CN112764957A - Application fault delimiting method and device - Google Patents
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Abstract
The embodiment of the application provides an application fault delimiting method and device, relates to the field of cloud computing, and can also be used in the field of finance, and the method comprises the following steps: respectively carrying out correlation analysis on the feature index data of the currently applied application service node and the feature index data of the application sub-node, and determining the fault delimitation feature index of the current application; performing mobility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index; clustering each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node; the method and the device can effectively improve the efficiency and accuracy of application fault delimitation.
Description
Technical Field
The application relates to the field of cloud computing, in particular to an application fault delimiting method and device.
Background
With the development of internet technology and financial technology, online transactions are more and more frequent, the amount of money of the transactions is continuously increased, online transactions bring convenience to people, and meanwhile, the accompanying transaction problems are more and more, and the calling relations among different types of nodes become more complicated, once a certain link fails or has a performance bottleneck, the troubleshooting of simple problems usually spans multiple platforms and teams, so that the efficiency of problem analysis and positioning is greatly reduced, and the influence caused by the failure is greatly increased, so that the internet financial era puts higher requirements on the failure positioning.
The fault root of current application is still the mode of human analysis because of the location, and research personnel obtain application operating data such as log information, monitoring information, link information from a plurality of monitoring platforms after the trouble takes place, then compares the possible node of assay problem from a large amount of application operating data according to alarm information.
The inventor finds that in the mode, effective alarms need to be screened out from massive alarm information, and in addition, a large amount of time is consumed by research and development personnel to analyze and delimit the root cause of the problem, which is found out from application operation data according to the screened effective alarms, so that the efficiency of delimiting and solving the problem is low.
In summary, the conventional method that the fault root cause delimitation depends on manual analysis by research and development personnel is long in time consumption and low in efficiency, and how to realize high-precision and intelligent fault root cause delimitation is a technical problem to be solved in the field.
Disclosure of Invention
Aiming at the problems in the prior art, the application fault delimitation method and device can effectively improve the efficiency and accuracy of application fault delimitation.
In order to solve at least one of the above problems, the present application provides the following technical solutions:
in a first aspect, the present application provides an application fault delimiting method, including:
respectively carrying out correlation analysis on the feature index data of the currently applied application service node and the feature index data of the application sub-node, and determining the fault delimitation feature index of the current application;
performing mobility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index;
and clustering each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node.
Further, the performing correlation analysis on the application service node characteristic index data and the application sub-node characteristic index data of the current application respectively to determine the fault definition characteristic index of the current application includes:
and respectively carrying out correlation analysis on the feature index data of each application service node and the feature index data of each application sub-node of the current application according to a preset association rule mining model, and determining the feature index of the target application service node and the feature index of the target application sub-node corresponding to the obtained frequent item set as the fault delimitation feature index of the current application.
Further, the performing mobility detection on the historical data of each fault delimiting characteristic index according to a preset anomaly detection algorithm to determine a target fault delimiting characteristic index includes:
determining the historical data abnormal proportion of each fault delimitation characteristic index according to a preset abnormal data calibration value;
and determining corresponding abnormal data of the historical data of each fault delimiting characteristic index in a set time window according to the historical data abnormal proportion and a preset abnormal detection algorithm, and setting the corresponding fault delimiting characteristic index as a target fault delimiting characteristic index if the abnormal data exceeds a preset abnormal data threshold value.
Further, the clustering each target fault delimiting characteristic indicator according to a preset clustering algorithm to determine the currently applied fault node includes:
determining link nodes having incidence relation with each target fault delimiting characteristic index;
and clustering the link nodes according to a preset clustering algorithm, and setting the corresponding link nodes as the currently applied fault nodes according to the clustering result.
In a second aspect, the present application provides an application fault delimiting apparatus, including:
the characteristic index correlation analysis module is used for respectively carrying out correlation analysis on the characteristic index data of the currently applied application service node and the characteristic index data of the application sub-node to determine the fault delimitation characteristic index of the current application;
the characteristic index volatility detection module is used for carrying out volatility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index;
and the characteristic index clustering processing module is used for clustering processing on each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node.
Further, the feature index correlation analysis module includes:
and the association rule mining analysis unit is used for respectively carrying out correlation analysis on the feature index data of each application service node and the feature index data of each application sub-node which are currently applied according to a preset association rule mining model, and determining the feature index of the target application service node and the feature index of the target application sub-node corresponding to the obtained frequent item set as the fault delimitation feature index of the current application.
