifically be counting the total number of times of the target abnormal event in the preset time range, that is, if the target function code has the target abnormal event in the running process, the target abnormal event is thrown, and counting the total number of times of throwing the target abnormal event in the preset time range. However, in order to count the number of times of throwing the target abnormal event, a counter for the target abnormal event is created in advance, and the counter is configured for the target function code, so that the target abnormal event is counted by using the counter, and the total number of times of throwing the target abnormal event in a preset time range is obtained.
Step S13: and judging whether the abnormal ejection statistical information meets a preset adjustment condition or not.
In this embodiment, after determining the corresponding abnormal ejection statistical information, it may be determined whether the abnormal ejection statistical information satisfies a preset adjustment condition. It can be understood that, when determining that the corresponding abnormal ejection statistical information is the total ejection times of the target abnormal event within the statistical preset time range, whether the abnormal ejection statistical information meets the preset adjustment condition can be determined by judging whether the total ejection times reach the preset ejection times threshold. And if the total throwing times reach the preset throwing times threshold, judging that the abnormal throwing statistical information meets the preset adjustment condition. And if the total throwing times do not reach the preset throwing times threshold, judging that the abnormal throwing statistical information does not meet the preset adjustment condition. When the abnormal ejection statistical information does not meet the preset adjustment condition, that is, when the abnormal ejection statistical information does not meet the preset adjustment condition, it is indicated that the target function code of the cloud platform only accidentally ejects the target abnormal event under the control of the current abnormal ejection statistical information.
For example, referring to fig. 2, when the preset number of times of ejection threshold is 10 times and the total number of times of ejection of the target abnormal event within one minute is counted to be 5 times, it may be determined that the total number of times of ejection does not reach the preset number of times of ejection threshold, and it is determined that the abnormal ejection statistical information does not meet the preset adjustment condition. That is, the preset adjustment condition is whether the total number of times of throwing of the target abnormal event thrown by the target function code reaches 10 times within one minute. When the total throwing times of the target function code for throwing the target abnormal event within one minute is counted to be 5 times, the abnormal throwing statistical information can be judged to not meet the preset adjustment condition, and the abnormal judgment parameters can not be adjusted at all. If the total number of times of throwing the target function code for throwing the target abnormal event within one minute is counted to be 10 times or more than 10 times, the total number of times of throwing can be judged to reach the preset throwing number threshold value, and further the abnormal throwing statistical information is judged to meet the preset adjusting condition.
Step S14: and if the abnormal ejection statistical information meets the preset adjustment condition, adjusting the abnormal judgment parameter, and loading the adjusted parameter to the target function code so that the abnormal ejection statistical information of the target function code loaded with the adjusted parameter in the operation process does not meet the preset adjustment condition.
In this embodiment, whether the abnormality determination parameter needs to be adjusted is determined by determining whether the abnormality ejection statistical information meets a preset adjustment condition. And if the abnormal ejection statistical information meets the preset adjustment condition, adjusting the abnormal judgment parameter, and loading the adjusted parameter to the target function code so that the abnormal ejection statistical information of the target function code loaded with the adjusted parameter in the operation process does not meet the preset adjustment condition. It can be understood that the abnormal judgment parameters are gradually adjusted by calling the target function codes, and the parameters after each adjustment are loaded to the called target function codes, so that the frequency of the target abnormal event being thrown is gradually reduced until the abnormal throwing statistical information of the called target function codes in the running process does not meet the preset adjustment conditions, and then the adjusted parameters corresponding to the abnormal throwing statistical information of the called target function codes in the running process does not meet the preset adjustment conditions are determined to be final preset judgment parameters.
