Method and device for allocating graded performance capacity

Abstract

The present invention provides a hierarchical performance capacity allocation method and device, which relates to the field of system maintenance and can be applied to the financial field and other fields. The method comprises: collecting performance capacity data of a target system and performance performance data involved in a transaction link; analyzing the performance capacity data through a predetermined node performance analysis model to obtain a process trigger signal, calling the performance capacity data and the link analysis model predetermined by the performance performance data through the process trigger signal to obtain analysis trend data; bringing the process trigger signal and the analysis trend data into a predetermined solution decision tree model for analysis to obtain a set of emergency disposal solutions; calculating the weight values ​​of each solution data in the solution set according to an impact risk weight calculation model, and converting the corresponding solution data into an execution script through the weight value; sending the execution script to the corresponding target node to execute the change operation.

Description

Technical Field

[0001] The present invention relates to the field of system maintenance, and can be applied to the financial field and other fields, and in particular to a hierarchical performance capacity allocation method and device. Background Art

[0002] With the continuous enrichment of banking business and the surge in the number of users brought by Internet finance, the scale of banking systems has continued to expand, and there are more and more performance and capacity problems. It is impossible to fully cover all programs by relying solely on performance testing. The scope of performance testing is getting smaller and smaller relative to the actual production and operation system. It is difficult to avoid systems and functions with performance and capacity defects from flowing into production. This patent hopes to improve the ability to discover and deal with problems through the technical methods of testing, combined with the intelligence and automation of production and operation. In response to the above problems, there are currently the following difficulties: 1. The causes of performance and capacity problems are complex, involving various hardware devices and software systems through which data flows inside and outside the system. 2. The performance and capacity monitoring of traditional production and operation often focuses on the performance of a single node and lacks systematic analysis and decision-making. 3. The efficiency of manual data collection, analysis, and decision-making is low, and emergency response takes a long time. Summary of the invention

[0003] The purpose of the present invention is to provide a hierarchical performance capacity allocation method and device, which divides the data flowing in from the outside into different processing layers, and then sets different monitoring items according to the technical characteristics and performance capacity performance of each layer, analyzes the performance capacity data of the system through an expert model, obtains the scope of the problem and possible solutions, selects the optimal solution through a decision tree, and automatically implements the emergency plan. Or it is combined with alarm feedback to the operation and maintenance personnel, who implement emergency disposal of production performance capacity events.

[0004] To achieve the above-mentioned purpose, the hierarchical performance capacity allocation method provided by the present invention specifically includes: collecting the performance capacity data of the target system and the performance performance data involved in the transaction link; analyzing the performance capacity data through a predetermined node performance analysis model to obtain a process trigger signal, calling the performance capacity data and the link analysis model predetermined by the performance performance data through the process trigger signal to obtain analysis trend data; bringing the process trigger signal and the analysis trend data into a predetermined solution decision tree model for analysis to obtain a set of emergency response solutions; calculating the weight value of each solution data in the solution set according to the risk weight calculation model, and converting the corresponding solution data into an execution script through the weight value; sending the execution script to the corresponding target node to execute the change operation.

[0005] In the above-mentioned hierarchical performance capacity allocation method, preferably, collecting the performance capacity data of the target system includes: collecting the performance capacity monitoring data of the current node through the Agent deployed on each node in the target system; and generating performance capacity data based on the performance capacity monitoring data of each node.

[0006] In the above-mentioned hierarchical performance capacity allocation method, preferably, collecting performance data related to the transaction link includes: collecting topology data, service data and operation data of the transaction link through distributed tracking calculation; generating performance data according to the topology data, the service data and the operation data.

[0007] In the above-mentioned hierarchical performance capacity allocation method, preferably, analyzing the performance capacity data through a predetermined node performance analysis model to obtain a process trigger signal includes: quantitatively analyzing the performance capacity data in a real-time stream manner to obtain a performance value; comparing the performance value with a preset threshold, and generating a process trigger signal based on the comparison result.

[0008] In the above-mentioned hierarchical performance capacity allocation method, preferably, generating a process trigger signal based on the comparison result also includes: aggregating the performance values ​​that exceed the preset threshold value to obtain an aggregation result, and generating a trigger alarm signal and / or a process trigger signal according to the aggregation result and the preset importance corresponding to the performance value.