Further, the feature indicator volatility detection module includes:
the historical data abnormal proportion determining unit is used for determining the historical data abnormal proportion of each fault delimitation characteristic index according to a preset abnormal data calibration value;
and the abnormal fluctuation detection unit is used for determining corresponding abnormal data of the historical data of each fault delimitation characteristic index in a set time window according to the historical data abnormal proportion and a preset abnormal detection algorithm, and setting the corresponding fault delimitation characteristic index as a target fault delimitation characteristic index if the abnormal data exceeds a preset abnormal data threshold value.
Further, the feature index clustering processing module includes:
an incidence relation determining unit, configured to determine link nodes having incidence relations with the target fault definition characteristic indicators;
and the fault node determining unit is used for clustering the link nodes according to a preset clustering algorithm and setting the corresponding link nodes as the currently applied fault nodes according to the clustering result.
In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the application fault delimiting method when executing the program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of applying fault definition.
According to the technical scheme, the application fault delimitation method and device are characterized in that the currently applied fault delimitation characteristic index is determined by respectively carrying out correlation analysis on currently applied application service node characteristic index data and application sub-node characteristic index data; performing mobility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index; and clustering each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node, so that the efficiency and accuracy of applying fault delimitation are effectively improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is one of flow diagrams of an application fault delimiting method in an embodiment of the present application;
FIG. 2 is a second flowchart illustrating an application fault delimiting method in an embodiment of the present application;
fig. 3 is a third schematic flowchart of an application fault delimiting method in the embodiment of the present application;
FIG. 4 is a diagram of one of the structures of an application fault delimiting apparatus in the embodiment of the present application;
FIG. 5 is a second block diagram of an application fault delimiting apparatus in an embodiment of the present application;
FIG. 6 is a third block diagram of an application fault delimiting apparatus in an embodiment of the present application;
FIG. 7 is a fourth block diagram of an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In consideration of the problems that in the prior art, effective alarms need to be screened out from massive alarm information, or the problems are delimited and solved in low efficiency due to the fact that research and development personnel need to spend a large amount of time on analyzing and delimiting the root of the problems from application operation data according to the screened effective alarms, the application fault delimiting method and the application fault delimiting device are provided, and the fault delimiting characteristic indexes of the current application are determined by respectively carrying out correlation analysis on the feature index data of the application service nodes and the feature index data of the application sub-nodes of the current application; performing mobility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index; and clustering each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node, so that the efficiency and accuracy of applying fault delimitation are effectively improved.
In order to effectively improve the efficiency and accuracy of application fault delimitation, the application provides an embodiment of an application fault delimitation method, and referring to fig. 1, the application fault delimitation method specifically includes the following contents:
step S101: and respectively carrying out correlation analysis on the currently applied application service node characteristic index data and the application sub-node characteristic index data to determine the currently applied fault delimitation characteristic index.
Optionally, in an application deployment link in the cloud computing field, the child nodes of the application may include a cluster, a template, a container, a virtual machine, a physical machine, and the like, and the service nodes of the application are mainly used for performing database operations, interacting with other application nodes, and the like.
Optionally, the application child node characteristic index data includes but is not limited to: CPU usage, memory usage, number of requests, number of failed requests, CPU usage, number of monitored valid alarms, level of monitored alarms, memory usage, disk usage, network hours of network hours; the application service node characteristic metric data includes, but is not limited to: access response time, access success or failure.
Optionally, the application may perform correlation analysis on feature index data of each application service node and feature index data of each application sub-node of the current application through a preset association rule mining model (for example, an artificial intelligence APRIORI algorithm), so as to obtain a frequent item set, and thus it can be known that a feature index corresponding to the frequent item set is a fault delimitation feature index that can influence whether the current application is in a fault state.
In another embodiment of the present application, the fault definition characteristic index may also be determined from each characteristic index by a manual screening method.
Step S102: and performing mobility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index.
It can be understood that, from the cloud computing deployment perspective, it is necessary to find out which containers, templates, servers, and clusters may be due to the boundary, and therefore, after the applied fault boundary characteristic indicators are screened out in step 101, optionally, the present application may use a preset anomaly detection algorithm (RRCF, Robust random cut forest) to detect the volatility of each fault boundary characteristic indicator, so as to find out those fault boundary characteristic indicators with large volatility, and set them as the target fault boundary characteristic indicators.
Optionally, the fluctuation detection may characterize the fluctuation based on the same-ratio historical data, and may also characterize the fluctuation based on the ring-ratio historical data.
Optionally, the fluctuation is a relative change between the similarity and the environmental data, and there is no threshold, and according to what is noted in advance in the historical data as abnormal points, an abnormal proportion in the historical data, for example, an abnormal proportion of 0.9% can be obtained, and then the abnormal detection algorithm can find out abnormal points in the detection data in the preset time window according to the proportion value.
