Application fault demarcation method and device

Abstract

The embodiment of the present application provides an application fault delimitation method and device, which relates to the field of cloud computing and can also be used in the financial field. The method includes: performing correlation analysis on the application service node characteristic indicator data and the application sub-node characteristic indicator data of the current application respectively, and determining the fault delimitation characteristic indicator of the current application; performing volatility detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm, and determining the target fault delimitation characteristic indicator; performing clustering processing on each of the target fault delimitation characteristic indicators according to a preset clustering algorithm, and determining the fault node of the current application; the present application can effectively improve the efficiency and accuracy of application fault delimitation.

Description

Technical Field

[0001] The present application relates to the field of cloud computing, and in particular to a method and device for demarcating application faults. Background Art

[0002] With the development of Internet technology and financial technology, online transactions are becoming more and more frequent, and the transaction amounts are also increasing. While online transactions bring convenience to people, there are also more and more transaction problems. In addition, the calling relationships between different types of nodes have become more complicated. Once a failure or performance bottleneck occurs in a certain link, the troubleshooting of simple problems usually requires crossing multiple platforms and teams, which greatly reduces the efficiency of problem analysis and location, and greatly increases the impact caused by the failure. Therefore, the Internet financial era has put forward higher requirements for fault location.

[0003] The root cause location of current application failures is still based on manual analysis. After a failure occurs, R&D personnel obtain application operation data such as log information, monitoring information, and link information from multiple monitoring platforms. Then, based on the alarm information, they compare and analyze the possible nodes of the problem from a large amount of application operation data.

[0004] The inventors found that this approach requires, on the one hand, screening out valid alarms from massive alarm information, and on the other hand, finding the root cause of the problem from the application operation data based on the screened out valid alarms requires R&D personnel to spend a lot of time analyzing and demarcating, resulting in low efficiency in problem demarcation and resolution.

[0005] In summary, the current method of fault root cause delineation relies on manual analysis by R&D personnel, which is time-consuming and inefficient. How to achieve high-precision and intelligent fault root cause delineation is a technical problem that needs to be solved urgently in this field. Summary of the invention

[0006] In view of the problems in the prior art, the present application provides an application fault demarcation method and device, which can effectively improve the efficiency and accuracy of application fault demarcation.

[0007] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0008] In a first aspect, the present application provides an application fault demarcation method, comprising:

[0009] Respectively performing correlation analysis on the application service node characteristic indicator data and the application sub-node characteristic indicator data of the current application to determine the fault delimitation characteristic indicator of the current application;

[0010] Performing fluctuation detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic indicator;

[0011] Clustering is performed on each of the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application.

[0012] Further, the performing of 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 delimitation characteristic index of the current application includes:

[0013] According to the preset association rule mining model, correlation analysis is performed on the characteristic indicator data of each application service node and each application sub-node of the current application, and the target application service node characteristic indicator and the target application sub-node characteristic indicator corresponding to the obtained frequent item set are determined as the fault delimitation characteristic indicator of the current application.

[0014] Furthermore, the fluctuation detection of the historical data of each of the fault delimitation characteristic indicators is performed according to a preset anomaly detection algorithm to determine the target fault delimitation characteristic indicator, including:

[0015] Determine the abnormal proportion of historical data of each fault delimitation characteristic indicator according to a preset abnormal data calibration value;

[0016] The abnormal data corresponding to the historical data of each fault delimitation feature indicator within a set time window is determined according to the abnormal proportion of the historical data and a preset abnormality detection algorithm; if the abnormal data exceeds a preset abnormal data threshold, the corresponding fault delimitation feature indicator is set as a target fault delimitation feature indicator.

[0017] Furthermore, clustering the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application includes:

[0018] Determining a link node associated with each of the target fault demarcation characteristic indicators;

[0019] The link nodes are clustered according to a preset clustering algorithm, and the corresponding link nodes are set as the fault nodes of the current application according to the result of the clustering.

