Real-time performance monitoring and self-optimizing system based on Java application program

Abstract

The invention provides a real-time performance monitoring and self-optimizing system based on a Java application program, belongs to the field of computer software development and performance optimization, introduces an intelligent analysis and self-adaptive optimization technology on the basis of traditional performance monitoring, and can monitor and automatically optimize the performance of the Java application program in real time. Through real-time collection, analysis and intelligent optimization of performance data during operation of the application program, automatic resource management, performance adjustment and load balancing are realized, so that the system performance, stability and resource utilization rate are improved to the maximum extent, and the workload of developers is also reduced.

Description

Technical Field

[0001] The present invention relates to the field of computer software development and performance optimization, and particularly to a real-time performance monitoring and self-optimization system based on Java applications. Background Art

[0002] With the continuous expansion of the scale of applications and the increase in load, Java applications will face various performance problems and bottlenecks during operation, such as memory leaks, resource contention, overuse of thread pools, insufficient or overconfigured database connection pools, etc. These problems often have a greater impact on the system's response time, throughput, stability, and scalability. Especially in highly concurrent and distributed environments, how to effectively monitor and optimize the performance of applications has become a key challenge that developers and operators urgently need to solve. Traditional performance monitoring tools and optimization solutions are often limited to static monitoring and manual intervention, and cannot automatically perform adaptive optimization and adjustment based on real-time data and load conditions. For example, in many existing Java application monitoring systems, users need to manually set fixed resource configuration parameters, and when performance bottlenecks occur, they often rely on developers or operators to manually troubleshoot, diagnose, and optimize, which is time-consuming and error-prone. In addition, most monitoring systems only count or alarm the usage of resources, and lack the ability to adaptively adjust resource configuration and optimize execution strategies for applications, resulting in performance bottlenecks often not being effectively resolved in a timely manner. Summary of the Invention

[0003] To solve the above technical problems, the present invention provides a real-time performance monitoring and self-optimization system based on Java applications, which can not only monitor key performance indicators such as memory, CPU, thread pool, and database connection pool of Java applications in real time, but also automatically adjust resource configuration and execution strategies based on real-time monitored application running data and load changes to maximize the performance and resource utilization efficiency of the system.

[0004] The technical solution of the present invention is as follows:

[0005] A real-time performance monitoring and self-optimization system for Java applications includes:

[0006] A real-time performance monitoring module for collecting performance data of Java applications, including CPU usage rate, memory usage, thread pool status, and database connection pool status;

[0007] An intelligent analysis and decision-making engine for analyzing the performance data, identifying performance bottlenecks, and generating optimization strategies;

[0008] An automatic optimization module for automatically adjusting the resource configuration of a Java application according to the optimization strategy, including JVM memory, thread pool capacity, and database connection pool;

[0009] A real-time alert and reporting module for generating alert messages and notifying relevant personnel when performance data is abnormal, and generating performance reports regularly.

[0010] Furthermore,

[0011] Among them, the intelligent analysis and decision-making engine predicts the system load based on performance data and generates a load prediction result to adjust the resource configuration in advance.

[0012] The automatic optimization module automatically adjusts the JVM memory size, the core thread number and the maximum thread number of the thread pool to optimize the application performance.

[0013] When the performance data exceeds the threshold, the real-time alert and reporting module generates an alert and notifies relevant personnel by means of emails, text messages, etc.

[0014] Through real-time monitoring of the performance data of a Java application and in combination with an intelligent analysis and decision-making engine, the present invention automatically performs performance optimization and resource scheduling. Specifically as follows:

[0015] 1. System architecture

[0016] This system includes four core modules:

[0017] 1) Real-time performance monitoring module: responsible for collecting the performance data of a Java application, such as memory usage, CPU occupancy, thread pool status, and database connection pool status.

[0018] 2) Intelligent analysis and decision-making engine: Based on the real-time monitoring data analysis, the system load is analyzed, performance bottlenecks are identified, and an optimization strategy is generated.

[0019] 3) Automatic optimization module: Automatically adjusts the resource configuration according to the optimization strategy of the decision-making engine, such as JVM memory, thread pool size, and database connection pool.

[0020] 4) Real-time alert and reporting module: Generates alert messages in real time and generates performance reports regularly to help developers quickly respond to problems and continuously optimize.

[0021] 2. Real-time performance monitoring module

[0022] The real-time performance monitoring module regularly collects the runtime data of the application through the APIs provided by the JVM and the operating system, mainly including:

[0023] 1) Memory monitoring: Using the JMX interface to collect the usage of heap memory, non-heap memory, and the number of garbage collections to help identify memory bottlenecks.

[0024] 2) CPU usage monitoring: Monitor the CPU usage through the OperatingSystemMXBean interface and generate an alarm when overloaded.

