A performance testing method for a server middleware

Abstract

The application discloses a kind of performance test methods of server middleware, it is related to computer technology field, including constructing dynamic business scenario model, baseline calibration and parameter snapshot are configured, deployment distributed test node cluster, multi-dimensional index real-time acquisition, adaptive load adjustment etc.The performance test method of the server middleware, through dynamic business scenario modeling-configuration baseline calibration-distributed collaborative load generation-multi-dimensional index linkage acquisition-adaptive load adjustment-full link performance tracing-long-term stability iteration verification integrated test process, in combination with the intelligent load regulation and control algorithm of real-time performance feedback and cross-level index correlation analysis model, realize the accurate evaluation of server middleware overall performance and single point component, while guaranteeing the benchmark consistency and long-term reliability of test result, test efficiency, data credibility and scene adaptability significantly improve, suitable for the full life cycle performance test of server middleware.

Description

Technical Field

[0001] This invention relates to the field of computer technology, specifically to a performance testing method for server middleware. Background Technology

[0002] As the core hub of a distributed system, server middleware undertakes key functions such as request forwarding, data caching, message routing, and load balancing. Its performance directly determines the stability and response efficiency of the entire distributed system. With the expansion of business scale and the increasing complexity of scenarios, higher requirements are placed on the performance testing of server middleware. Existing testing methods only evaluate baseline and macro performance, and are lacking in accurately locating performance bottlenecks. To address the aforementioned shortcomings, existing technology (Chinese patent publication number CN111324545B, publication date 2023-09-22) describes an application server middleware performance testing method, test terminal, and application server. This method involves sending a test access request to the application server and recording the sending time of the request, enabling the application server to process the request and record the processing data. The method also includes receiving the processing result from the application server and recording the receiving time; acquiring the processing data recorded by the application server; and analyzing the application server middleware performance based on the sending time of the test access request and / or the receiving time of the result and / or the processing data recorded by the application server. Specifically, analyzing the application server middleware performance based on the sending time of the test access request and / or the receiving time of the result and / or the processing data recorded by the application server allows for the breakdown of the application server middleware's processing flow, enabling the targeted extraction of performance data for individual components or stages. This helps application developers and users assess the suitability of the application server middleware's performance and, when performance issues arise, can assist in quickly locating middleware performance bottlenecks, improving problem analysis efficiency.

[0003] The above solutions suffer from several drawbacks during use. The test scenarios are statically fixed, the load adjustment is passive and lacks an adaptive mechanism, the indicator dimensions are single and lack cross-level linkage analysis, the distributed test collaboration is insufficient and the high concurrency support is weak. In addition, there are no configuration baseline calibration and long-term stability verification steps, and there is no closed loop for test result verification. As a result, the test scenarios are disconnected from real business, the accuracy of bottleneck location is low, the test security and efficiency are insufficient, and the reliability and practicality of the results are limited. They cannot meet the performance testing needs of middleware throughout its entire lifecycle in complex distributed systems. Summary of the Invention

[0004] The purpose of this invention is to provide a performance testing method for server middleware, in order to solve the problems of existing server middleware performance testing methods mentioned in the background art, which suffer from static and fixed test scenarios, passive load adjustment without adaptive mechanisms, single indicator dimensions and lack of cross-level linkage analysis, insufficient distributed test collaboration and weak high-concurrency support. At the same time, they do not set up configuration baseline calibration and long-term stability verification links, nor do they have a closed loop for test result verification. This leads to the disconnect between test scenarios and real business, low accuracy of bottleneck location, insufficient test security and efficiency, and limited reliability and practicality of results, and cannot meet the performance testing needs of middleware throughout its entire life cycle in complex distributed systems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a performance testing method for server middleware, comprising the following steps: S1: Construct a dynamic business scenario model. Based on the historical business logs and real-time traffic data of the target server middleware, extract the dynamic fluctuation characteristics of business request types (such as query, write, delete), request frequency, data payload size, and concurrent users. Train these parameters using an LSTM neural network to predict the load fluctuation pattern in different time periods. Based on the prediction results, generate a continuous dynamic load curve containing "baseline load segment - peak load segment - burst load segment - fallback load segment". The baseline load is set according to the average daily business load, the peak load is 2-5 times the baseline load (matching the business peak load), and the burst load lasts for 10-60 seconds with a random triggering frequency (simulating scenarios such as promotions and sudden access). At the same time, by filtering request types and blocking irrelevant loads, exclusive test scenarios for individual parts can be generated. S1.5: Configure baseline calibration and parameter snapshot. Perform initial configuration baseline calibration on the target server middleware. Export complete configuration parameters (including thread pool size, connection pool threshold, caching strategy, timeout, etc.) by calling the middleware's native configuration interface, and generate an immutable configuration snapshot file. Based on the baseline load segment in S1, conduct a 30-minute baseline performance test on the initial configuration, collect baseline values ​​of core indicators (average response time, throughput, CPU utilization), and use this as a basis to correct the preset thresholds of indicators in S3 (such as response time threshold = baseline response time × 1.5). At the same time, mark key configuration parameters (such as maximum number of connections in the connection pool, number of core threads in the thread pool) as "optimizable variables" to provide a baseline reference and clear direction for subsequent load adjustment in S4 and bottleneck analysis in S5. S2: Deploy a distributed test node cluster, which includes a master control node and at least 3 slave test nodes. The master control node and the slave test nodes establish an encrypted communication link through the RPC protocol. The slave test nodes are deployed in layers according to geographical partitions and network bandwidth, and each slave test node is configured with an independent traffic simulation module and data acquisition module. S3: Real-time collection of multi-dimensional metrics. The main control node issues collection commands to synchronously collect the performance metrics of the target middleware from the test nodes. The performance metrics include: business layer metrics (request response time, throughput, error rate), middleware kernel layer metrics (thread pool active count, connection pool utilization, cache hit rate), and system resource layer metrics (CPU utilization, memory usage, disk I / O rate, network latency). The collection frequency is dynamically adjusted according to the load stage. The collection interval is 1 second for the baseline load segment and 100ms for the peak / burst load segment. It supports filtering the metric collection range as needed and customizing exclusive metric sets for individual parts. S4: Adaptive load adjustment. A load control model is built based on the PID algorithm. The master control node receives multi-dimensional indicator data uploaded from the test nodes in real time. When a certain indicator exceeds a preset threshold (such as response time > 500ms, CPU utilization > 85%), the load output of the corresponding test node is automatically adjusted: if the business layer indicator is abnormal, the frequency of sudden load triggering is reduced; if the kernel layer indicator is abnormal, the number of simulated connections is dynamically expanded; if the system resource layer indicator is abnormal, part of the load is diverted to other test nodes to ensure that the middleware does not crash and the data is collected completely during the test. S5: Full-link performance analysis and result output. Through correlation analysis model, it performs linkage analysis on indicators of business layer, middleware kernel layer, and system resource layer, locates performance bottleneck nodes (such as insufficient connection pool leading to decreased throughput, low cache hit rate leading to CPU overload), and generates test result files containing dynamic load curves, multi-dimensional indicator time series graphs, bottleneck analysis reports, and performance optimization suggestions. It supports exporting to PDF or JSON format and supports linkage calculation of only single-point related indicators. S6: Test result verification. The performance optimization suggestions output in step S5 are verified by replaying real business traffic. The changes in multi-dimensional indicators before and after optimization are compared. If the core indicators (response time, throughput, error rate) are improved by ≥20% after optimization, the test results are deemed valid. S7: Long-term stability iterative testing. Based on the optimized configuration verified in S6, a long-term dynamic load iterative test is initiated, with an iteration cycle set to 72 hours. The load is generated cyclically according to the S1 dynamic load curve. During this period, a "configuration snapshot comparison" is triggered every 12 hours (comparing with the initial configuration snapshot of S1.5 and the optimized configuration snapshot of S6) to prevent abnormal drift of middleware configuration. If abnormal changes are found in key configuration parameters (such as the connection pool size being mistakenly changed), an alarm is immediately triggered and the change log is recorded to ensure configuration consistency during the test. At the same time, "performance degradation indicators" are added for collection (fluctuation range of core indicators, number of abnormal restarts, memory leak rate). If the performance degradation indicators exceed the preset threshold at a certain moment (such as memory leak rate > 50MB / h, fluctuation range of core indicators > 30%), load degradation and alarms are automatically triggered. After the test, a "Long-term stability analysis report" is generated to clarify the performance degradation pattern and extreme carrying capacity of the middleware under continuous business pressure.