Step S103: and clustering each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node.
Optionally, after the target fault definition characteristic index is determined through volatility detection, the target fault definition characteristic index may be clustered by using a preset clustering algorithm (e.g., DBSCAN), and specifically, the link node (e.g., a container or a template thereof) having an association relationship (e.g., a dependency relationship) with the target fault definition characteristic index may be clustered.
Taking containers as an example, the method can find out the relationship among the containers of each target fault delimiting characteristic index based on the topological relationship of the deployed links in the log and gather the relationships into one class; in addition, the clustering operation may also be, for example, a container on the same host or belonging to the same template, a cluster, or the like, so that the link node obtained by clustering is determined to be the currently applied fault node, and the fault delimiting operation is completed.
As can be seen from the above description, the application fault delimiting method provided in the embodiment of the present application can determine the fault delimiting characteristic index of the current application by performing correlation analysis on the application service node characteristic index data and the application sub-node characteristic index data of the current application respectively; performing mobility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index; and clustering each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node, so that the efficiency and accuracy of applying fault delimitation are effectively improved.
In order to accurately determine the fault delimiting characteristic index in each application characteristic index, in an embodiment of the application fault delimiting method of the present application, the step S101 may further specifically include the following steps:
and respectively carrying out correlation analysis on the feature index data of each application service node and the feature index data of each application sub-node of the current application according to a preset association rule mining model, and determining the feature index of the target application service node and the feature index of the target application sub-node corresponding to the obtained frequent item set as the fault delimitation feature index of the current application.
Optionally, the application may perform correlation analysis on feature index data of each application service node and feature index data of each application sub-node of the current application through a preset association rule mining model (for example, an artificial intelligence APRIORI algorithm), so as to obtain a frequent item set, and thus it can be known that a feature index corresponding to the frequent item set is a fault delimitation feature index that can influence whether the current application is in a fault state.
In order to accurately determine the fault delimiting characteristic indicator in each applied characteristic indicator, in an embodiment of the application fault delimiting method of the present application, referring to fig. 2, the step S102 may further specifically include the following steps:
step S201: and determining the historical data abnormal proportion of each fault delimitation characteristic index according to a preset abnormal data calibration value.
Step S202: and determining corresponding abnormal data of the historical data of each fault delimiting characteristic index in a set time window according to the historical data abnormal proportion and a preset abnormal detection algorithm, and setting the corresponding fault delimiting characteristic index as a target fault delimiting characteristic index if the abnormal data exceeds a preset abnormal data threshold value.
Optionally, the volatility detection may represent fluctuations based on the same-ratio historical data, or represent fluctuations based on the ring-ratio historical data, where the fluctuations are relative changes of the same-ratio and environmental data, and there is no threshold, and according to what is noted in the historical data in advance is an abnormal point, an abnormal proportion in the historical data, for example, an abnormal proportion of 0.9% may be obtained, and then the abnormal detection algorithm may find an abnormal point (i.e., abnormal data) in the detection data within a preset time window according to the proportion value, and if the abnormal data exceeds a preset abnormal data threshold, set the corresponding fault delimiting feature index as the target fault delimiting feature index.
In order to accurately determine the fault delimiting characteristic indicator in each applied characteristic indicator, in an embodiment of the application fault delimiting method of the present application, referring to fig. 3, the step S103 may further specifically include the following steps:
step S301: and determining link nodes having an association relation with each target fault definition characteristic index.
Step S302: and clustering the link nodes according to a preset clustering algorithm, and setting the corresponding link nodes as the currently applied fault nodes according to the clustering result.
Optionally, after the target fault definition characteristic index is determined through volatility detection, the target fault definition characteristic index may be clustered by using a preset clustering algorithm (e.g., DBSCAN), and specifically, the link node (e.g., a container or a template thereof) having an association relationship (e.g., a dependency relationship) with the target fault definition characteristic index may be clustered.
Taking containers as an example, the method can find out the relationship among the containers of each target fault delimiting characteristic index based on the topological relationship of the deployed links in the log and gather the relationships into one class; in addition, the clustering operation may also be, for example, a container on the same host or belonging to the same template, a cluster, or the like, so that the link node obtained by clustering is determined to be the currently applied fault node, and the fault delimiting operation is completed.