[0025] 3) Thread pool monitoring: Monitor the number of active threads and the queue length of the thread pool. If the thread pool reaches the saturation state, trigger automatic adjustment.

[0026] 4) Database connection pool monitoring: Collect the status of the database connection pool and optimize the allocation and recycling of database connections.

[0027] 3. Intelligent Analysis and Decision-making Engine

[0028] The intelligent analysis and decision-making engine is based on real-time monitoring data to identify the performance bottlenecks of the system and generate optimization strategies:

[0029] 1) Load prediction: Predict future loads through time series analysis (such as ARIMA or LSTM) and make adjustments in advance.

[0030] 2) Bottleneck identification: Compare the real-time data with the threshold to identify resource bottlenecks such as CPU, memory, and thread pool.

[0031] 3) Optimization strategy generation: Generate optimization strategies for adjusting memory, thread pool, database connection pool, etc. according to the load situation.

[0032] 4. Automatic Optimization Module

[0033] The automatic optimization module automatically adjusts the system resource configuration according to the decisions of the analysis engine:

[0034] 1) Memory optimization: Automatically adjust the JVM heap memory size, GC policy, etc. according to the memory usage to reduce performance problems caused by memory overflow or GC.

[0035] 2) Thread pool optimization: Automatically increase or decrease the capacity of the thread pool to cope with load fluctuations and ensure that tasks can be executed in a timely manner.

[0036] 3) Database connection pool optimization: Automatically adjust the maximum number of connections according to the usage of the database connection pool to avoid overloading of database resources.

[0037] 5. Real-time Alarm and Reporting Module

[0038] The alarm and reporting module monitors the application performance in real time. Once an anomaly is detected, it immediately issues an alarm and notifies the operation and maintenance personnel. In addition, the system regularly generates performance reports to evaluate the optimization effect and help developers continuously optimize the system.

[0039] 1) Real-time alarm: When the performance metrics exceed the threshold, the system will send alarms via email, SMS, or message push.

[0040] 2) Regular report: Generate reports including performance trends, bottleneck analysis, and optimization results to facilitate subsequent optimization decisions.

[0041] The beneficial effects of the present invention are

[0042] The real-time performance monitoring and self-optimization system for Java applications of the present invention has the following innovative effects:

[0043] 1. Automatic optimization: Through intelligent analysis and adaptive optimization algorithms, the system can automatically identify performance bottlenecks and execute corresponding optimization tasks, reducing manual intervention and improving system performance.

[0044] 2. Dynamic adjustment: The system dynamically adjusts resource allocation according to the runtime load conditions, such as memory, thread pool, database connection pool, etc., avoiding resource waste or over-occupation and improving resource utilization.

[0045] 3. Real-time monitoring and feedback: The system can monitor various performance metrics in real time and send alarms in a timely manner when abnormalities occur to ensure the stable operation of the system.

[0046] 4. Efficient load management: Through load balancing optimization and dynamic resource scheduling, the system can maintain good performance in a high-concurrency environment and ensure fast response. Description of the Drawings

[0047] Figure 1 is a schematic diagram of the system architecture of the present invention;

[0048] Figure 2 is a schematic diagram of the work flow of the present invention. Detailed Embodiments

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0050] The present invention provides a real-time performance monitoring and self-optimization system for Java applications. Through the collaborative work of the following technical modules, the resource allocation and execution strategy of the application program are dynamically adjusted to achieve the purposes of optimizing performance, enhancing stability, and reducing manual intervention. Specifically, the present invention includes:

[0051] Real-time Performance Monitoring Module: Responsible for collecting various types of performance data of the application, including indicators such as CPU, memory, thread pool, database connection pool, etc., to ensure that the resource usage can be monitored in real time during the application running process;

[0052] Intelligent Analysis and Decision-making Module: Based on the real-time collected performance data, the system will perform real-time analysis on the application, identify potential bottlenecks and make resource allocation and scheduling decisions according to the preset optimization strategies.

[0053] Automatic Optimization Module: Automatically execute resource adjustment operations according to the analysis results, including memory adjustment, thread pool expansion and contraction, database connection pool scheduling, etc.

[0054] Real-time Alarm and Reporting Module: When an anomaly is detected, the system will generate an alarm in a timely manner and notify the developers. At the same time, detailed performance reports will be generated regularly to help the developers analyze the application performance trend.

[0055] Among them,

[0056] Real-time Performance Monitoring Module

[0057] The real-time performance monitoring module is the basic module of the system, responsible for collecting various types of performance data in real time and transmitting these data to the intelligent analysis and decision-making engine. The monitoring content includes but is not limited to the following aspects:

[0058] 1) Memory Monitoring

[0059] Monitor the usage of heap memory and non-heap memory. Use the tools provided by JVM (such as MemoryMXBean) to regularly collect data such as memory usage, garbage collection situation, and memory leak possibility.