[0006] In this embodiment, in step S1, the dynamic business scenario model is trained on historical business logs using an LSTM neural network to predict the load fluctuation pattern in different time periods. The historical business logs are collected over a period of no less than 7 days, and the data sample size is no less than 1 million records, ensuring that the dynamic load curve fits the real business with a degree of ≥90%.

[0007] In this embodiment, in step S1.5, the configuration snapshot file uses blockchain notarization technology to generate a unique hash value, which is stored in a distributed database. During subsequent testing, the integrity of the configuration file can be verified through the hash value to prevent the configuration from being maliciously tampered with during the testing process.

[0008] In this embodiment, in step S2, the traffic simulation module of the test node supports simulating requests for multiple communication protocols such as HTTP, TCP, and MQTT, and the maximum concurrent simulation capability of each test node is not less than 100,000 QPS. The master control node allocates test tasks through a consistent hashing algorithm to avoid uneven load on the test nodes.

[0009] In this embodiment, in step S3, the middleware kernel layer metrics are collected by calling the middleware's native monitoring API, and the system resource layer metrics are collected by a lightweight collection agent deployed on the server. The resource utilization rate of the collection agent is ≤1% to avoid affecting the accuracy of the test results.

[0010] In this embodiment, the proportional coefficient (Kp), integral coefficient (Ki), and derivative coefficient (Kd) of the PID load control model in step S4 are optimized using a genetic algorithm. The optimization objective is to "maximize test coverage and minimize index fluctuations" to ensure that the response time of load adjustment is ≤500ms.

[0011] In this embodiment, in step S5, the correlation analysis model uses the Pearson correlation coefficient to calculate the correlation between different indicators. When the absolute value of the correlation coefficient is ≥0.7, it is determined to be a strong correlation and is automatically marked as a performance bottleneck correlation combination (such as a strong correlation between "connection pool utilization >90%" and "response time >500ms").

[0012] In this embodiment, step S6, test result verification, verifies the performance optimization suggestions output in step S5 by replaying real business traffic and comparing the changes in multi-dimensional indicators before and after optimization. If the core indicators (response time, throughput, error rate) are improved by ≥20% after optimization, the test result is deemed valid.

[0013] In this embodiment, the S7 long-term stability iterative test supports the "breakpoint resumption" function. If the test is interrupted due to server failure or network interruption, the test process can be automatically resumed after restarting based on the most recent indicator snapshot and load phase without re-executing the entire cycle. If the performance degradation indicators exceed the preset threshold (such as memory leak rate > 50MB / h, core indicator fluctuation amplitude > 30%), the frequency of sudden load triggering is automatically reduced (load degradation), and testers are notified via SMS and email to avoid data loss due to the middleware being in an abnormal state for a long time.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This server middleware performance testing method employs an integrated testing process encompassing dynamic business scenario modeling, configuration baseline calibration, distributed collaborative load generation, multi-dimensional indicator linkage collection, adaptive load adjustment, end-to-end performance tracing, and long-term stability iterative verification. Combined with a real-time performance feedback-based intelligent load control algorithm and a cross-level indicator correlation analysis model, it achieves accurate evaluation of the overall performance of the server middleware and individual components. Simultaneously, it ensures the baseline consistency and long-term reliability of test results, significantly improving testing efficiency, data credibility, and scenario adaptability. It is suitable for full lifecycle performance testing of various server middleware types, including microservice middleware, message middleware, and caching middleware.

[0015] 1. Furthermore, dynamic scenarios fit real business scenarios, resulting in more accurate test results: By training historical business data through LSTM neural networks, dynamic load curves containing multiple load segments are generated, improving the fitting degree and solving the problem of the disconnect between traditional static scenarios and real business scenarios. It can comprehensively evaluate the performance of middleware under baseline, peak, and burst loads, providing a more reliable reference for actual deployment.

[0016] 2. Furthermore, the linkage of multi-dimensional indicators enables more accurate bottleneck identification: The innovative integration of indicators from the business layer, middleware kernel layer, and system resource layer, along with the discovery of strong correlations between indicators through a correlation analysis model, can accurately pinpoint the root cause of performance bottlenecks (such as insufficient connection pool, low cache hit rate, and server resource overload), avoiding the shortcomings of traditional testing.

[0017] 3. Furthermore, adaptive load adjustment improves testing efficiency and security: The load control model based on the PID algorithm can respond to performance anomalies in real time, automatically adjust the load intensity and distribution strategy, avoid data loss caused by middleware downtime, and shorten the testing cycle without manual intervention; the distributed node cluster supports load testing of more than 100,000 QPS, breaking through the bottleneck of single-point testing.

[0018] 4. Furthermore, the test results are verifiable and more practical: A new test result verification step has been added, which verifies the effectiveness of optimization suggestions by replaying real business traffic. This ensures that the test results can directly guide the parameter optimization of middleware and system deployment, improve the overall stability and response efficiency of distributed systems, and is applicable to the entire lifecycle testing scenarios of various server middleware.

[0019] 5. Further, configure baseline calibration. By configuring snapshot evidence and benchmark performance testing, a unified configuration benchmark and scientific indicator thresholds are provided for subsequent tests. This avoids test result deviations caused by configuration differences and subjective thresholds, and improves the comparability and credibility of test data.