In the embodiment of the application, according to the abnormality discrimination parameters corresponding to the preset abnormality types, the target function codes of the cloud platform are thrown out in the operation process, the corresponding abnormality throwing statistical information is determined, when the abnormality throwing statistical information is judged to meet the preset adjustment conditions, the abnormality discrimination parameters corresponding to the preset abnormality types in the target function codes are adjusted, so that the abnormality throwing statistical information of the target function codes loaded with the adjusted parameters in the operation process does not meet the preset adjustment conditions, the throwing-out of the target abnormality events of the target function codes loaded with the adjusted parameters in the operation process is further reduced, the adaptability of the cloud platform software to the diversity brought by different hardware performances is further solved, namely, the cloud platform can self-evolve the abnormality discrimination parameters corresponding to the preset abnormality types in the abnormality adjustment events to adapt to the diversity of hardware equipment, meanwhile, the usability and the stability of the cloud platform function are improved, the cloud platform function is more smoothly operated, and the working pressure of operation and maintenance staff is lightened.
Referring to fig. 3, an embodiment of the present invention discloses a specific cloud platform configuration method, and compared with the previous embodiment, the present embodiment further describes and optimizes a technical solution.
Step S21: and monitoring target state parameters of target function codes of the cloud platform in the running process in real time.
In this embodiment, a target state parameter of a target function code of a cloud platform in an operation process is monitored in real time through a preset monitoring interface. It may be understood that the target state parameter may be a target state parameter corresponding to a response of the target function code of the cloud platform in the running process.
Step S22: comparing a predetermined abnormality discrimination parameter corresponding to a preset abnormality type with the target state parameter to determine whether a target abnormality event corresponding to the preset abnormality type occurs in the running process of the target function code.
In this embodiment, after determining a target state parameter of a target function code of a cloud platform monitored in real time in an operation process, comparing a predetermined abnormality discrimination parameter corresponding to a preset abnormality type with the target state parameter to determine whether a target abnormality event corresponding to the preset abnormality type occurs in the target function code in the operation process. It can be understood that, according to different preset anomaly types, different comparison modes exist when comparing the predetermined anomaly discrimination parameters corresponding to the preset anomaly types with the target state parameters. That is, different preset exception types may have different determination manners in determining whether the target function code has a target exception event corresponding to the preset exception type in the running process.
In a specific real-time manner, comparing the predetermined abnormality discrimination parameter corresponding to the preset abnormality type with the target state parameter may include: judging whether the target state parameter is greater than or equal to the abnormality judgment parameter, if so, judging that a target abnormal event corresponding to the preset abnormal type occurs in the running process of the target functional code; and if the target state parameter is smaller than the abnormality discrimination parameter, judging that the target function code does not have a target abnormal event corresponding to the preset abnormal type in the operation process.
In another embodiment, comparing the predetermined abnormality discrimination parameter corresponding to the preset abnormality type with the target state parameter may include: judging whether the target state parameter is smaller than the abnormality judging parameter, if so, judging that a target abnormal event corresponding to the preset abnormal type occurs in the running process of the target functional code; if the target state parameter is greater than or equal to the abnormality discrimination parameter, judging that a target abnormal event corresponding to the preset abnormal type does not occur in the running process of the target functional code;
Step S23: and if the target function code has the target abnormal event in the running process, throwing the target abnormal event, and determining corresponding abnormal throwing statistical information.
Step S24: and judging whether the abnormal ejection statistical information meets a preset adjustment condition or not.
Step S25: and if the abnormal ejection statistical information meets the preset adjustment condition, adjusting the abnormal judgment parameter, and loading the adjusted parameter to the target function code so that the abnormal ejection statistical information of the target function code loaded with the adjusted parameter in the operation process does not meet the preset adjustment condition.
For more specific processing procedures of the above steps S23 to S25, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no detailed description is given here.
Therefore, the embodiment of the application utilizes the real-time monitoring of the target state parameter of the target function code of the cloud platform in the operation process to compare with the abnormal judgment parameter so as to judge whether the target function code has the target abnormal event corresponding to the preset abnormal type in the operation process, and can more pertinently throw out the preset abnormal event corresponding to the preset abnormal type, thereby determining the corresponding abnormal throwing statistical information, and then adjusting the abnormal judgment parameter when the abnormal throwing statistical information meets the preset adjustment condition, namely, the cloud platform can self-evolve the abnormal judgment parameter corresponding to the preset abnormal type in the abnormal event to adapt to the diversity of hardware equipment, thereby reducing the throwing out of the abnormal event in the operation process of the function code loaded with the adjusted parameter, reducing the throwing out of the abnormal event log in the background log of the system, further reducing the working pressure of operation and maintenance personnel, improving the usability and the stability of the cloud platform function, and enabling the cloud platform function to operate more smoothly.