[0060] When the heap memory usage exceeds the set threshold, the system will trigger the automatic garbage collection mechanism and automatically expand or contract the heap memory size according to the memory allocation situation.

[0061] 2) CPU Usage Monitoring

[0062] Through the JVM interface or the API at the operating system level, monitor the CPU occupancy of the application in real time. When the CPU occupancy is too high, the system can generate an alarm and further analyze whether it is a performance bottleneck caused by thread competition, deadlock, etc.

[0063] 3) Thread Pool Monitoring

[0064] Monitor the status of the thread pool in the application and collect data such as the number of active threads, queue size, and waiting time of each thread pool.

[0065] If the thread pool reaches its maximum number of threads or there are tasks in the queue that have not been processed for a long time, the system can automatically expand the thread pool or adjust the task scheduling strategy to ensure the high concurrency ability of the system.

[0066] 4) Database connection pool monitoring

[0067] Monitor the active connection count, idle connection count, connection pool fullness, etc. of the database connection pool. The system can dynamically adjust the size of the database connection pool to avoid waste or over - occupation of connection resources.

[0068] 5) Network request monitoring

[0069] Monitor metrics such as the response time and error rate of Web requests and API calls. Especially in high - concurrency situations, it can promptly detect performance bottlenecks in network requests and take corresponding optimization measures.

[0070] Intelligent analysis and decision - making module

[0071] The intelligent analysis and decision - making engine is the core part of the system, responsible for processing and analyzing the real - time collected performance data and making corresponding optimization decisions based on the analysis results. This engine uses the following analysis methods:

[0072] 1) Load analysis and trend prediction

[0073] Through retrospective analysis of historical data, the system can identify load fluctuation patterns and predict the load trend based on machine learning models (such as time - series prediction algorithms).

[0074] When there are significant fluctuations in the system load, the system can, based on the prediction model, adjust the resource configuration in advance to avoid system overload.

[0075] 2) Bottleneck identification and cause analysis

[0076] Based on the collected monitoring data and combined with the preset performance benchmark values, the system automatically identifies performance bottlenecks in the application program. For example, if it is found that the memory usage is approaching the upper limit or the thread pool is full, the system will automatically analyze the bottleneck cause and generate a report.

[0077] Based on bottleneck identification, the intelligent engine will select appropriate optimization strategies, such as expanding the heap memory, increasing the thread pool capacity, optimizing the database connection pool, etc.

[0078] 3) Adaptive optimization algorithms

[0079] Based on real - time data, load patterns, and bottleneck analysis, the system uses adaptive algorithms (such as reinforcement learning, genetic algorithms) to dynamically adjust resource allocation and scheduling strategies. These algorithms can automatically find the optimal configuration under different load scenarios.

[0080] 4) Strategy Adjustment and Execution Plan Generation

[0081] Based on the analysis results, the engine generates an execution plan and passes it to the automatic optimization module. This plan includes specific operation steps such as memory tuning, thread pool adjustment, and database connection pool optimization.

[0082] Automatic Optimization Module

[0083] The automatic optimization module receives instructions from the intelligent analysis and decision-making engine and executes a series of automated optimization tasks. The optimization content includes:

[0084] 1) Memory Management Optimization

[0085] Dynamically adjust the memory parameters of the JVM (such as -Xms, -Xmx) and the heap memory allocation strategy according to the memory usage. By enabling the garbage collector tuning mechanism, reduce memory fragmentation and improve memory utilization efficiency.

[0086] 2) Thread Pool Expansion and Contraction

[0087] Automatically expand or contract the size of the thread pool to adapt to different load conditions. The system will automatically increase the thread pool capacity when the load increases, reducing the overhead of thread switching; when the load is low, contract the thread pool to release system resources.

[0088] 3) Dynamic Adjustment of Database Connection Pool

[0089] Automatically adjust the maximum and minimum number of connections in the database connection pool to avoid excessive connection occupation or waste of connection resources. The system can also dynamically adjust the connection pool configuration according to the priority and response time of requests.

[0090] 4) Load Balancing Optimization

[0091] In high-concurrency scenarios, the system automatically adjusts the load balancing strategy to ensure that requests are reasonably distributed among multiple service instances, avoiding single-point overload.

[0092] Real-time Alarm and Reporting Module

[0093] This module is responsible for generating alarm information about the system status and notifying developers or operation and maintenance personnel. At the same time, it can also generate detailed performance reports to help developers understand the running status of the system.

[0094] 1) Alarm Generation and Push

[0095] When the system monitors an abnormal state (such as high CPU, memory overflow, thread pool saturation, etc.), it immediately generates an alarm and notifies developers or operation and maintenance personnel via email, SMS, WeChat, etc.