[0020] 6. Furthermore, long-term stability iterative testing achieves full-cycle coverage of "short-term performance evaluation + long-term stability verification," accurately capturing the performance degradation patterns and configuration drift risks of middleware under continuous business pressure. This addresses the shortcomings of existing tests that "only test the short term and ignore the long term," providing key references for the long-term operation and maintenance of middleware. The configuration includes snapshot blockchain evidence storage and breakpoint resume testing functions, further enhancing the security and continuity of the testing process, avoiding repetitive work caused by configuration tampering and test interruptions, and improving testing efficiency. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the performance testing method for the server middleware of the present invention; Figure 2 This is a schematic diagram of the dynamic load curve in S1 of the present invention; Figure 3 This is a schematic diagram of the dynamic business scenario model in S1 of the present invention; Figure 4 This is a schematic diagram illustrating the full collection and classification of configuration parameters in S1.5 of the present invention; Figure 5 This is a schematic diagram illustrating the generation and storage of an immutable configuration snapshot in S1.5 of the present invention; Figure 6 This is a schematic diagram illustrating the precise collection of core indicators in S1.5 of the present invention; Figure 7 This is a schematic diagram of the intelligent correction of the preset threshold of the indicator in S1.5 of the present invention; Figure 8 This is a schematic diagram of the overall architecture of the present invention, S2. Figure 9 This is a schematic diagram illustrating the establishment of a communication link using the enhanced RPC protocol in S2 of this invention. Figure 10 This is a schematic diagram of the request simulation in S2 of the present invention; Figure 11 This is a schematic diagram illustrating the migration and load of node expansion / shrinkage tasks in the S2 balanced hash algorithm of this invention; Figure 12 This is a schematic diagram of the multi-dimensional indicator collection system of layered collection and intelligent adaptation in S3 of the present invention; Figure 13 This is a schematic diagram of the middleware kernel layer structure in S3 of the present invention; Figure 14 This is a schematic diagram of the optimized lightweight acquisition agent in S3 of the present invention; Figure 15 This is a schematic diagram of the optimized acquisition frequency scheduling logic in S3 of the present invention; Figure 16 This is a schematic diagram of the standard configuration of the customized selection of indicator set in S3 of the present invention. Figure 17 This is a schematic diagram illustrating the integrity and consistency of the collected data in S3 of the present invention; Figure 18 This is a schematic diagram of the overall architecture and components of the present invention S4; Figure 19 This is a schematic diagram of the upgrade of the correlation analysis model in S5 of the present invention; Figure 20 This is a schematic diagram of the hierarchical bottleneck location and root cause analysis in S5 of the present invention; Figure 21 This is a schematic diagram illustrating the customized output and visualization optimization of the S5 of this invention; Figure 22 This is a schematic diagram of dual-mode real traffic playback in S6 of the present invention; Figure 23 This is a schematic diagram of the multi-dimensional quantitative comparison system in S6 of the present invention; Figure 24 This is a schematic diagram of the grading validity determination criteria in S6 of the present invention; Figure 25 This is a schematic diagram of the extended performance degradation index system in S7 of the present invention; Figure 26 This is a schematic diagram illustrating the optimization of the fault tolerance and continued testing mechanism in S7 of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This invention provides the following technical solution: a performance testing method for server middleware, the core of which is: based on the historical business logs and real-time traffic data of the target server middleware, extracting dynamic fluctuation characteristics of business request type, request frequency, data payload size, and concurrent user count; training these parameters using an LSTM neural network to predict load fluctuation patterns over different time periods; generating a continuous dynamic load curve containing "baseline load segment - peak load segment - burst load segment - descent load segment" based on the prediction results; simultaneously generating dedicated test scenarios for individual parts by filtering request types and blocking irrelevant loads; performing initial configuration baseline calibration on the target server middleware; exporting complete configuration parameters by calling the middleware's native configuration interface; and generating an immutable configuration snapshot file based on historical logs. Real-time traffic is used to generate dynamic load curves with multiple load segments through LSTM training (supporting customization for specific scenarios); middleware configuration snapshots are exported and benchmarked for 30 minutes, calibrating indicator thresholds and marking optimizable variables; a "1 master N slave" encrypted communication cluster is deployed, with slave nodes deployed in layers, dynamically adjusting the collection interval according to load stages, and synchronously collecting three-layer indicators (supporting customization of specific indicator sets); indicators are monitored in real time through a PID model, and the load is automatically adjusted according to the anomaly type (business / kernel / system resource layer) to ensure middleware stability and data integrity; multi-dimensional indicator linkage analysis (supporting single-point indicator calculation) is performed to generate multi-format test reports; real traffic is replayed to verify the optimization effect (core indicator improvement ≥20% is considered effective); a 72-hour long-cycle iterative test is launched to monitor configuration drift and performance degradation, and generate a stability report. This solution employs an integrated testing process encompassing dynamic business scenario modeling, configuration baseline calibration, distributed collaborative load generation, multi-dimensional indicator linkage collection, adaptive load adjustment, end-to-end performance tracing, and long-term stability iterative verification. Combined with intelligent load control algorithms based on real-time performance feedback and cross-level indicator correlation analysis models, it achieves accurate evaluation of the overall performance of server middleware and individual components. Simultaneously, it ensures benchmark consistency and long-term reliability of test results, significantly improving testing efficiency, data credibility, and scenario adaptability. It is suitable for full lifecycle performance testing of various server middleware, including microservice middleware, message middleware, and caching middleware.

[0024] Example 1: To better understand the above technical solution, the following will describe it in detail with reference to the accompanying drawings and specific implementation methods. Figure 1 As shown, Figure 1 This is a diagram illustrating the performance testing methods for server middleware. The performance testing methods for server middleware include the following steps: In step S1, a dynamic business scenario model is constructed. Based on the historical business logs and real-time traffic data of the target server middleware, dynamic fluctuation characteristics of business request types (such as query, write, delete), request frequency, data payload size, and concurrent users are extracted. These parameters are trained using an LSTM neural network to predict the load fluctuation pattern in different time periods. Based on the prediction results, a continuous dynamic load curve containing "baseline load segment - peak load segment - burst load segment - descent load segment" is generated (see reference). Figure 2 As shown in the figure, the baseline load is set according to the average daily business load, the peak load is 2-5 times the baseline load (matching the peak business load), the duration of the burst load is 10-60 seconds and the trigger frequency is random (simulating promotion, burst access, and other scenarios). At the same time, you can filter the request type and block irrelevant loads to generate exclusive test scenarios for a single part. refer to Figure 3 As shown, specifically, the dynamic business scenario model is trained on historical business logs using an LSTM neural network to predict load fluctuation patterns over different time periods. The historical business logs are collected over a period of no less than 7 days, with a data sample size of no less than 1 million records, ensuring that the dynamic load curve fits the real business with a degree of ≥90%. It should be further explained that historical business logs and real-time traffic data must cover all time periods, including weekdays, weekends, and holidays, to ensure that they include complete scenario data such as business downtime, normal operation, and peak fluctuations; real-time traffic data is collected through two channels: port mirroring and middleware native traffic monitoring interface, with a collection latency of ≤100ms to ensure data timeliness. Based on the collected full data, abnormal data (such as invalid requests with a request time > 10 seconds, duplicate logs, and incorrectly formatted data) are removed using data cleaning algorithms, and then core parameters are extracted through feature engineering. Business request types: Precisely categorize requests into subcategories such as query, write, delete, update, and batch operations, and statistically analyze the percentage of each type of request (e.g., in e-commerce scenarios, query requests account for 70%, write requests account for 25%, and delete requests account for 5%), and mark core business requests (such as payment callbacks and order submissions, which cannot be interrupted) and non-core requests (such as browsing history queries and cache preheating, which can be degraded). Request frequency: The number of requests per unit time is counted in a 1-minute time window to form a time-series frequency curve, capturing the frequency fluctuation characteristics of daily peaks (such as 9 am and 8 pm), weekly peaks (such as Friday night), and monthly peaks (such as end-of-month settlement). Data payload size: Statistically analyze the payload size distribution range by request type (e.g., query request payloads are mostly 1-10KB, batch write request payloads are mostly 50-200KB), record the proportion and extreme values ​​of each range, and avoid distortion of test scenarios caused by a single fixed payload; Concurrent users: The number of active users in each time window is counted by deduplicating the session ID, and the average number of requests per user is calculated by combining the request frequency to restore the intensity of real user behavior.

[0025] Baseline load segment: Set according to the average daily business load (take the average value of the 12-hour stable operation period after removing peak and trough), with load fluctuation range ≤ ±10%, simulating the normal business operation state. Peak load segment: 2-5 times the baseline load, lasting 30-120 minutes (such as the pre-heating peak 1 hour before an e-commerce promotion), with load fluctuation range of ±15%, matching the periodic peak characteristics of the business. Sudden load segment: 5-8 times the baseline load, lasting 10-60 seconds, with a random triggering frequency (randomly triggered once every 1-4 hours), simulating scenarios such as promotional flash sales, sudden trending searches, and traffic surges after system failure recovery. At the same time, the percentage of request types during the sudden load is recorded (e.g., query requests account for more than 90% in flash sale scenarios). Drop-off load phase: The load is gradually reduced from the peak / burst load to the baseline load, and the cooling time is 1.5 times the duration of the peak / burst load to avoid test data distortion caused by sudden load drop.