[0020] In a second aspect, the present application provides an application fault demarcation device, comprising:

[0021] A characteristic index correlation analysis module, used to respectively perform correlation analysis on the characteristic index data of the application service node and the characteristic index data of the application sub-node of the current application, and determine the fault delimitation characteristic index of the current application;

[0022] A characteristic indicator volatility detection module is used to perform volatility detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic indicator;

[0023] The characteristic index clustering processing module is used to perform clustering processing on each of the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application.

[0024] Furthermore, the characteristic index correlation analysis module includes:

[0025] The association rule mining and analysis unit is used to perform correlation analysis on the characteristic indicator data of each application service node and each application sub-node of the current application according to a preset association rule mining model, and determine the target application service node characteristic indicator and the target application sub-node characteristic indicator corresponding to the obtained frequent item set as the fault delimitation characteristic indicator of the current application.

[0026] Furthermore, the characteristic index volatility detection module includes:

[0027] A historical data abnormality ratio determination unit, used to determine the historical data abnormality ratio of each of the fault delimitation characteristic indicators according to a preset abnormal data calibration value;

[0028] The abnormal fluctuation detection unit is used to determine the abnormal data corresponding to the historical data of each fault delimitation characteristic indicator within a set time window according to the abnormal proportion of the historical data and a preset abnormal detection algorithm. If the abnormal data exceeds a preset abnormal data threshold, the corresponding fault delimitation characteristic indicator is set as a target fault delimitation characteristic indicator.

[0029] Furthermore, the characteristic index clustering processing module includes:

[0030] An association relationship determination unit, used to determine a link node having an association relationship with each of the target fault delimitation characteristic indicators;

[0031] The fault node determination unit is used to perform clustering processing on the link nodes according to a preset clustering algorithm, and set the corresponding link node as the fault node of the current application according to the result of the clustering processing.

[0032] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the application fault delimitation method when executing the program.

[0033] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the application fault demarcation method when executed by a processor.

[0034] It can be seen from the above technical scheme that the present application provides an application fault delimitation method and device, which determines the fault delimitation characteristic indicators 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; performs volatility detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine the target fault delimitation characteristic indicator; performs clustering processing on each of the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application, thereby effectively improving the efficiency and accuracy of application fault delimitation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 This is one of the flowcharts of the application fault demarcation method in an embodiment of the present application;

[0037] Figure 2 This is a second flow chart of the method for demarcating application faults in an embodiment of the present application;

[0038] Figure 3 This is a third flow chart of the application fault demarcation method in an embodiment of the present application;

[0039] Figure 4 This is one of the structural diagrams of the application fault demarcation device in the embodiment of the present application;

[0040] Figure 5 This is the second structural diagram of the application fault demarcation device in the embodiment of the present application;

[0041] Figure 6 This is the third structural diagram of the application fault demarcation device in the embodiment of the present application;

[0042] Figure 7 This is the fourth structural diagram of the application fault demarcation device in the embodiment of the present application;

[0043] Figure 8 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] Considering that in the prior art, it is necessary to screen out effective alarms from massive alarm information, or to find out the root cause of the problem from the application operation data based on the screened out effective alarms, which requires R&D personnel to spend a lot of time to analyze and delimit, resulting in low efficiency in problem delimitation and solution. The present application provides an application fault delimitation method and device, which determines the fault delimitation 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; performs volatility detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine the target fault delimitation characteristic index; performs clustering processing on each of the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application, thereby effectively improving the efficiency and accuracy of application fault delimitation.

[0046] In order to effectively improve the efficiency and accuracy of application fault demarcation, the present application provides an embodiment of an application fault demarcation method, see Figure 1 The application fault demarcation method specifically includes the following contents:

[0047] Step S101: performing correlation analysis on the application service node characteristic indicator data and the application sub-node characteristic indicator data of the current application respectively, and determining the fault delimitation characteristic indicator of the current application.