[0009] In the above-mentioned hierarchical performance capacity allocation method, preferably, the performance capacity data and the performance performance data are called through the process trigger signal to obtain analysis trend data through a predetermined link analysis model analysis, which includes: obtaining the node information and service information of the target transaction data flow according to the process trigger signal and the performance performance data analysis; obtaining the service range and node range according to the transaction information, the node information and the transaction link information in the performance performance data analysis; extracting the service range and the node range according to a preset time period to generate analysis trend data.

[0010] In the above-mentioned hierarchical performance capacity allocation method, preferably, the process trigger signal and the analysis trend data are brought into a predetermined solution decision tree model for analysis to obtain a set of emergency disposal solutions, which includes: bringing the process trigger signal and the analysis trend data into a predetermined solution decision tree model, and analyzing one by one according to the input elements from the termination node of the transaction call chain forward according to the root cause analysis principle to obtain a set of emergency disposal solutions; or, analyzing one by one according to the input elements from the starting node of the call chain backward according to the disposal analysis principle to obtain a set of emergency disposal solutions.

[0011] The present invention also provides a hierarchical performance capacity allocation device, which includes a monitoring module, a single-node analysis module, a link analysis module and an emergency response module; the monitoring module is used to collect performance capacity data of the target system and performance performance data involved in the transaction link; the single-node analysis module is used to analyze the performance capacity data through a predetermined node performance analysis model to obtain a process trigger signal; the link analysis module is used to call the performance capacity data and the link analysis model predetermined by the performance performance data through the process trigger signal to obtain analysis trend data; the emergency response module is used to bring the process trigger signal and the analysis trend data into a predetermined solution decision tree model for analysis to obtain a set of emergency response solutions; the weight values ​​of each solution data in the solution set are calculated according to the risk weight calculation model, and the corresponding solution data is converted into an execution script through the weight value; the execution script is sent to the corresponding target node to execute the change operation.

[0012] The present invention also 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 above method when executing the computer program.

[0013] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for executing the above method.

[0014] The beneficial technical effects of the present invention are as follows: the causes of performance and capacity problems are complex, and manual analysis relies on human technical capabilities. By aggregating and correlating separated monitoring data, it is possible to quickly locate the root cause and scope of the problem, improve handling efficiency, and reduce the impact of the problem. Standardize the changes in emergency disposal, solidify production emergency strategies through the emergency plan library, and improve the overall operation and maintenance capabilities and levels of the system. Reduce personnel intervention in the implementation of emergency plans, ensure the transparency and controllability of the process, and avoid operational risks. The efficiency of manual execution of emergency disposal changes is low, and the risks of most capacity expansions and current limiting adjustments within the range are low, and it has a great positive effect on alleviating production pressure and avoiding systemic risks. Efficient emergency disposal in an automated manner can greatly reduce the impact time and scope of performance and capacity problems and improve risk prevention and control capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of the present application, and do not constitute a limitation of the present invention. In the drawings:

[0016] Figure 1 A schematic diagram of a process flow of a hierarchical performance capacity allocation method provided by an embodiment of the present invention;

[0017] Figure 2A schematic diagram of a process flow for generating a process trigger signal provided by an embodiment of the present invention;

[0018] Figure 3 A schematic diagram of a generation process of analysis trend data provided by an embodiment of the present invention;

[0019] Figure 4 A schematic diagram of the application process of the hierarchical performance capacity allocation method provided by an embodiment of the present invention;

[0020] Figure 5 A schematic diagram of a structure of a hierarchical performance capacity allocation device provided in an embodiment of the present invention

[0021] Figure 6 A schematic diagram of the application structure of a hierarchical performance capacity allocation device provided in an embodiment of the present invention

[0022] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will describe the implementation methods of the present invention in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present invention applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that as long as there is no conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other, and the technical solutions formed are all within the protection scope of the present invention.

[0024] In addition, the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be performed in an order different from that shown here.

[0025] Please refer to Figure 1 As shown, the hierarchical performance capacity allocation method provided by the present invention specifically includes:

[0026] S101 collects performance capacity data of the target system and performance data involved in the transaction link;

[0027] S102: Analyze the performance capacity data through a predetermined node performance analysis model to obtain a process trigger signal, and call the link analysis model predetermined by the performance capacity data and the performance data through the process trigger signal to obtain analysis trend data;

[0028] S103: Bring the process trigger signal and the analysis trend data into a predetermined solution decision tree model for analysis to obtain a set of emergency response solutions;

[0029] S104 calculates the weight value of each solution data in the solution set according to the impact risk weight calculation model, and converts the corresponding solution data into an execution script according to the weight value;

[0030] S105 sends the execution script to the corresponding target node to execute the change operation.