[0026] It also supports customized scenario generation: Through a visual configuration interface, users can filter request types as needed (such as testing only the performance limits of write requests), block irrelevant loads (such as excluding non-core browsing requests), and adjust load parameters (such as manually setting the frequency of burst load triggering and peak load multiple) to generate exclusive test scenarios for individual functional modules or specific business scenarios (such as "Payment Interface Peak Load Test Scenario" and "Batch Data Import Exclusive Scenario"). The generated dynamic load curves support visual preview, allowing users to intuitively view parameters such as the duration, load intensity, and request type ratio of each load segment. One-click adjustment and regeneration are supported to ensure that the scenario meets the testing requirements.

[0027] Through the above process, we can ensure that the dynamic load curve fits the real business with a degree of ≥90%, which not only restores the load fluctuation characteristics of the whole scenario, but also supports customized testing requirements, and solves the shortcomings of traditional static scenarios that cannot cover complex business fluctuations.

[0028] In step S1.5, baseline calibration and parameter snapshot are configured. Initial configuration baseline calibration is performed on the target server middleware. Complete configuration parameters (including thread pool size, connection pool threshold, caching strategy, timeout, etc.) are exported by calling the middleware's native configuration interface, generating an immutable configuration snapshot file. Based on the baseline load segment in S1, a 30-minute baseline performance test is performed on the initial configuration. Baseline values ​​of core indicators (average response time, throughput, CPU utilization) are collected. Based on these values, the preset thresholds of the indicators in S3 (such as response time threshold = baseline response time × 1.5) are corrected. At the same time, key configuration parameters (such as the maximum number of connections in the connection pool and the number of core threads in the thread pool) are marked as "optimizable variables," providing a baseline reference and clear direction for subsequent load adjustment in S4 and bottleneck analysis in S5. refer to Figure 4 As shown, specifically, the configuration snapshot file uses blockchain notarization technology to generate a unique hash value, which is stored in a distributed database. During subsequent testing, the integrity of the configuration file can be verified through the hash value to prevent the configuration from being maliciously tampered with during the testing process. It should be further explained that all configuration parameters were collected and categorized: By calling the middleware's native configuration interface, a complete set of configuration parameters is exported, covering three core dimensions and avoiding parameter omissions: Key performance parameters include: thread pool size (core thread count, maximum thread count), thread pool queue length, connection pool thresholds (maximum number of connections, minimum number of idle connections, connection timeout), caching strategy (eviction algorithm, cache expiration time, maximum cache capacity), request queue length, and concurrency control thresholds, which directly affect performance. Functional configuration parameters: Protocol type (HTTP / TCP / MQTT), data serialization method, message retry count, load balancing strategy, circuit breaker degradation threshold, data persistence mode (such as RDB / AOF), and other function-related parameters; System adaptation parameters: parameters that adapt to the server hardware and system environment, such as memory allocation ratio, CPU affinity settings, disk I / O cache size, network buffer size, log level and output frequency; The exported parameters are structured and categorized for storage, generating a JSON-formatted configuration list that labels each parameter with its default value, current configuration value, parameter description, and modification permissions (read-only / modifiable), providing a clear basis for subsequent calibration and comparison.

[0029] Immutable configuration snapshot generation and storage: refer to Figure 5 As shown, based on the structured configuration list, an immutable configuration snapshot file is generated to ensure that the configuration is traceable and tamper-proof throughout the entire testing process. Snapshot file generation: The original snapshot is generated using a combination of "parameter content + collection timestamp + middleware unique identifier (such as device ID + version number)" to avoid snapshot duplication or confusion; Blockchain-based evidence storage reinforcement: The configuration snapshot file is uploaded to the consortium blockchain node, and a unique hash value is generated using the SHA-256 hash algorithm. At the same time, the evidence storage time and the operator's identity information are recorded to form an immutable evidence storage record. The hash value and the snapshot file are stored on different nodes of a distributed database (such as HBase) to achieve triple backup of "file + hash + evidence storage record". Integrity verification mechanism: During subsequent testing (such as before each round of load testing and after configuration adjustments), the middleware configuration interface is automatically called to re-collect parameters, generate real-time snapshots and calculate hash values, and compare them with the initial hash values ​​stored on the blockchain. If the hash values ​​are inconsistent, an alarm is immediately triggered and the difference parameters are recorded (such as the maximum number of connections in the connection pool being changed from 1000 to 800). At the same time, the middleware configuration modification permissions are locked to prevent malicious tampering or misoperation from affecting the effectiveness of the test.

[0030] Benchmark performance testing and intelligent threshold calibration: Based on the baseline load segment generated in step S1, conduct a 30-minute continuous baseline performance test to ensure the stability and reliability of the baseline data. Test environment preprocessing: Before testing, clear the middleware cache, disable unnecessary log output, and ensure that the server CPU / memory / disk resource utilization is ≤10% to avoid environmental interference; The baseline load is executed in three stages: "smooth application - stable operation - data collection". The first 5 minutes are the load warm-up period (gradually increasing from 50% to 100% of the baseline load), the middle 20 minutes are the stable collection period (maintaining 100% baseline load), and the last 5 minutes are the load decline period, during which only the indicator data of the stable collection period are collected; Accurate collection of core indicators: refer to Figure 6 As shown, the collected benchmark metrics include two dimensions: "performance metrics + resource metrics". Performance metrics: average response time, P50 / P95 / P99 percentile response time, throughput (QPS / TPS), error rate (number of failed requests / total number of requests), and request queuing time. Resource metrics: CPU utilization (average utilization, peak utilization), memory utilization (heap memory / non-heap memory are counted separately), disk I / O rate (read / write are counted separately), network bandwidth utilization; Intelligent correction of preset thresholds for indicators: refer to Figure 7 As shown, based on the collected benchmark indicators, the preset threshold of the indicators in step S3 is automatically corrected through a dynamic threshold algorithm to avoid misjudgments caused by subjective settings. Rigid thresholds (cannot be exceeded): Response time threshold = baseline P99 response time × 1.5, Error rate threshold = baseline error rate × 2 (if the baseline error rate is 0, then set to 0.5%), CPU utilization threshold = 85% (in conjunction with server hardware redundancy capabilities), Memory utilization threshold = 90%; Elastic threshold (early warning): When the indicator reaches 80% of the rigid threshold, an early warning is triggered (e.g., an early warning is triggered when the response time reaches 240ms, and the rigid threshold is 300ms), reserving reaction time for subsequent adaptive load adjustment; Intelligent labeling of optimizable variables: Through parameter sensitivity analysis algorithms, the impact weight of each configuration parameter on core performance indicators is calculated (such as the impact weight of the maximum number of connections in the connection pool on throughput, and the impact weight of cache expiration time on response time). Parameters with an impact weight ≥ 0.6 (such as the maximum number of connections in the connection pool, the core number of threads in the thread pool, and the cache eviction algorithm) are marked as "high-priority optimizable variables", and parameters with an impact weight of 0.3-0.6 (such as connection timeout time and queue length) are marked as "medium-priority optimizable variables". This provides clear guidance for load adjustment in step S4 (targeted scaling up / down of key parameters) and bottleneck analysis in step S5 (focusing on high-priority variables), avoiding blind optimization.