Referring to fig. 4, an embodiment of the present invention discloses a specific cloud platform configuration method, and compared with the previous embodiment, the present embodiment further describes and optimizes a technical solution.
Step S31: and determining whether a target function code of the cloud platform has a target abnormal event corresponding to the preset abnormal type in the operation process by using an abnormal discrimination parameter corresponding to the preset abnormal type.
Step S32: and if the target function code has the target abnormal event in the running process, throwing the target abnormal event, and determining corresponding abnormal throwing statistical information.
Step S33: and judging whether the abnormal ejection statistical information meets a preset adjustment condition or not.
For the specific content of the above steps S31 to S33, reference may be made to the corresponding content disclosed in the foregoing embodiment, and a detailed description is omitted herein.
Step S34: and if the abnormal ejection statistical information meets the preset adjustment condition, acquiring a preset parameter adjustment multiple, and multiplying the preset parameter adjustment multiple by the abnormal judgment parameter to obtain an adjusted parameter.
In this embodiment, when it is determined that the abnormal ejection statistical information meets the preset adjustment condition, a preset parameter adjustment multiple is obtained, and the preset parameter adjustment multiple is multiplied by the abnormal discrimination parameter to obtain an adjusted parameter. It will be appreciated that after the adjusted parameters are obtained, the target function code is invoked to load the adjusted parameters into the invoked target function code. It should be noted that, when the parameter adjustment multiple is preset, the parameter adjustment multiple may be set to 1.5 times, 2 times, 3 times, or the like.
Step S35: and re-determining the abnormal ejection statistical information by calling the adjusted parameters, and judging whether the re-determined abnormal ejection statistical information meets the preset adjustment condition.
In this embodiment, after the abnormal judgment parameter is adjusted, when an adjusted parameter is obtained, the abnormal ejection statistical information is redetermined based on the adjusted parameter, and further, whether the redetermined abnormal ejection statistical information meets the preset adjustment condition is determined.
Step S36: and if the abnormal throwing statistical information does not meet the preset adjustment condition, thermally loading the adjusted parameters to the target function code and ending.
In this embodiment, after the redetermined abnormal ejection statistical information is determined to not meet the preset adjustment condition, the adjusted parameter is hot loaded to the target function code and is ended. It can be understood that the target function code of the adjusted parameter is thermally loaded onto the cloud platform, so that the throwing of the target abnormal event corresponding to the preset abnormal type is reduced, and the function corresponding to the target function code on the cloud platform becomes easy to use and stable.
Step S37: if the abnormal ejection statistical information meets the preset adjustment condition, updating the preset parameter adjustment multiple to obtain the updated preset parameter adjustment multiple, and re-entering the step of multiplying the preset parameter adjustment multiple with the abnormal judgment parameter.
In this embodiment, after it is determined that the redetermined abnormal ejection statistical information does not meet the preset adjustment condition, the preset parameter adjustment multiple is updated to obtain the updated preset parameter adjustment multiple, and the step of multiplying the preset parameter adjustment multiple with the abnormal discrimination parameter is re-entered. It can be understood that, by calling the target function code on the cloud platform for multiple times, the adjustment multiple of the preset parameter is updated gradually to adjust the abnormal discrimination parameter, until the abnormal ejection statistical information does not meet the preset adjustment condition, the adjusted parameter is loaded to the target function code and is ended. It should be noted that, when the preset parameter adjustment multiple is updated, the preset parameter adjustment multiple may be updated to 1.5 times, 4 times, 6 times, etc. according to an actual application scenario.
Therefore, the embodiment of the application obtains the preset parameter adjustment multiple, adjusts the abnormal discrimination parameters to obtain the adjusted parameters, completes the self-evolution adjustment of the abnormal discrimination parameters, reduces the throwing of the abnormal event in the operation process of the functional code loading the adjusted parameters, and solves the problem of the adaptability of the cloud platform software to the diversity brought by different hardware performances, namely, the cloud platform can self-evolve the abnormal discrimination parameters corresponding to the preset abnormal types in the abnormal event to adapt to the diversity of hardware equipment, and simultaneously improves the usability and stability of the cloud platform function, so that the cloud platform function operation is smoother, and the working pressure of operation and maintenance personnel is lightened.