[0048] Optionally, in the application deployment link in the field of cloud computing, the application's sub-nodes may include clusters, templates, containers, virtual machines, physical machines, etc. The application's service nodes are mainly used to perform database operations, interact with other application nodes, etc. Based on this, the present application can perform a correlation analysis on the application service node characteristic indicator data and the application sub-node characteristic indicator data of the current application, so as to screen out the fault delimiting characteristic indicators that can affect whether the current application is in a fault state from the characteristic indicators of each application service node and the characteristic indicators of the application sub-nodes of the current application.

[0049] Optionally, the characteristic indicator data of the application sub-node includes but is not limited to: CPU usage, memory usage, number of requests, number of failed requests, CPU usage rate, number of effective monitoring alarms, monitoring alarm level, memory usage rate, disk usage rate, and number of network timeouts; the characteristic indicator data of the application service node includes but is not limited to: access response time and whether the access is successful.

[0050] Optionally, the present application can perform correlation analysis on the characteristic indicator data of each application service node and each application sub-node of the current application through a preset association rule mining model (such as the artificial intelligence APRIORI algorithm) to obtain frequent item sets, from which it can be known that the characteristic indicator corresponding to the frequent item set is the fault delimiting characteristic indicator that can affect whether the current application is in a fault state.

[0051] In another embodiment of the present application, the fault delimitation characteristic index may also be determined from various characteristic indexes by manual screening.

[0052] Step S102: performing fluctuation detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic indicator.

[0053] It is understandable that, from the perspective of cloud computing deployment, root cause delimitation needs to find out which containers, templates, servers, and clusters may have problems. Therefore, after the fault delimitation feature indicators of the application are screened out in step 101, optionally, the present application can use a preset anomaly detection algorithm (RRCF, Robust random cut forest) to detect the volatility of each fault delimitation feature indicator to find out those fault delimitation feature indicators with large volatility, and set them as target fault delimitation feature indicators.

[0054] Optionally, the volatility detection may be based on year-on-year historical data to characterize the volatility, or may be based on month-on-month historical data to characterize the volatility.

[0055] Optionally, fluctuation is the relative change of year-on-year and environmental data. There is no threshold. According to the abnormal points pre-marked in the historical data, the abnormal proportion in the historical data can be obtained, such as an abnormal proportion of 0.9%. Then the anomaly detection algorithm can find the abnormal points in the detection data within the preset time window according to this proportion value.

[0056] Step S103: clustering the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application.

[0057] Optionally, after the target fault delimitation characteristic indicator is determined through volatility detection, the present application can also use a preset clustering algorithm (such as DBSCAN) to cluster the target fault delimitation characteristic indicator. Specifically, link nodes (such as their containers or templates) that have an association relationship (such as a subordinate relationship) with the target fault delimitation characteristic indicator can be clustered.

[0058] Taking containers as an example, the present application can find out the relationship between containers of each target fault demarcation feature indicator based on the deployment link topology relationship in the log and cluster them into one category; in addition, the clustering operation can also be, for example, containers on the same host machine or belonging to the same template, a cluster, etc., thereby determining the link node obtained by clustering as the fault node of the current application to complete the fault demarcation operation.

[0059] From the above description, it can be seen that the application fault delimitation method provided in the embodiment of the present application can determine the fault delimitation characteristic indicators 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; perform volatility detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine the target fault delimitation characteristic indicator; perform clustering processing on each of the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application, thereby effectively improving the efficiency and accuracy of application fault delimitation.

[0060] In order to accurately determine the fault delimitation characteristic index among the application characteristic indexes, in an embodiment of the application fault delimitation method of the present application, the above step S101 may further specifically include the following contents:

[0061] According to the preset association rule mining model, correlation analysis is performed on the characteristic indicator data of each application service node and each application sub-node of the current application, and the target application service node characteristic indicator and the target application sub-node characteristic indicator corresponding to the obtained frequent item set are determined as the fault delimitation characteristic indicator of the current application.

[0062] Optionally, the present application can perform correlation analysis on the characteristic indicator data of each application service node and each application sub-node of the current application through a preset association rule mining model (such as the artificial intelligence APRIORI algorithm) to obtain frequent item sets, from which it can be known that the characteristic indicator corresponding to the frequent item set is the fault delimiting characteristic indicator that can affect whether the current application is in a fault state.