[0031] In the above embodiment, collecting the performance capacity data of the target system includes: collecting the performance capacity monitoring data of the current node through the Agent deployed on each node in the target system; and generating performance capacity data based on the performance capacity monitoring data of each node. Collecting the performance data involved in the transaction link may include: collecting the topology data, service data and operation data of the transaction link through distributed tracking calculation; generating performance data based on the topology data, the service data and the operation data. In actual work, step S101 mainly uses existing system tools to collect and archive relevant information of performance counters at the operating system level (for example: CPU utilization, memory utilization, IO throughput, IOPS, network traffic, number of network connections); and collect and archive relevant performance data of application servers (taking JAVA as an example, mainly collecting JVM memory, CPU utilization, GC frequency, and GC time consumption).

[0032] Please refer to Figure 2 As shown, in one embodiment of the present invention, analyzing the performance capacity data by a predetermined node performance analysis model to obtain a process trigger signal may include:

[0033] S201 quantitatively analyzes the performance capacity data in a real-time streaming manner to obtain a performance value;

[0034] S202 compares the performance value with a preset threshold, and generates a process trigger signal according to the comparison result.

[0035] Furthermore, in the above embodiment, generating a process trigger signal according to the comparison result may also include: aggregating the performance values ​​that exceed the preset threshold to obtain an aggregation result, and generating a trigger alarm signal and / or a process trigger signal according to the preset importance corresponding to the aggregation result and the performance value. Specifically, under normal circumstances, the content of a single indicator is of low importance and only triggers an alarm. For example, an alarm is directly triggered when the operating system CPU usage is higher than the threshold of 70%. Indicators that are jointly analyzed after aggregating multiple indicators are generally of high importance and trigger subsequent emergency processes. For example, if the indicator "(database dbtime / monitoring duration) / (number of server CPUs*server CPU usage)" is greater than the threshold 3, the subsequent emergency process is triggered (SQL statements with higher dbtime are truncated).

[0036] Please refer to Figure 3As shown, in one embodiment of the present invention, calling the performance capacity data and the performance performance data through the process trigger signal to obtain analysis trend data through a predetermined link analysis model includes:

[0037] S301 obtains node information and service information of target transaction data flow according to the process trigger signal and the performance data analysis;

[0038] S302 obtains a service scope and a node scope according to the transaction information, the node information and the transaction link information in the performance data through analysis;

[0039] S303 extracts the service range and the node range according to a preset time period to generate analysis trend data.

[0040] In actual work, it is mainly to determine the scope of service dimensions and node dimensions that may have performance impacts, i.e., node information and service information, by combining the call chain and node association relationship topology tracked and generated in the performance data; then, based on the node information and service information, a trend analysis is performed on the corresponding services and nodes through a single node analysis module, and the analysis results of comparison and trend changes, i.e., analysis trend data, are generated according to the dimensions of different historical time periods of 1 minute, 5 minutes, 30 minutes, and 60 minutes.

[0041] In one embodiment of the present invention, the process trigger signal and the analysis trend data are brought into the predetermined solution decision tree analysis to obtain the emergency disposal solution set, which may include: bringing the process trigger signal and the analysis trend data into the predetermined solution decision tree model, and analyzing the emergency disposal solution set according to the input elements one by one according to the root cause analysis principle from the end node of the transaction call chain forward; or, analyzing the emergency disposal solution set according to the input elements one by one according to the disposal analysis principle from the start node of the call chain backward. In actual work, the decision tree analysis model can be used to analyze the input elements one by one according to the principle of root cause analysis from the end of the call chain forward and disposal mode from the beginning of the call chain backward to generate the emergency disposal solution set. Thereafter, the weight calculation is performed according to the impact degree and implementation risk (the factors affecting the weight mainly include: the degree of change compared with the current situation, the greater the weight, the higher the weight; the importance of transactions and nodes; the maturity of emergency solutions, etc.) If the weight calculation is small and does not exceed the threshold, it can directly enter the subsequent step S105 for processing. If the weight calculation is large and exceeds the threshold, human intervention and approval are required, and after approval, it will be processed by step S105.