Correspondingly, the embodiment of the application also discloses a cloud platform configuration device, which is shown in fig. 5, and comprises the following components:
the abnormal event determining module 11 is configured to determine whether a target function code of the cloud platform has a target abnormal event corresponding to a preset abnormal type in an operation process by using an abnormal discrimination parameter corresponding to the preset abnormal type;
an abnormal event throwing module 12, configured to throw the target abnormal event when the target function code has the target abnormal event in the running process;
the information determining module 13 is used for determining corresponding abnormal ejection statistical information;
a condition judgment module 14, configured to judge whether the abnormal ejection statistical information meets a preset adjustment condition;
and the parameter adjustment module 15 is configured to adjust the abnormality discrimination parameter when the abnormal ejection statistical information meets the preset adjustment condition, and load the adjusted parameter to the target function code, so that the abnormal ejection statistical information of the target function code loaded with the adjusted parameter in the operation process does not meet the preset adjustment condition.
In the embodiment of the application, according to the abnormality discrimination parameters corresponding to the preset abnormality types, the target function codes of the cloud platform are thrown out in the operation process, the corresponding abnormality throwing statistical information is determined, when the abnormality throwing statistical information is judged to meet the preset adjustment conditions, the abnormality discrimination parameters corresponding to the preset abnormality types in the target function codes are adjusted, so that the abnormality throwing statistical information of the target function codes loaded with the adjusted parameters in the operation process does not meet the preset adjustment conditions, the throwing-out of the target abnormality events of the target function codes loaded with the adjusted parameters in the operation process is further reduced, the adaptability of the cloud platform software to the diversity brought by different hardware performances is further solved, namely, the cloud platform can self-evolve the abnormality discrimination parameters corresponding to the preset abnormality types in the abnormality adjustment events to adapt to the diversity of hardware equipment, meanwhile, the usability and the stability of the cloud platform function are improved, the cloud platform function is more smoothly operated, and the working pressure of operation and maintenance staff is lightened.
In some specific embodiments, the abnormal event determining module 11 specifically includes:
the parameter detection unit is used for monitoring the target state parameters of the target function codes of the cloud platform in real time in the running process;
and the parameter comparison unit is used for comparing the predetermined abnormality discrimination parameters corresponding to the preset abnormality type with the target state parameters to determine whether the target function code has a target abnormal event corresponding to the preset abnormality type in the operation process.
In some specific embodiments, the abnormal event determination module 11 specifically further includes:
a parameter judging unit for judging whether the target state parameter is greater than or equal to the abnormality judging parameter;
the first judging unit is used for judging that a target abnormal event corresponding to the preset abnormal type occurs in the running process of the target functional code when the target state parameter is larger than or equal to the abnormal judging parameter;
and the second judging unit is used for judging that the target function code does not have the target abnormal event corresponding to the preset abnormal type in the running process when the target state parameter is smaller than the abnormal judging parameter.
In some embodiments, the exception event ejection module 12 specifically includes:
the abnormal event throwing unit is used for throwing the target abnormal event when the target function code generates the target abnormal event in the running process;
the total throwing times counting unit is used for counting the total throwing times of the target abnormal events within a preset time range;
in some embodiments, the condition determining module 14 specifically includes:
the threshold value judging unit is used for judging whether the total throwing times reach a preset throwing times threshold value, and if the total throwing times reach the preset throwing times threshold value, the abnormal throwing statistical information is judged to meet the preset adjusting condition.