In order to effectively improve the efficiency and accuracy of application fault definition, the present application provides an embodiment of an application fault definition apparatus for implementing all or part of the content of the application fault definition method, and referring to fig. 4, the application fault definition apparatus specifically includes the following contents:
and the characteristic index correlation analysis module 10 is configured to perform correlation analysis on the currently applied application service node characteristic index data and the application sub-node characteristic index data, respectively, and determine a fault definition characteristic index of the current application.
And the characteristic index volatility detection module 20 is configured to perform volatility detection on the historical data of each fault delimiting characteristic index according to a preset anomaly detection algorithm, and determine a target fault delimiting characteristic index.
And the characteristic index clustering processing module 30 is configured to perform clustering processing on each target fault delimiting characteristic index according to a preset clustering algorithm, and determine the currently applied fault node.
As can be seen from the above description, the application fault delimiting device provided in the embodiment of the present application can determine the fault delimiting characteristic index of the current application by performing correlation analysis on the application service node characteristic index data and the application sub-node characteristic index data of the current application respectively; performing mobility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index; and clustering each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node, so that the efficiency and accuracy of applying fault delimitation are effectively improved.
In order to accurately determine the fault definition characteristic indicators in the application characteristic indicators, in an embodiment of the application fault definition apparatus of the present application, referring to fig. 5, the characteristic indicator correlation analysis module 10 includes:
and the association rule mining analysis unit 11 is configured to perform correlation analysis on feature index data of each application service node and feature index data of each application sub-node, which are currently applied, according to a preset association rule mining model, and determine a target application service node feature index and a target application sub-node feature index, which correspond to the obtained frequent item set, as a fault delimitation feature index of the current application.
In order to accurately determine the fault definition characteristic indicators in the application characteristic indicators, in an embodiment of the application fault definition apparatus of the present application, referring to fig. 6, the characteristic indicator volatility detecting module 20 includes:
and the historical data abnormal proportion determining unit 21 is used for determining the historical data abnormal proportion of each fault delimitation characteristic index according to a preset abnormal data calibration value.
And the abnormal fluctuation detection unit 22 is configured to determine, according to the historical data abnormal proportion and a preset abnormal detection algorithm, abnormal data corresponding to the historical data of each fault delimiting characteristic index within a set time window, and set the corresponding fault delimiting characteristic index as a target fault delimiting characteristic index if the abnormal data exceeds a preset abnormal data threshold.
In order to accurately determine the fault definition characteristic indicators in the application characteristic indicators, in an embodiment of the application fault definition apparatus of the present application, referring to fig. 7, the characteristic indicator clustering module 30 includes:
and an association relation determining unit 31, configured to determine link nodes having an association relation with each of the target fault definition characteristic indicators.
And the fault node determination unit 32 is configured to perform clustering processing on the link nodes according to a preset clustering algorithm, and set the corresponding link node as the currently applied fault node according to a result of the clustering processing.
In terms of hardware, in order to effectively improve the efficiency and accuracy of application fault definition, the present application provides an embodiment of an electronic device for implementing all or part of the contents in the application fault definition method, where the electronic device specifically includes the following contents:
a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the application fault delimitation device and relevant equipment such as a core service system, a user terminal, a relevant database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the logic controller may be implemented with reference to the embodiment of the application fault delimiting method and the embodiment of the application fault delimiting apparatus in the embodiment, and the contents thereof are incorporated herein, and repeated descriptions are omitted.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), an in-vehicle device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical applications, part of the application of the fault-delimiting method may be performed on the electronic device side as described above, or all operations may be performed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.
The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.
Fig. 8 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 8, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 8 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In an embodiment, the application fault delimiting method function may be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:
step S101: and respectively carrying out correlation analysis on the currently applied application service node characteristic index data and the application sub-node characteristic index data to determine the currently applied fault delimitation characteristic index.
Step S102: and performing mobility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index.
Step S103: and clustering each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node.
As can be seen from the above description, in the electronic device provided in the embodiment of the present application, relevance analysis is performed on application service node feature index data and application sub-node feature index data of a current application, so as to determine a fault-definition feature index of the current application; performing mobility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index; and clustering each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node, so that the efficiency and accuracy of applying fault delimitation are effectively improved.
In another embodiment, the application fault delimiting means may be configured separately from the central processor 9100, for example, the application fault delimiting means may be configured as a chip connected to the central processor 9100, and the application fault delimiting method function is implemented by the control of the central processor.
As shown in fig. 8, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 8; further, the electronic device 9600 may further include components not shown in fig. 8, which may be referred to in the art.