[0042] In order to more clearly understand the specific application methods of the above embodiments provided by the present invention, please refer to the following Figure 4 As an example, the above embodiments are described in combination with the real-time method. Those skilled in the art should know that this description does not limit it in any way.

[0043] S401 traditional performance capacity monitoring data collection, through the Agent deployed on the node, collects the performance capacity monitoring information at the system level, application service level, and data access level. It is sent to the analysis module in the form of streaming data and archived in the form of files through data compression.

[0044] S402 distributed tracing is based on the Span model, Trace technology, and log management system. It outputs the topology of transaction links and node associations, as well as the performance of services and operations, and archives them in the form of logs.

[0045] The S403 node performance analysis model receives the data stream from S401 and performs quantitative analysis through real-time stream computing, which is currently mainly based on threshold analysis. The monitoring results that exceed the threshold are aggregated and combined with the importance of the indicators to choose whether to trigger only an alarm or directly trigger an emergency process. The triggered emergency process is sent to S404 and S406 based on the node and transaction information generated by S401.

[0046] The link analysis model S404 receives the node transaction information sent by S403, combines the call chain and node association relationship topology generated by the distributed tracking S402, determines the scope of the service dimension and node dimension that may have performance impact, and sends the service and node information within the scope to S405 and S406.

[0047] S405: The node performance trend analysis model receives the service and node information sent by S404, performs trend analysis on the corresponding services and nodes through the single node analysis module, generates comparison and trend change analysis results according to the dimensions of different historical time periods of 1 minute, 5 minutes, 30 minutes, and 60 minutes, and sends them to S406.

[0048] The S406 solution decision tree analysis model receives the analysis results sent by S403, S404, and S405. Through the decision tree analysis model, according to the principle of root cause analysis from the end of the call chain forward and disposal mode from the beginning of the call chain backward, it analyzes the input elements one by one to generate a set of emergency disposal solutions. The weight is calculated according to the degree of impact and implementation risk (the factors affecting the weight mainly include: the degree of change compared with the current situation, the greater the weight, the higher the weight; the importance of transactions and nodes; the maturity of emergency plans, etc.) The weight calculation is small and does not exceed the threshold, which can be directly transferred to S407. The weight calculation is large and exceeds the threshold. It requires human intervention and approval, and flows into S407 after passing.

[0049] S407 generates emergency response plans. According to the emergency response plan set generated in S406, the corresponding technical solutions and tools are obtained from the emergency solution library. A technical change script that can be automatically executed by the execution unit is generated. Submit to S408.

[0050] S408 emergency plan execution, receives the technical change script submitted by S407, publishes the task to the target node of the change through the automatic scheduling module, executes the change script, and feeds back the change result to the plan decision unit to display the implementation result of the emergency plan.

[0051] It is worth noting that the above-mentioned model provided in this application can adopt a learning model, a fitting model or other classification model constructed by the machine learning method in the prior art. The present invention does not make any limitation here, and relevant technical personnel in this field can choose to use it according to actual needs.

[0052] Please refer to Figure 5 As shown, the present invention also provides a hierarchical performance capacity allocation device, which includes a monitoring module, a single-node analysis module, a link analysis module and an emergency response module; the monitoring module is used to collect the performance capacity data of the target system and the performance performance data involved in the transaction link; the single-node analysis module is used to analyze the performance capacity data through a predetermined node performance analysis model to obtain a process trigger signal; the link analysis module is used to call the performance capacity data and the link analysis model predetermined by the performance performance data through the process trigger signal to obtain analysis trend data; the emergency response module is used to bring the process trigger signal and the analysis trend data into a predetermined solution decision tree model for analysis to obtain a set of emergency response solutions; the weight values ​​of each solution data in the solution set are calculated according to the risk weight calculation model, and the corresponding solution data is converted into an execution script through the weight value; the execution script is sent to the corresponding target node to execute the change operation.