In some embodiments, the parameter adjustment module 15 specifically includes:
the multiple acquisition unit is used for acquiring a preset parameter adjustment multiple and multiplying the preset parameter adjustment multiple by the abnormality discrimination parameter to obtain an adjusted parameter;
the condition judging unit is used for redefining the abnormal ejection statistical information by calling the adjusted parameters and judging whether the redetermined abnormal ejection statistical information meets the preset adjusting conditions or not;
The heat loading unit is used for loading the adjusted parameters to the target function codes and ending when the abnormal throwing statistical information does not meet the preset adjustment conditions;
and the multiple updating unit is used for updating the preset parameter adjustment multiple when the abnormal throwing statistical information meets the preset adjustment condition so as to obtain the updated preset parameter adjustment multiple, and reentering the step of multiplying the preset parameter adjustment multiple with the abnormal judgment parameter.
In some embodiments, the cloud platform configuration device specifically includes:
the ejection tool creation module is used for creating an abnormal ejection tool aiming at the preset abnormal type;
and the ejection tool configuration module is used for configuring the abnormal ejection tool for the target function code so as to eject the target abnormal event by using the abnormal ejection tool when the target function code has the target abnormal event in the running process.
In some embodiments, the cloud platform configuration device specifically includes:
a counter creation module for creating a counter for the target abnormal event;
And the counter configuration module is used for configuring the counter for the target function code so as to count the target abnormal event by using the counter to obtain the total throwing times of the target abnormal event within a preset time range.
Further, the embodiment of the application also provides electronic equipment. Fig. 6 is a block diagram of an electronic device 20, according to an exemplary embodiment, and is not intended to limit the scope of use of the present application in any way.
Fig. 6 is a schematic structural diagram of an electronic device 20 according to an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. The memory 22 is configured to store a computer program, where the computer program is loaded and executed by the processor 21 to implement relevant steps in the cloud platform configuration method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in the present embodiment may be specifically an electronic computer.
In this embodiment, the power supply 23 is configured to provide an operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and the communication protocol to be followed is any communication protocol applicable to the technical solution of the present application, which is not specifically limited herein; the input/output interface 25 is used for acquiring external input data or outputting external output data, and the specific interface type thereof may be selected according to the specific application requirement, which is not limited herein.
The memory 22 may be a carrier for storing resources, such as a read-only memory, a random access memory, a magnetic disk, or an optical disk, and the resources stored thereon may include an operating system 221, a computer program 222, and the like, and the storage may be temporary storage or permanent storage.
The operating system 221 is used for managing and controlling various hardware devices on the electronic device 20 and computer programs 222, which may be Windows Server, netware, unix, linux, etc. The computer program 222 may further include a computer program that can be used to perform other specific tasks in addition to the computer program that can be used to perform the cloud platform configuration method performed by the electronic device 20 disclosed in any of the foregoing embodiments.
Further, the embodiment of the application also discloses a storage medium, wherein the storage medium stores a computer program, and when the computer program is loaded and executed by a processor, the cloud platform configuration method steps disclosed in any embodiment are realized.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The cloud platform configuration method, device, equipment and storage medium provided by the invention are described in detail, and specific examples are applied to illustrate the principle and implementation of the invention, and the description of the above examples is only used for helping to understand the method and core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (9)
1. The cloud platform configuration method is characterized by comprising the following steps of:
determining whether a target function code of the cloud platform has a target abnormal event corresponding to a preset abnormal type in the operation process by using an abnormal discrimination parameter corresponding to the preset abnormal type;
if the target function code has the target abnormal event in the running process, throwing the target abnormal event, and determining corresponding abnormal throwing statistical information;
judging whether the abnormal ejection statistical information meets a preset adjustment condition or not;
if the abnormal ejection statistical information meets the preset adjustment condition, adjusting the abnormal judgment parameter, and loading the adjusted parameter to the target function code so that the abnormal ejection statistical information of the target function code loaded with the adjusted parameter in the operation process does not meet the preset adjustment condition;
if the target function code has the target abnormal event in the running process, the target abnormal event is thrown, and corresponding abnormal throwing statistical information is determined, including:
if the target function code has the target abnormal event in the running process, the target abnormal event is thrown out, and the total throwing times of the target abnormal event in a preset time range are counted;
Correspondingly, the judging whether the abnormal ejection statistical information meets the preset adjustment condition comprises the following steps:
judging whether the total throwing times reach a preset throwing times threshold value, and if the total throwing times reach the preset throwing times threshold value, judging that the abnormal throwing statistical information meets the preset adjustment condition.