[0063] In order to accurately determine the fault delimitation characteristic index among the application characteristic indexes, in one embodiment of the application fault delimitation method of the present application, see Figure 2 , the above step S102 may also specifically include the following contents:

[0064] Step S201: determining the abnormal proportion of the historical data of each of the fault delimitation characteristic indicators according to a preset abnormal data calibration value.

[0065] Step S202: determining the abnormal data corresponding to the historical data of each of the fault delimitation feature indicators within a set time window according to the abnormal proportion of the historical data and a preset abnormality detection algorithm; if the abnormal data exceeds a preset abnormal data threshold, setting the corresponding fault delimitation feature indicator as a target fault delimitation feature indicator.

[0066] Optionally, the volatility detection can characterize the fluctuation based on year-on-year historical data, or based on month-on-month historical data. The fluctuation is the relative change of year-on-year and environmental data. There is no threshold. The abnormal proportion in the historical data can be obtained based on which points are pre-annotated as abnormal in the historical data, such as an abnormal proportion of 0.9%. Then, the anomaly detection algorithm can find the abnormal points (i.e., abnormal data) in the detection data within the preset time window based on this proportion value. If the abnormal data exceeds the preset abnormal data threshold, the corresponding fault delimitation feature indicator is set as the target fault delimitation feature indicator.

[0067] In order to accurately determine the fault delimitation characteristic index among the application characteristic indexes, in one embodiment of the application fault delimitation method of the present application, see Figure 3 , the above step S103 may also specifically include the following contents:

[0068] Step S301: determining link nodes associated with each of the target fault demarcation characteristic indicators.

[0069] Step S302: clustering the link nodes according to a preset clustering algorithm, and setting the corresponding link nodes as the fault nodes of the current application according to the result of the clustering.

[0070] Optionally, after the target fault delimitation characteristic indicator is determined through volatility detection, the present application can also use a preset clustering algorithm (such as DBSCAN) to cluster the target fault delimitation characteristic indicator. Specifically, link nodes (such as their containers or templates) that have an association relationship (such as a subordinate relationship) with the target fault delimitation characteristic indicator can be clustered.

[0071] Taking containers as an example, the present application can find out the relationship between containers of each target fault demarcation feature indicator based on the deployment link topology relationship in the log and cluster them into one category; in addition, the clustering operation can also be, for example, containers on the same host machine or belonging to the same template, a cluster, etc., thereby determining the link node obtained by clustering as the fault node of the current application to complete the fault demarcation operation.

[0072] In order to effectively improve the efficiency and accuracy of application fault demarcation, the present application provides an embodiment of an application fault demarcation device for implementing all or part of the application fault demarcation method, see Figure 4 The application fault demarcation device specifically includes the following contents:

[0073] The characteristic index correlation analysis module 10 is used to perform correlation analysis on the characteristic index data of the application service node and the characteristic index data of the application sub-node of the current application respectively, and determine the fault delimitation characteristic index of the current application.

[0074] The characteristic index volatility detection module 20 is used to perform volatility detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic indicator.

[0075] The characteristic index clustering processing module 30 is used to perform clustering processing on each of the target fault delimiting characteristic indexes according to a preset clustering algorithm to determine the fault node of the current application.

[0076] From the above description, it can be seen that the application fault delimitation device provided in the embodiment of the present application can determine the fault delimitation characteristic indicators 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; perform volatility detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine the target fault delimitation characteristic indicator; perform clustering processing on each of the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application, thereby effectively improving the efficiency and accuracy of application fault delimitation.

[0077] In order to accurately determine the fault delimitation characteristic index among the application characteristic indexes, in one embodiment of the application fault delimitation device of the present application, see Figure 5 , the characteristic index correlation analysis module 10 includes:

[0078] The association rule mining and analysis unit 11 is used to perform correlation analysis on the characteristic indicator data of each application service node and each application sub-node of the current application according to a preset association rule mining model, and determine the target application service node characteristic indicator and the target application sub-node characteristic indicator corresponding to the obtained frequent item set as the fault delimitation characteristic indicator of the current application.