[0053] In actual work, please refer to the specific structure of each module in the hierarchical performance capacity allocation device provided by the present invention. Figure 6 As shown, the monitoring module can be subdivided into two parts: the traditional performance capacity monitoring module and the distributed tracking module; the single node analysis module can further include a node analysis unit 007 and a transaction analysis unit 008, and the link analysis module can include a transaction link analysis unit 009 and an associated node analysis unit 010; the emergency response module can include a solution decision unit 011, a solution generation unit 012, an execution unit 014 and an emergency solution library 013; specifically:

[0054] The monitoring module includes: 001 is the system layer monitoring unit, which is mainly used to collect and archive relevant information of the performance counters at the operating system level (for example: CPU usage, memory usage, IO throughput, IOPS, network traffic, number of network connections). 002 is the application layer monitoring unit, which is mainly used to collect and archive relevant performance data of the application server (taking JAVA as an example, it mainly collects JVM memory, CPU usage, GC frequency, and GC time consumption). 003 is the data access layer monitoring unit, which is mainly used to collect and archive database performance data (taking relational database as an example, it mainly collects DB time, Buffer Hit and other performance indicators). 004 is the link tracking unit, which mainly collects and stores the call information of the link through the span model, and summarizes the call information of the link into the topology of the node relationship. 005 is the service tracking unit, which tracks and monitors the upstream and downstream call relationships of the service based on the span model. 006 is the operation tracking unit, which associates operations with services and links based on Trace technology to form a more fine-grained statement-level tracking.

[0055] The single node analysis module includes: 007 is a node analysis unit, which analyzes the monitoring data output in 001, 002, and 003. The current analysis mainly includes two models: Model 1: triggering alarms and emergency processes through threshold judgment. Model 2: judging nodes and levels that may have performance and capacity problems through trend analysis. 008 is a transaction analysis unit, which mainly determines transactions with performance and capacity problems based on the application logs output in 002 and the monitoring of the compiler.

[0056] The link analysis module includes: 009 is a transaction link analysis unit, which analyzes the nodes and services through which data flows on the transaction link according to the problem transaction output by the transaction analysis unit 008 and the upstream and downstream relationship of the service output by 005. 010 is an associated node analysis unit, which analyzes the nodes that may be affected according to the node alarm information output by the node analysis unit 007 and the node relationship topology of 004, and then performs trend analysis on the possible nodes through 007 and returns the analysis results.

[0057] The emergency response module includes: 011 is the solution decision unit, which generates an emergency plan through the decision tree based on the analysis results input by 009 and 010. 012 is the solution generation unit, which returns the emergency response tools of the corresponding nodes and levels from the emergency solution library according to the emergency plan input by 011, and sends them to the execution unit. 013 is the emergency solution library, which manages emergency response plans and tools for different application systems, different nodes, and different levels. 014 is the performance unit, which receives the emergency plans and tools generated by the previous item, deploys them to the corresponding nodes, implements technical changes and returns the implementation results.

[0058] The beneficial technical effects of the present invention are as follows: the causes of performance and capacity problems are complex, and manual analysis relies on human technical capabilities. By aggregating and correlating separated monitoring data, it is possible to quickly locate the root cause and scope of the problem, improve handling efficiency, and reduce the impact of the problem. Standardize the changes in emergency disposal, solidify production emergency strategies through the emergency plan library, and improve the overall operation and maintenance capabilities and levels of the system. Reduce personnel intervention in the implementation of emergency plans, ensure the transparency and controllability of the process, and avoid operational risks. The efficiency of manual execution of emergency disposal changes is low, and the risks of most capacity expansions and current limiting adjustments within the range are low, and it has a great positive effect on alleviating production pressure and avoiding systemic risks. Efficient emergency disposal in an automated manner can greatly reduce the impact time and scope of performance and capacity problems and improve risk prevention and control capabilities.

[0059] The present invention also 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 above method when executing the computer program.

[0060] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for executing the above method.

[0061] like Figure 7 As shown, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily have to include Figure 7 In addition, the electronic device 600 may also include Figure 7 For components not shown, reference may be made to the prior art.

[0062] like Figure 7 As shown, the central processor 100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The central processor 100 receives inputs and controls the operations of various components of the electronic device 600.

[0063] The memory 140 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 100 may execute the program stored in the memory 140 to implement information storage or processing.

[0064] The input unit 120 provides input to the CPU 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0065] The memory 140 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 140 may also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 600 through the central processor 100.

[0066] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 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.).