As shown in fig. 8, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
An embodiment of the present application further provides a computer-readable storage medium capable of implementing all the steps in the application fault definition method in which an execution subject is a server or a client in the foregoing embodiments, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements all the steps in the application fault definition method in which the execution subject is the server or the client in the foregoing embodiments, for example, when the processor executes the computer program, the processor implements the following steps:
step S101: and respectively carrying out correlation analysis on the currently applied application service node characteristic index data and the application sub-node characteristic index data to determine the currently applied fault delimitation characteristic index.
Step S102: and performing mobility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index.
Step S103: and clustering each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node.
As can be seen from the above description, the computer-readable storage medium provided in the embodiment of the present application determines the fault-delimiting characteristic indicator of the current application by performing correlation analysis on the application service node characteristic indicator data and the application sub-node characteristic indicator data of the current application respectively; performing mobility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index; and clustering each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node, so that the efficiency and accuracy of applying fault delimitation are effectively improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for application fault delineation, the method comprising:
respectively carrying out correlation analysis on the feature index data of the currently applied application service node and the feature index data of the application sub-node, and determining the fault delimitation feature index of the current application;
performing mobility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index;
and clustering each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node.
2. The method for delimiting application faults according to claim 1, wherein the performing correlation analysis on the application service node characteristic index data and the application sub-node characteristic index data of the current application to determine the fault-delimiting characteristic index of the current application comprises:
and respectively carrying out correlation analysis on the feature index data of each application service node and the feature index data of each application sub-node of the current application according to a preset association rule mining model, and determining the feature index of the target application service node and the feature index of the target application sub-node corresponding to the obtained frequent item set as the fault delimitation feature index of the current application.
3. The method for applying fault definition according to claim 1, wherein the step of performing mobility detection on the historical data of each fault definition characteristic index according to a preset anomaly detection algorithm to determine a target fault definition characteristic index comprises the following steps:
determining the historical data abnormal proportion of each fault delimitation characteristic index according to a preset abnormal data calibration value;
and determining corresponding abnormal data of the historical data of each fault delimiting characteristic index in a set time window according to the historical data abnormal proportion and a preset abnormal detection algorithm, and setting the corresponding fault delimiting characteristic index as a target fault delimiting characteristic index if the abnormal data exceeds a preset abnormal data threshold value.
4. The method for applying fault definition according to claim 1, wherein the clustering each target fault definition characteristic indicator according to a preset clustering algorithm to determine the currently applied fault node comprises:
determining link nodes having incidence relation with each target fault delimiting characteristic index;
and clustering the link nodes according to a preset clustering algorithm, and setting the corresponding link nodes as the currently applied fault nodes according to the clustering result.
5. An application fault delimiting apparatus, comprising:
the characteristic index correlation analysis module is used for respectively carrying out correlation analysis on the characteristic index data of the currently applied application service node and the characteristic index data of the application sub-node to determine the fault delimitation characteristic index of the current application;
the characteristic index volatility detection module is used for carrying out volatility detection on the historical data of each fault delimitation characteristic index according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic index;
and the characteristic index clustering processing module is used for clustering processing on each target fault delimiting characteristic index according to a preset clustering algorithm to determine the currently applied fault node.
6. The application fault delimiting device of claim 5, wherein the feature indicator correlation analysis module comprises:
and the association rule mining analysis unit is used for respectively carrying out correlation analysis on the feature index data of each application service node and the feature index data of each application sub-node which are currently applied according to a preset association rule mining model, and determining the feature index of the target application service node and the feature index of the target application sub-node corresponding to the obtained frequent item set as the fault delimitation feature index of the current application.
7. The application fault delimiting device of claim 5, wherein the characteristic indicator volatility detection module comprises:
the historical data abnormal proportion determining unit is used for determining the historical data abnormal proportion of each fault delimitation characteristic index according to a preset abnormal data calibration value;
and the abnormal fluctuation detection unit is used for determining corresponding abnormal data of the historical data of each fault delimitation characteristic index in a set time window according to the historical data abnormal proportion and a preset abnormal detection algorithm, and setting the corresponding fault delimitation characteristic index as a target fault delimitation characteristic index if the abnormal data exceeds a preset abnormal data threshold value.
8. The application fault delimiting device of claim 5, wherein the feature index clustering module comprises:
an incidence relation determining unit, configured to determine link nodes having incidence relations with the target fault definition characteristic indicators;
and the fault node determining unit is used for clustering the link nodes according to a preset clustering algorithm and setting the corresponding link nodes as the currently applied fault nodes according to the clustering result.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of applying the fault delimiting method as claimed in any one of claims 1 to 4 are implemented by the processor when executing the program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of applying a fault-delimiting method as claimed in any one of claims 1 to 4.
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