[0031] Baseline version management and rollback: The initial configuration snapshot, baseline performance metrics, revised metric thresholds, and list of optimizable variables are integrated into a "benchmark test package," which is assigned a unique version number (e.g., V1.0_20240520) and stored in the version management system. During subsequent testing, if the configuration needs to be adjusted or the test needs to be retested, the initial baseline state can be quickly rolled back based on this version number to ensure the comparability of different rounds of testing. At the same time, it supports generating a new snapshot version (e.g., V2.0_20240520) of the optimized configuration, forming a full lifecycle management of "initial baseline - optimization iteration - stable version".

[0032] In step S2, a distributed test node cluster is deployed. The test node cluster includes a master control node and at least three slave test nodes. The master control node and the slave test nodes establish an encrypted communication link through the RPC protocol. The slave test nodes are deployed in layers according to geographical partitions and network bandwidth, and each slave test node is configured with an independent traffic simulation module and data acquisition module. Specifically, the traffic simulation module of the test node supports simulating requests for multiple communication protocols such as HTTP, TCP, and MQTT, and the maximum concurrent simulation capacity of each test node is no less than 100,000 QPS. The master control node allocates test tasks through a consistent hashing algorithm to avoid uneven load on the test nodes. refer to Figure 8 As shown, it is worth noting that the main control node is configured with a high-performance server (16 cores, 32GB of memory, 1TB SSD storage, and 10Gbps dual network cards are recommended) to undertake core functions such as test task scheduling, load balancing policy distribution, data aggregation and analysis, and cluster status management; it has a built-in cluster coordination module (based on ZooKeeper) to synchronize the status of each node (online / offline, load rate, resource utilization) in real time to ensure cluster consistency.

[0033] From the test nodes: Deploy at least 3 test nodes, using a three-dimensional layered deployment based on "geographical partition + network bandwidth + hardware specifications" to meet the needs of different testing scenarios. Geographic partitioning: Cover the business coverage area of ​​the target middleware (e.g., deploy at least one node in each of East China, North China, and South China) to simulate cross-regional user access scenarios; Network bandwidth: Configure differentiated bandwidth (1Gbps / 10Gbps / 40Gbps) to support full-scenario testing from normal concurrency to ultra-large-scale high concurrency (such as 1 million QPS); Hardware specifications: Nodes for high-concurrency scenarios are configured with 32 cores and 64GB of memory, and 2TB of SSD storage; nodes for regular scenarios are configured with 16 cores and 32GB of memory, and 1TB of SSD storage, to ensure performance redundancy for a single node. Dedicated modules: Each test node has an independent built-in traffic simulation module, data acquisition module, and local caching module (caching recently collected data to avoid network transmission delays). The modules communicate with each other via local Socket, and the data transmission delay is ≤5ms.

[0034] Monitoring and scheduling node: A new independent monitoring and scheduling node (8C16G memory, 500GB SSD storage) is added to monitor the hardware resources (CPU / memory / disk / network), process status, and communication link stability of the master control node and slave test nodes in real time. When a node fails (such as CPU utilization continuously >95% or network interruption), an alarm is automatically triggered and the master control node is notified to adjust the task allocation to avoid single point of failure in the cluster.

[0035] refer to Figure 9 As shown, the enhanced RPC protocol establishes a communication link, balancing security and transmission efficiency: Encryption mechanism: The "TLS1.3 protocol + national cryptographic SM4 algorithm" is used to encrypt the transmitted data (task instructions, indicator data, node status) end-to-end, and the node identity is verified by two-way authentication (based on digital certificate) to prevent data from being eavesdropped, tampered with or forged. Link optimization: Enable the TCP protocol congestion control algorithm (BBR algorithm) to dynamically adjust the transmission rate and avoid data packet loss in high-concurrency scenarios; set up a link heartbeat detection mechanism (send a heartbeat packet every 100ms). When no response is received for 3 consecutive times, the link is determined to be interrupted, and the main control node automatically switches to the backup communication channel (such as a backup network card or a remote link) to ensure communication continuity. Data compression: The LZ4 compression algorithm is used for the transmitted indicator data (such as time series indicator sets), with a compression ratio of ≥3:1, which reduces network bandwidth usage and improves data transmission efficiency.

[0036] refer to Figure 10 As shown, the traffic simulation module on the test node supports multi-protocol, high concurrency, and high-fidelity request simulation, meeting the testing needs of different types of server middleware. Multi-protocol adaptation: Supports mainstream communication protocols such as HTTP / HTTPS, TCP / UDP, MQTT, AMQP, Redis, and Kafka. Protocol parameters (such as HTTP request headers, TCP timeout, and MQTT QoS level) can be customized through configuration files, and it adapts to various test objects such as microservice middleware, message middleware, and caching middleware. High concurrency capability: The maximum concurrent simulation capability of a single test node is no less than 100,000 QPS, supporting random distribution of requests (simulating real user request intervals according to Poisson distribution) and time-series distribution (sending requests according to the time pattern of the S1 dynamic load curve), while also supporting custom request payload generation (randomly generated according to the payload size distribution range extracted from S1 to ensure the authenticity of request data). Fault Injection Function: A new fault simulation submodule has been added, which can inject fault scenarios such as network latency (adjustable from 0-1000ms), data packet loss (adjustable loss rate from 0-50%), out-of-order requests, and protocol anomalies (such as illegal request headers and incomplete data packets) as needed to test the fault tolerance capability of the target middleware in abnormal network environments. Request reuse optimization: For repeated requests (such as high-frequency query requests), request template caching and reuse are supported to reduce the resource consumption of generating duplicate data and increase the simulation limit of single-node concurrency.

[0037] refer to Figure 11 As shown, an improved consistent hashing algorithm is used to distribute test tasks, solving the problems of large task migration and uneven load distribution when traditional hashing algorithms expand / shrink nodes. Task allocation logic: Using the "load segment" of the S1 dynamic load curve as the smallest task unit, and combining the real-time load rate (CPU utilization, memory usage, network bandwidth usage), geographical partitioning, and protocol adaptation capabilities of the test nodes, the "task carrying weight" of the nodes is calculated (the lower the load rate and the higher the bandwidth, the higher the weight). Based on the consistent hashing algorithm, task units are mapped to corresponding test nodes, and a virtual node mechanism is introduced (each physical node corresponds to 10 virtual nodes) to balance the task distribution on the hash ring and ensure that the load deviation of each node is ≤5%. Dynamic load adjustment: The monitoring and scheduling node collects load data of each slave test node in real time. When the load rate of a node is ≥80% (trigger threshold), the master control node automatically migrates some tasks of the node to a node with a load rate <50%. When a new slave test node is added (expansion), only the task units mapped by the virtual node of the node need to be migrated, with a migration amount of ≤10%, to avoid test interruption caused by large-scale task migration. Task priority scheduling: Supports setting priorities (high / medium / low) for test tasks. Core business scenario test tasks (such as payment interface performance testing) are set to high priority and resources are allocated first; non-core tasks (such as non-critical query interface testing) are set to low priority and can be temporarily downgraded when resources are scarce to ensure the stability of core test tasks.