[0079] In order to accurately determine the fault delimitation characteristic index among the application characteristic indexes, in one embodiment of the application fault delimitation device of the present application, see Figure 6 , the characteristic index volatility detection module 20 includes:

[0080] The historical data abnormality ratio determination unit 21 is used to determine the historical data abnormality ratio of each of the fault delimitation characteristic indicators according to a preset abnormal data calibration value.

[0081] The abnormal fluctuation detection unit 22 is used to determine the abnormal data corresponding to the historical data of each fault delimitation characteristic indicator within a set time window according to the abnormal proportion of the historical data and a preset abnormal detection algorithm. If the abnormal data exceeds a preset abnormal data threshold, the corresponding fault delimitation characteristic indicator is set as a target fault delimitation characteristic indicator.

[0082] In order to accurately determine the fault delimitation characteristic index among the application characteristic indexes, in one embodiment of the application fault delimitation device of the present application, see Figure 7 , the characteristic index clustering processing module 30 includes:

[0083] The association relationship determination unit 31 is used to determine the link nodes that have an association relationship with each of the target fault demarcation characteristic indicators.

[0084] The fault node determination unit 32 is used to perform clustering processing on the link nodes according to a preset clustering algorithm, and set the corresponding link node as the fault node of the current application according to the result of the clustering processing.

[0085] From a hardware perspective, in order to effectively improve the efficiency and accuracy of application fault demarcation, the present application provides an embodiment of an electronic device for implementing all or part of the content of the application fault demarcation method, and the electronic device specifically includes the following content:

[0086] A processor, a memory, a communications interface and a bus; wherein the processor, the memory and the communications interface communicate with each other through the bus; the communications interface is used to realize information transmission between the application fault demarcation device and related devices such as the core business system, the user terminal and the related database; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the application fault demarcation method and the embodiment of the application fault demarcation device in the embodiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0087] It is understandable 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), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0088] In practical applications, part of the application fault delimitation method can be executed on the electronic device side as described above, or all operations can be completed in the client device. The selection can be made based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0089] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0090] Figure 8 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 8 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Figure 8 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0091] In one embodiment, the application fault delimitation method function may be integrated into the central processing unit 9100. The central processing unit 9100 may be configured to perform the following control:

[0092] Step S101: performing correlation analysis on the application service node characteristic indicator data and the application sub-node characteristic indicator data of the current application respectively, and determining the fault delimitation characteristic indicator of the current application.

[0093] Step S102: performing fluctuation detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic indicator.

[0094] Step S103: clustering the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application.

[0095] From the above description, it can be seen that the electronic device provided in the embodiment of the present application determines the fault delimitation characteristic indicators 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; performs volatility detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine the target fault delimitation characteristic indicator; clusters each of the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application, thereby effectively improving the efficiency and accuracy of application fault delimitation.

[0096] In another embodiment, the application fault delimiting device may be configured separately from the central processing unit 9100. For example, the application fault delimiting device may be configured as a chip connected to the central processing unit 9100, and the application fault delimiting method function may be implemented under the control of the central processing unit.

[0097] like Figure 8 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Figure 8 In addition, the electronic device 9600 may also include Figure 8 For components not shown, reference may be made to the prior art.

[0098] like Figure 8 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0099] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0100] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0101] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0102] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0103] The communication module 9110 is a transmitter / receiver 9110 that sends 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 the case of a conventional mobile communication terminal.

[0104] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module, etc. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0105] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the method for delimiting an application fault in the above embodiment, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all the steps of the method for delimiting an application fault in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0106] Step S101: performing correlation analysis on the application service node characteristic indicator data and the application sub-node characteristic indicator data of the current application respectively, and determining the fault delimitation characteristic indicator of the current application.

[0107] Step S102: performing fluctuation detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic indicator.

[0108] Step S103: clustering the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application.