2. The cloud platform configuration method according to claim 1, wherein the determining whether the target function code of the cloud platform has the target abnormal event corresponding to the preset abnormal type in the operation process by using the abnormality discrimination parameter corresponding to the preset abnormal type includes:
monitoring target state parameters of target function codes of the cloud platform in the running process in real time;
comparing a predetermined abnormality discrimination parameter corresponding to a preset abnormality type with the target state parameter to determine whether a target abnormality event corresponding to the preset abnormality type occurs in the running process of the target function code.
3. The cloud platform configuration method according to claim 2, wherein comparing the predetermined abnormality discrimination parameter corresponding to the preset abnormality type with the target state parameter to determine whether the target function code has a target abnormality event corresponding to the preset abnormality type during operation, includes:
Judging whether the target state parameter is greater than or equal to the abnormality judgment parameter;
if the target state parameter is greater than or equal to the abnormality discrimination parameter, judging that a target abnormal event corresponding to the preset abnormal type occurs in the running process of the target functional code;
and if the target state parameter is smaller than the abnormality discrimination parameter, judging that the target function code does not have a target abnormal event corresponding to the preset abnormal type in the operation process.
4. The cloud platform configuration method according to claim 3, wherein said adjusting the abnormality discrimination parameter and loading the adjusted parameter to the target function code includes:
acquiring a preset parameter adjustment multiple, and multiplying the preset parameter adjustment multiple by the abnormality discrimination parameter to obtain an adjusted parameter;
the abnormal ejection statistical information is redetermined by calling the adjusted parameters, and whether the redetermined abnormal ejection statistical information meets the preset adjustment conditions is judged;
if the abnormal throwing statistical information does not meet the preset adjustment conditions, thermally loading the adjusted parameters to the target function codes and ending;
If the abnormal ejection statistical information meets the preset adjustment condition, updating the preset parameter adjustment multiple to obtain the updated preset parameter adjustment multiple, and re-entering the step of multiplying the preset parameter adjustment multiple with the abnormal judgment parameter.
5. The cloud platform configuration method according to claim 1, wherein before the throwing the target abnormal event, further comprising:
creating an exception rejection tool aiming at the preset exception type, and configuring the exception rejection tool for the target function code so as to reject the target exception event by using the exception rejection tool when the target function code has the target exception event in the running process.
6. The cloud platform configuration method according to claim 1, wherein before counting the total number of times of throwing the target abnormal event in the preset time range, further comprising:
creating a counter for the target abnormal event, and configuring the counter for the target function code so as to count the target abnormal event by using the counter to obtain the total throwing times of the target abnormal event in a preset time range.
7. A cloud platform configuration device, comprising:
the abnormal event determining module is used for determining whether a target function code of the cloud platform has a target abnormal event corresponding to a preset abnormal type in the operation process by utilizing an abnormal discrimination parameter corresponding to the preset abnormal type;
the abnormal event throwing module is used for throwing the target abnormal event when the target function code generates the target abnormal event in the running process;
the information determining module is used for determining corresponding abnormal throwing statistical information;
the condition judgment module is used for judging whether the abnormal ejection statistical information meets a preset adjustment condition or not;
the parameter adjustment module is used for adjusting the abnormal judgment parameters when the abnormal ejection statistical information meets the preset adjustment conditions, and loading the adjusted parameters to the target function codes so that the abnormal ejection statistical information of the target function codes loaded with the adjusted parameters in the operation process does not meet the preset adjustment conditions;
the abnormal event throwing module is specifically configured to: if the target function code has the target abnormal event in the running process, the target abnormal event is thrown out, and the total throwing times of the target abnormal event in a preset time range are counted;
Correspondingly, the condition judging module is specifically configured to:
judging whether the total throwing times reach a preset throwing times threshold value, and if the total throwing times reach the preset throwing times threshold value, judging that the abnormal throwing statistical information meets the preset adjustment condition.
8. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the cloud platform configuration method according to any of claims 1 to 6.
9. A computer-readable storage medium storing a computer program; wherein the computer program when executed by a processor implements the steps of cloud platform configuration according to any of claims 1 to 6.
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