[0038] In step S3, multi-dimensional metrics are collected in real time. The main control node issues collection commands to synchronously collect the performance metrics of the target middleware from the test nodes. The performance metrics include: business layer metrics (request response time, throughput, error rate), middleware kernel layer metrics (thread pool active count, connection pool utilization, cache hit rate), and system resource layer metrics (CPU utilization, memory usage, disk I / O rate, network latency). The collection frequency is dynamically adjusted according to the load stage. The collection interval for the baseline load stage is 1 second, and the collection interval for the peak / burst load stage is 100ms. It supports filtering the metric collection range as needed and customizing a dedicated metric set for a single part. Specifically, middleware kernel layer metrics are collected by calling the middleware's native monitoring API, while system resource layer metrics are collected by a lightweight collection agent deployed on the server. The resource utilization of the collection agent is ≤1% to avoid affecting the accuracy of the test results. refer to Figure 12 As shown, it is necessary to further explain that, based on the unified scheduling of the main control node, a multi-dimensional indicator collection system of "layered collection - intelligent adaptation - precise filtering - time-series alignment" is constructed to ensure that the indicator data is comprehensive, accurate, and free from interference. The specific solution is as follows: Business-level metrics: Response time categories: average response time, P50 / P95 / P99 / P99.9 percentile response time, request queuing time, request processing time (breaking down queuing and processing stages to accurately pinpoint the source of latency). Throughput metrics: QPS / TPS (subdivided by request type, such as query QPS, write TPS), successful requests per second, rejected requests per second; Error categories: error rate (subdivided by error type, such as timeout error, protocol error, business logic error), cumulative number of errors, and duration of continuous error triggers.

[0039] Middleware kernel layer metrics: Resource pool status: number of active threads, number of idle threads, number of threads waiting in the thread pool queue, thread creation / destruction frequency, connection pool utilization, number of idle connections, connection creation / closure frequency, and number of connection timeouts; Cache / storage status: cache hit rate, cache invalidation frequency, cache penetration count, cache update time, message backlog (message middleware), message consumption latency (message middleware), data persistence time; Kernel running status: request queue length, number of concurrent processes, number of kernel thread blocking times, lock contention time, and protocol parsing success rate.

[0040] System resource layer indicators: Computing resources: CPU utilization (statistics broken down by core), average CPU load (1min / 5min / 15min), context switching frequency; Storage resources: memory utilization (heap memory / non-heap memory / physical memory are counted separately), memory page swapping frequency, disk I / O rate (read / write are counted separately), disk utilization, disk IOPS, disk cache hit rate; Network resources: network bandwidth utilization (send / receive statistics are counted separately), network latency (round-trip time), packet loss rate, TCP connection establishment success rate, and half-open connection queue length.

[0041] Optimization and adaptation of tiered data collection mechanism: Differentiated data collection schemes are adopted to address the characteristics of different tiered indicators, balancing collection efficiency and data accuracy while avoiding interference from duplicate collection. Business layer metrics collection: The built-in collection submodule of the traffic simulation module from the test node collects data through the entire process of "request sending - response receiving" by setting up tracking points. It records the sending time, receiving time, status code, and error type of each request, aggregates and calculates locally, and then uploads the data. The collection latency is ≤10ms. refer to Figure 13 As shown, middleware kernel layer metric collection: A dual-mode collection approach using "native API + kernel probe" is employed. Common metrics (such as thread pool status and cache hit rate) are collected by calling the middleware's native monitoring APIs (such as Redis's INFO and Kafka's JMX) to ensure data authority. Deep kernel metrics (such as lock contention time and protocol parsing time) are collected by deploying lightweight kernel probes (based on eBPF technology, non-intrusive), with probe resource utilization ≤0.5%, avoiding impact on middleware operation; refer to Figure 14 As shown, system resource layer metric collection: optimized lightweight collection agent (based on PrometheusExporter modification), supports: Multi-dimensional segmented data collection: Accurately collect resource usage of the target middleware by process ID (rather than the entire server), avoiding interference from other processes; Dynamic sampling adjustment: The sampling granularity is adaptively adjusted according to the fluctuation range of resource usage. For example, when the CPU usage is stable, sampling is performed at a preset interval, and when the fluctuation is severe, the sampling is automatically encrypted. Data collection agent resource management: Through resource limitation mechanisms (such as a CPU usage limit of 1% and a memory usage limit of 500MB), it is ensured that the agent itself does not affect the test results. At the same time, it supports agent status self-check, and automatically restarts and switches to the backup data collection channel when an abnormality occurs.

[0042] refer to Figure 15 As shown, the data acquisition frequency scheduling logic is optimized to support flexible customization and balance data density and resource consumption. Dynamic frequency adaptive adjustment: Based on the existing load phase division, a new "abnormal fluctuation trigger encryption" mechanism is added. Baseline load segment: Data collection interval is 1 second. If the index fluctuation range is greater than 5%, the interval will be automatically shortened to 500 ms. Peak / burst load segment: The data collection interval is 100ms. If the indicator exceeds the warning threshold (such as the response time reaching 80% of the threshold), it will be further shortened to 50ms. Fallback load phase: The acquisition interval is gradually restored from 100ms to 1s (linearly adjusted according to the fallback duration) to avoid wasting acquisition resources after the load decreases; refer to Figure 16 As shown, the indicator set customization filtering provides a visual indicator configuration interface and supports: Filter by test objective: If you are only testing the performance of the middleware cache, you can select "Cache related metrics set" (cache hit rate, cache invalidation frequency, etc.) and hide irrelevant metrics (such as message backlog). Filter by level / type: Supports individual selection of business layer / kernel layer / system resource layer metrics, or filtering by type such as "response time" or "resource usage"; Customizable metric thresholds: Individual collection trigger conditions can be set for filtered metrics (e.g., collect the metric only when the cache hit rate is <80%), reducing the collection of invalid data.

[0043] refer to Figure 17 As shown, ensuring the integrity and consistency of the collected data provides a reliable foundation for subsequent correlation analysis: Data transmission optimization: The collected data adopts the "local aggregation - batch upload" mode, and the indicator data is uploaded in batches of 500ms from the test node. The Protocol Buffers serialization format (which improves the compression rate by 60% compared to JSON) is used, combined with the LZ4 compression algorithm to reduce network bandwidth consumption; at the same time, a data verification mechanism (CRC32 check) is enabled to ensure that there is no tampering or loss during the data transmission process. Time alignment mechanism: All acquisition nodes are synchronously calibrated to the NTP server (time error ≤ 1ms), and each indicator data carries a precise timestamp (accurate to the millisecond level); after receiving the data, the main control node performs time alignment on the indicators of different slave test nodes and different levels according to the timestamp, ensuring that indicators under the same time dimension can be correlated and compared, and avoiding analysis errors caused by time deviation; Data anomaly handling: For situations such as collection failure and missing data, an "interpolation completion + anomaly marking" strategy is adopted: short-term missing data (≤3 collection cycles) is completed by linear interpolation, and long-term missing data (>3 collection cycles) is marked as an anomaly and an alarm is triggered. At the same time, the reason for the missing data (such as network interruption or API call failure) is recorded to ensure that data integrity does not affect the overall analysis.

[0044] In step S4, adaptive load adjustment is performed. A load control model is constructed based on the PID algorithm. The master control node receives multi-dimensional indicator data uploaded from the test nodes in real time. When a certain indicator exceeds a preset threshold (such as response time > 500ms, CPU utilization > 85%), the load output of the corresponding test node is automatically adjusted: if the business layer indicator is abnormal, the frequency of sudden load triggering is reduced; if the kernel layer indicator is abnormal, the number of simulated connections is dynamically expanded; if the system resource layer indicator is abnormal, part of the load is diverted to other test nodes to ensure that the middleware does not crash and the data is collected completely during the test. refer to Figure 18 As shown, specifically, the proportional coefficient (Kp), integral coefficient (Ki), and derivative coefficient (Kd) of the PID load control model are optimized using a genetic algorithm. The optimization objective is to "maximize test coverage + minimize index fluctuations" to ensure that the response time of load adjustment is ≤500ms. It should be further explained that, based on the improved PID load control model, combined with hierarchical indicator identification and intelligent decision-making mechanisms, accurate and rapid load adjustment is achieved, ensuring a balance between test stability and coverage. The specific solution is as follows: Load regulation model optimization: Core algorithm upgrade: Fuzzy control logic is integrated into the PID, and the PID parameters are dynamically corrected through a fuzzy rule base (preset control strategies for different abnormal scenarios of indicators), which solves the problem of insufficient adaptability of traditional PID to nonlinear scenarios; the proportional coefficient (Kp), integral coefficient (Ki), and derivative coefficient (Kd) are still optimized by genetic algorithm, iterating 20 times to select the optimal parameter combination, and the optimization goal is strengthened to "maximize test coverage + minimize indicator fluctuation + optimize adjustment smoothness", with load adjustment response time ≤500ms and indicator stabilization time ≤2s after adjustment.