[0067] The communication module 110 is a transmitter / receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter / receiver) 110 is coupled to the central processor 100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0068] Based on different communication technologies, multiple communication modules 110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module (transmitter / receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide an audio output via the speaker 131 and receive an audio input from the microphone 132, thereby realizing a common telecommunication function. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 130 is also coupled to the central processor 100, so that recording can be performed on the local machine through the microphone 132, and the sound stored on the local machine can be played through the speaker 131.

[0069] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, 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.

[0070] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), 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 produce 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.

[0071] 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.

[0072] 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 The steps for the functions specified in one or more boxes.

[0073] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A hierarchical performance capacity allocation method, characterized in that: The method comprises: Collect performance capacity data of the target system and performance data involved in the transaction link; Analyzing the performance capacity data through a predetermined node performance analysis model to obtain a process trigger signal, and calling the link analysis model predetermined for the performance capacity data and the performance data through the process trigger signal to obtain analysis trend data; Bringing the process trigger signal and the analysis trend data into a predetermined solution decision tree model for analysis to obtain a set of emergency response solutions; Calculate the weight value of each solution data in the solution set according to the risk weight calculation model, and convert the corresponding solution data into an execution script according to the weight value; The execution script is sent to the corresponding target node to execute the change operation.

2. The hierarchical performance capacity allocation method according to claim 1, characterized in that: The performance and capacity data of the target system to be collected includes: The Agent deployed on each node in the target system collects the performance and capacity monitoring data of the current node; The performance capacity data is generated based on the performance capacity monitoring data of each node.

3. The hierarchical performance capacity allocation method according to claim 1, characterized in that: The performance data involved in the transaction link collection includes: Collect topological data, service data, and operation data of transaction links through distributed tracing calculations; Performance data is generated based on the topology data, the service data, and the operational data.

4. The hierarchical performance capacity allocation method according to claim 1, characterized in that: Analyzing the performance capacity data by a predetermined node performance analysis model to obtain a process trigger signal includes: Quantitatively analyzing the performance capacity data in a real-time streaming manner to obtain a performance value; The performance value is compared with a preset threshold value, and a process trigger signal is generated according to the comparison result.

5. The hierarchical performance capacity allocation method according to claim 4, characterized in that: Generating a process trigger signal according to the comparison result also includes: The performance values ​​exceeding a preset threshold are aggregated to obtain an aggregation result, and a trigger alarm signal and / or a process trigger signal is generated according to the aggregation result and a preset importance corresponding to the performance value.

6. The hierarchical performance capacity allocation method according to claim 1, characterized in that: The process trigger signal is used to call the performance capacity data and the performance performance data and obtain the analysis trend data through a predetermined link analysis model analysis, including: Obtaining node information and service information of the target transaction data flow according to the process trigger signal and the performance data analysis; Obtaining a service scope and a node scope according to the transaction information, the node information and the transaction link information in the performance data; The service range and the node range are extracted according to a preset time period to generate analysis trend data.

7. The hierarchical performance capacity allocation method according to claim 1, characterized in that: Bringing the process trigger signal and the analysis trend data into a predetermined solution decision tree for analysis to obtain an emergency response solution set includes: Bring the process trigger signal and the analysis trend data into the predetermined solution decision tree model, and analyze the input elements one by one from the termination node of the transaction call chain forward according to the root cause analysis principle to obtain a set of emergency disposal solutions; Or, according to the disposal analysis principle, start from the starting node of the call chain and work backwards, and analyze the input elements one by one to obtain a set of emergency disposal solutions.

8. A hierarchical performance capacity allocation device, characterized in that: The device comprises a monitoring module, a single node analysis module, a link analysis module and an emergency handling module; The monitoring module is used to collect performance capacity data of the target system and performance data related to the transaction link; The single node analysis module is used to analyze the performance capacity data through a predetermined node performance analysis model to obtain a process trigger signal; The link analysis module is used to call the link analysis model predetermined by the performance capacity data and the performance performance data through the process trigger signal to analyze and obtain analysis trend data; The emergency response module is used to bring the process trigger signal and the analysis trend data into a predetermined solution decision tree model for analysis to obtain a set of emergency response solutions; calculate the weight value of each solution data in the solution set according to the risk weight calculation model, and convert the corresponding solution data into an execution script according to the weight value; The execution script is sent to the corresponding target node to execute the change operation.

9. 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 computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for causing a computer to execute the method according to any one of claims 1 to 7.

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