[0109] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application determines the fault delimitation characteristic indicators 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; performs volatility detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine the target fault delimitation characteristic indicator; clusters each of the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application, thereby effectively improving the efficiency and accuracy of application fault delimitation.

[0110] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0114] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for demarcating an application fault, characterized in that: The method comprises: Respectively performing correlation analysis on the application service node characteristic indicator data and the application sub-node characteristic indicator data of the current application to determine the fault delimitation characteristic indicator of the current application; Performing fluctuation detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic indicator; Clustering the target fault demarcation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application; The volatility detection is based on the year-on-year historical data to characterize the volatility or based on the month-on-month historical data to characterize the volatility; The performing of fluctuation detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine the target fault delimitation characteristic indicator includes: Determine the abnormal proportion of historical data of each fault delimitation characteristic indicator according to a preset abnormal data calibration value; The abnormal data corresponding to the historical data of each fault delimitation feature indicator within a set time window is determined according to the abnormal proportion of the historical data and a preset abnormality detection algorithm. If the abnormal data exceeds a preset abnormal data threshold, the corresponding fault delimitation feature indicator is set as a target fault delimitation feature indicator.

2. The application fault demarcation method according to claim 1, characterized in that: 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 delimitation characteristic index of the current application includes: According to the preset association rule mining model, correlation analysis is performed on the characteristic indicator data of each application service node and each application sub-node of the current application, and the target application service node characteristic indicator and the target application sub-node characteristic indicator corresponding to the obtained frequent item set are determined as the fault delimitation characteristic indicator of the current application.

3. The application fault demarcation method according to claim 1, characterized in that: The clustering process is performed on each of the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application, including: Determining a link node associated with each of the target fault demarcation characteristic indicators; The link nodes are clustered according to a preset clustering algorithm, and the corresponding link nodes are set as the fault nodes of the current application according to the result of the clustering.

4. An application fault demarcation device, characterized in that: include: A characteristic index correlation analysis module, used to respectively perform correlation analysis on the characteristic index data of the application service node and the characteristic index data of the application subnode of the current application, and determine the fault delimitation characteristic index of the current application; A characteristic indicator volatility detection module is used to perform volatility detection on the historical data of each of the fault delimitation characteristic indicators according to a preset anomaly detection algorithm to determine a target fault delimitation characteristic indicator; A characteristic index clustering processing module, used to perform clustering processing on each of the target fault delimitation characteristic indicators according to a preset clustering algorithm to determine the fault node of the current application; The volatility detection is based on the year-on-year historical data to characterize the volatility or based on the month-on-month historical data to characterize the volatility; The characteristic index volatility detection module includes: A historical data abnormality ratio determination unit, used to determine the historical data abnormality ratio of each of the fault delimitation characteristic indicators according to a preset abnormal data calibration value; The abnormal fluctuation detection unit is used to determine the abnormal data corresponding to the historical data of each fault delimitation characteristic indicator within a set time window according to the abnormal proportion of the historical data and a preset abnormal detection algorithm. If the abnormal data exceeds a preset abnormal data threshold, the corresponding fault delimitation characteristic indicator is set as a target fault delimitation characteristic indicator.

5. The application fault demarcation device according to claim 4, characterized in that: The characteristic index correlation analysis module includes: The association rule mining and analysis unit is used to perform correlation analysis on the characteristic indicator data of each application service node and each application sub-node of the current application according to a preset association rule mining model, and determine the target application service node characteristic indicator and the target application sub-node characteristic indicator corresponding to the obtained frequent item set as the fault delimitation characteristic indicator of the current application.

6. The application fault demarcation device according to claim 4, characterized in that: The characteristic index clustering processing module includes: An association relationship determination unit, used to determine a link node having an association relationship with each of the target fault delimitation characteristic indicators; The fault node determination unit is used to perform clustering processing on the link nodes according to a preset clustering algorithm, and set the corresponding link node as the fault node of the current application according to the result of the clustering processing.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the application fault demarcation method according to any one of claims 1 to 3 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the application fault demarcation method according to any one of claims 1 to 3 are implemented.

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