[0045] Customized design of the core algorithm (adapting to PID parameter optimization and load control requirements): Encoding method: Binary encoding is adopted, which maps Kp, Ki, and Kd into fixed-length binary strings (32 bits per parameter, 96 bits in total encoding length) according to preset value ranges (Kp: 0.1-5.0, Ki: 0.01-1.0, Kd: 0.05-2.0), balancing parameter accuracy and computational efficiency, and adapting to the rapid iteration requirements of load control; Fitness function: A core customized module, directly associated with the optimization objective, the formula is: in: To test coverage (0-1, the closer the load is to the middleware limit, the lower the load becomes). The closer to 1), The indicator volatility coefficient is 0-1; the more stable the indicator, the lower the volatility coefficient. The closer to 0), To adjust the fluctuation coefficient (0-1, the smoother the load adjustment, the better). (The closer to 0) =0.4、 =0.3、 =0.3 is the weighting coefficient, consistent with the weighting of the indicator layers, ensuring that the algorithm optimization direction is highly matched with the load control requirements; fitness value The larger the value, the better the parameter combination; Genetic manipulation: Selection: A tournament selection method is used (5 individuals are randomly selected each time, and the 2 with the highest fitness are retained) to avoid elite individuals being eliminated and to ensure optimization convergence; Crossover: Single-point crossover is adopted (crossover point is randomly selected, crossover probability is 0.75) to balance population diversity and preservation of superior genes; Mutation: Low-probability bit mutation (mutation probability 0.01) is used to prevent premature convergence of the algorithm, while avoiding excessive mutation that could lead to parameter confusion. Core execution steps of the algorithm: Initial population generation: Within the preset value range of Kp, Ki, and Kd, 50 initial parameter combinations (initial population size) are randomly generated. Each combination corresponds to one binary coded individual, covering different value ranges within the parameter range to ensure initial diversity.

[0046] Fitness assessment: Substitute the parameter combination of each individual into the PID load control model, and calculate the test coverage under that parameter through simulation testing (based on the dynamic load curve of step S1 and the benchmark index of S3). Indicator volatility coefficient Adjusting the fluctuation coefficient This leads to the fitness value. This completes the ranking of all individuals based on their merits.

[0047] Genetic iteration: Select, crossover, and mutation operations are performed sequentially on the initial population to generate a new generation population (maintaining a size of 50). The fitness evaluation-genetic operation process is repeated for 20 generations (balancing optimization effect and computation time, parameter convergence can be achieved in 20 generations).

[0048] Optimal parameter output: After the iteration is complete, select the fitness value. The largest individual is decoded into decimal Kp, Ki, and Kd, which are used as the final optimal parameters of the PID load control model and directly deployed to the main control node. Key features of the algorithm (matching load control scenario requirements): Lightweight and efficient: The population size and number of iterations are optimized for the scenario. The calculation time for a single iteration is ≤100ms, and the optimization time for the whole process is ≤2s. It will not affect the real-time performance of load adjustment and ensures that the PID model adjustment response time is ≤500ms. Scenario adaptation: The fitness function is directly related to the core objective of load control, avoiding the disconnect between algorithm optimization and actual application. The optimized parameter combination can simultaneously take into account test coverage and indicator stability. Strong convergence: By using tournament selection and low-probability mutation, the algorithm is ensured to converge quickly to the optimal parameter range, avoiding invalid iterations and adapting to dynamic scenarios of server middleware performance testing.

[0049] Multi-indicator fusion decision-making: Instead of triggering adjustments based on a single indicator, a comprehensive outlier is calculated by weighted summation (business layer indicator weight 0.4, kernel layer weight 0.3, system resource layer weight 0.3). Adjustments are initiated when the comprehensive outlier is ≥0.7, avoiding frequent misadjustments caused by fluctuations in a single indicator.

[0050] Layered and precise control strategy: For abnormal business layer metrics (such as P99 response time exceeding the threshold or error rate spikes): In addition to reducing the frequency of sudden load triggers, new strategies such as "request priority downgrade" (temporarily blocking non-core requests) and "peak load delay trigger" (delaying peak load segments by 5-10 minutes until the metrics recover) are added to ensure the integrity of core business test data.

[0051] If kernel-level metrics are abnormal (such as connection pool utilization exceeding 90% or thread pool blocking): dynamically expand the simulated number of connections / threads (the expansion range is adjusted linearly according to the degree of abnormality, with a maximum expansion of 2 times the original configuration), while triggering temporary optimization of middleware kernel parameters (such as increasing the request queue length), and synchronously collecting the optimized metrics to provide data for subsequent bottleneck analysis.

[0052] Abnormal system resource indicators (such as CPU utilization exceeding 85% or insufficient memory): In addition to load balancing, add "node-level load limiting" (limiting the load of abnormal nodes to 70% of the current load) and "automatic backup node replacement" (enabling reserved load balancing from test nodes) to avoid overall cluster load imbalance.

[0053] Adjustment boundaries and rollback mechanism: Set load adjustment boundaries: The load reduction should not exceed 30% at a time to avoid the test scenario from being distorted by a sudden drop in load; The minimum load during the peak load period should not be lower than 1.5 times the baseline load to ensure test coverage in peak scenarios.

[0054] Intelligent rollback strategy: When the abnormal indicator recovers to below 80% of the threshold and remains stable for 30 seconds, the load is gradually rolled back according to the original adjustment path (e.g., the offloaded load is migrated back to the original node in 3 steps), while continuously monitoring indicator changes to prevent secondary anomalies.

[0055] In step S5, the full-link performance analysis and result output are performed. Through the correlation analysis model, the indicators of the business layer, middleware kernel layer, and system resource layer are analyzed in a coordinated manner to locate performance bottleneck nodes (such as insufficient connection pool leading to decreased throughput, low cache hit rate leading to CPU overload). A test result file containing dynamic load curves, multi-dimensional indicator time series diagrams, bottleneck analysis reports, and performance optimization suggestions is generated. It supports exporting to PDF or JSON format and supports linkage calculation of only single-point related indicators. Specifically, the correlation analysis model uses the Pearson correlation coefficient to calculate the correlation between different indicators. When the absolute value of the correlation coefficient is ≥0.7, it is determined to be a strong correlation and is automatically marked as a performance bottleneck correlation combination (such as a strong correlation between "connection pool utilization >90%" and "response time >500ms"). refer to Figure 19 As shown, it is necessary to further explain the upgrade of the association analysis model (multi-model fusion to improve association accuracy): Core algorithm optimization: Based on the Pearson correlation coefficient, Spearman rank correlation coefficient and mutual information entropy are integrated to form a multi-dimensional correlation evaluation: Pearson coefficient (adapted to linear correlation indicators, such as CPU utilization and throughput), Spearman coefficient (adapted to non-linear monotonic correlation indicators, such as cache hit rate and response time), and mutual information entropy (capturing non-monotonic dependencies between indicators, such as lock contention and thread pool blocking). The correlation strength is determined by combining the results of the three. When the results of any two algorithms meet the "strong correlation" (Pearson / Spearman absolute value ≥ 0.7, mutual information entropy ≥ 0.6), it is marked as a bottleneck correlation combination.