[0096] 2) Performance Report Generation

[0097] The system will regularly generate detailed performance reports, including trend analysis of various indicators, system bottlenecks, optimization measures and implementation results, etc., to help developers with subsequent optimization.

[0098] System Integration and Deployment

[0099] The present invention can be seamlessly integrated into existing Java applications. Through the embedded monitoring agent or JMX interface, the system can automatically collect performance data and adjust resource configuration in real time through the intelligent analysis engine. This system supports cloud environments and distributed architectures, can adapt to Java applications of different scales and loads, and provides flexible optimization and monitoring functions.

[0100] The above are only the preferred embodiments of the present invention, which are only used to illustrate the technical solutions of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are all included in the protection scope of the present invention.

Claims

1. A real-time performance monitoring and self-optimization system based on Java applications, characterized in that, It includes: A real-time performance monitoring module, which is used to collect the performance data of Java applications and transmit this data to the intelligent analysis and decision-making engine; An intelligent analysis and decision-making engine, which is used to analyze the performance data, identify performance bottlenecks, and generate optimization strategies; An automatic optimization module, which is used to automatically adjust the resource configuration of Java applications according to the optimization strategies; A real-time alarm and reporting module, which is used to generate alarm information and notify relevant personnel when the performance data is abnormal, and generate performance reports regularly.

2. The system according to claim 1, characterized in that, The real-time performance monitoring module regularly collects the runtime data of the application through the APIs provided by the JVM and the operating system, including: 1) Memory monitoring: Use the JMX interface to collect the usage of heap memory, non-heap memory, and the number of garbage collections to help identify memory bottlenecks; 2) CPU usage monitoring: Monitor the CPU usage through the OperatingSystemMXBean interface and generate an alarm when overloaded; 3) Thread pool monitoring: Monitor the number of active threads and the queue length of the thread pool. If the thread pool reaches the saturation state, trigger automatic adjustment; 4) Database connection pool monitoring: Collect the status of the database connection pool and optimize the allocation and recycling of database connections.

3. The system according to claim 2, characterized in that, It further includes: Network request monitoring: Monitor the response time and error rate metrics of Web requests and API calls, identify the performance bottlenecks of network requests, and take corresponding optimization measures.

4. The system according to claim 1, characterized in that, The intelligent analysis and decision-making engine predicts the system load based on the performance data and generates a load prediction result to adjust the resource configuration in advance.

5. The system according to claim 4, characterized in that, This engine adopts the following analysis methods: 1) Load analysis and trend prediction: Through retrospective analysis of historical data, identify the load fluctuation pattern, and predict the load trend based on the machine learning model; When the system load fluctuates violently, based on the prediction model, adjust the resource configuration in advance to avoid system overload; 2) Bottleneck identification and cause analysis: According to the collected monitoring data, combined with the preset performance benchmark values, automatically identify the performance bottlenecks in the application; Based on the bottleneck identification, the intelligent engine will select appropriate optimization strategies; 3) Adaptive optimization algorithm: According to the real-time data, load pattern, and bottleneck analysis, use the adaptive algorithm to dynamically adjust the resource allocation and scheduling strategies; 4) Strategy adjustment and execution plan generation: Based on the analysis results, the engine generates an execution plan and transmits it to the automatic optimization module.

6. The system according to claim 1, characterized in that, The automatic optimization module automatically adjusts the JVM memory size, the core thread number and the maximum thread number of the thread pool to optimize the application performance; The optimization content includes: 1) Memory management optimization: Dynamically adjust the memory parameters of the JVM according to the memory usage; Enable the garbage collector tuning mechanism; 2) Thread pool expansion and contraction: Automatically expand or contract the size of the thread pool to adapt to different load conditions; automatically increase the thread pool capacity when the load increases to reduce the overhead of thread switching; when the load is below the set value, contract the thread pool to release system resources; 3) Dynamic adjustment of database connection pool: Automatically adjust the maximum and minimum number of connections in the database connection pool; dynamically adjust the connection pool configuration according to the priority and response time of requests.

7. The system according to claim 6, wherein It further includes load balancing optimization. In high-concurrency scenarios, automatically adjust the load balancing strategy to ensure that requests are reasonably distributed among several service instances.

8. The system according to claim 1, wherein The real-time alarm and reporting module generates an alarm and gives a notice when the performance data exceeds the threshold.

9. The system according to claim 7, wherein This module includes: 1) Alarm generation and push: When an abnormal state is monitored, immediately generate an alarm and push it through email, SMS or WeChat notification; 2) Performance report generation: Regularly generate detailed performance reports, including trend analysis of various indicators, system bottlenecks, optimization measures and execution results, to assist in subsequent optimization.

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