[0056] Enhanced temporal correlation: Introducing a sliding time window (adjustable from 1 to 5 seconds) to calculate the correlation degree of different indicators within the same time window, avoiding miscorrelation of indicators across time periods (such as the response time delay after a sudden load is not related to subsequent CPU overload); at the same time, marking the correlation time difference (such as the response time starting to spike after the connection pool utilization exceeds the threshold by 300ms), providing a time dimension basis for bottleneck tracing.

[0057] refer to Figure 20 As shown, the bottleneck location and root cause analysis are performed at different levels. Bottleneck level determination: Automatically locate the bottleneck level (business layer / kernel layer / system resource layer) through association and combination, and distinguish between "direct bottleneck" and "indirect bottleneck" (e.g., CPU overload in the system resource layer is an indirect bottleneck, while low cache hit rate in the kernel layer leading to high CPU consumption is a direct bottleneck).

[0058] Root cause quantification analysis: Calculate the "influence weight" (based on the random forest algorithm) for strongly correlated combinations. For example, the influence weight of "cache hit rate < 70%" on "CPU utilization > 85%" is 0.85, and the influence weight of "connection pool utilization > 90%" on "response time > 500ms" is 0.92. Sort the core root causes by weight and output them to avoid confusion caused by multiple bottlenecks.

[0059] Single-point metric linkage support: Users can specify a single point object (such as "caching module" or "write request"), and the system will automatically filter all-level metrics related to that object (such as cache hit rate, cache update time, write TPS, and memory usage) and perform linkage calculations only on the target metric set to focus on single-point performance issues.

[0060] refer to Figure 21 As shown, the customized results output and visualization optimization are as follows: Results file upgrade: In addition to the original content, the following have been added: "Bottleneck Association Map" (visualizing the relationship and impact path between indicators) and "Root Cause Quantification Report" (including impact weight and optimization priority sorting); support for filtering output by test scenario (e.g., exporting only bottleneck analysis of burst load segments), and the export formats have been expanded to Excel and HTML (supporting online preview).

[0061] Enhanced visualization: Dynamic load curves and multi-dimensional indicator time series graphs support "bottlenecks linked" (clicking on a bottleneck node automatically highlights the indicator fluctuations for the corresponding time period); optimization suggestions are sorted by "cost-benefit ratio" (e.g., "adjusting cache eviction policy" has low cost and high benefit, so it is recommended first), and parameter adjustment ranges are provided (e.g., "the maximum number of connections in the connection pool is recommended to be adjusted to 1500-2000"), improving operability.

[0062] In step S6, the test results are verified by replaying real business traffic to verify the performance optimization suggestions output in step S5. The changes in multi-dimensional indicators before and after optimization are compared. If the core indicators (response time, throughput, error rate) are improved by ≥20% after optimization, the test results are deemed valid. Specifically, the test results are verified by replaying real business traffic to validate the performance optimization suggestions output in step S5. The changes in multi-dimensional indicators before and after optimization are compared. If the core indicators (response time, throughput, error rate) are improved by ≥20% after optimization, the test results are deemed valid. refer to Figure 22 As shown, it is necessary to further explain the dual-mode real traffic replay: Full traffic replay: Based on the collected real business traffic (covering all time periods for 7 days, with a data volume of ≥500,000 records), the request type, frequency, payload size, and time sequence characteristics are restored at a 1:1 ratio to avoid verification bias caused by sampled traffic; Targeted stress replay: For the bottleneck scenarios identified in step S5 (such as peak load or sudden traffic), amplify the corresponding traffic intensity (to 1.2 times the actual peak value) to verify the adaptability of the optimization suggestions in extreme scenarios.

[0063] refer to Figure 23 As shown, a multi-dimensional quantitative comparison system: In-depth comparison of core indicators: In addition to response time, throughput, and error rate, a comparison of the quantile values ​​(P95 / P99 response time) and stability indicators (indicator fluctuation range) of the new core indicators has been added to avoid the mean from masking extreme cases; Linked indicator comparison: Simultaneously compare bottleneck-related indicators before and after optimization (such as cache hit rate and CPU utilization, connection pool utilization and response time) to verify whether the root cause has been completely resolved (such as CPU utilization decreasing synchronously after cache hit rate is improved).

[0064] refer to Figure 24 As shown, the criteria for determining the validity of the classification are as follows: Effective in the basics: The core indicators (average response time, throughput, error rate) are improved by ≥20% overall, and the response time per quantile decreases by ≥15%, with the indicator fluctuation range ≤10%; High quality and effective: The core indicators are improved by ≥30%, there are no error requests in extreme scenarios, and the related indicators are optimized in a coordinated manner (such as cache hit rate improved by ≥25% and CPU utilization decreased by ≥20%). Supplementary verification mechanism: If the basic effective standard is not met, the correlation analysis process in step S5 will be automatically traced back, and optimization suggestions will be re-screened and verified a second time. If the second verification still fails to meet the standard, it will be marked as "suggestion to be optimized" and a deviation analysis will be attached (such as unreasonable parameter adjustment range or misjudgment of bottleneck root cause).

[0065] In step S7, long-term stability iterative testing is conducted. Based on the optimized configuration verified in S6, a long-cycle dynamic load iterative test is initiated with an iteration cycle of 72 hours. The load is generated cyclically according to the dynamic load curve of S1. During this period, a "configuration snapshot comparison" is triggered every 12 hours (comparing with the initial configuration snapshot of S1.5 and the optimized configuration snapshot of S6) to prevent abnormal drift of the middleware configuration. If abnormal changes are found in key configuration parameters (such as the connection pool size being mistakenly changed), an alarm is immediately triggered and a change log is recorded to ensure configuration consistency during the test. At the same time, "performance degradation indicators" are added for collection (fluctuation range of core indicators, number of abnormal restarts, and memory leak rate). If the performance degradation indicators exceed the preset threshold at a certain moment (such as memory leak rate > 50MB / h, fluctuation range of core indicators > 30%), load degradation and alarms are automatically triggered. After the test, a "Long-Term Stability Analysis Report" is generated to clarify the performance degradation pattern and limit bearing time of the middleware under continuous business pressure. Specifically, long-term stability iterative testing supports the "breakpoint resumption" function. If the test is interrupted due to server failure or network interruption, the test process can be automatically resumed after restarting based on the most recent indicator snapshot and load phase without re-executing the entire cycle. If the performance degradation indicators exceed the preset threshold (such as memory leak rate > 50MB / h, core indicator fluctuation > 30%), the frequency of sudden load triggering will be automatically reduced (load degradation), and testers will be notified via SMS and email to avoid data loss due to the middleware being in an abnormal state for a long time. It should be further explained that load iteration and configuration monitoring have been enhanced: Dynamic load optimization: Iterates cyclically according to the S1 dynamic load curve, and adds a "load intensity gradient increase" mechanism (increases peak / burst load intensity by 5% every 24 hours, up to 1.2 times the original peak value) to simulate the long-term growth trend of business traffic; Configuration monitoring upgrade: Configuration snapshot comparison every 12 hours, new "parameter change traceability" (records the operator, time, and terminal IP of the change), and secondary authorization is required for changes to key parameters (optimizable variables); at the same time, the middleware operation log is monitored in real time, configuration-related errors (such as parameter failure to take effect) are extracted, and alarms are triggered in conjunction with the snapshot comparison results.

[0066] refer to Figure 25 As shown, the performance degradation index system is expanded: In addition to the existing indicators, the following are added: Functional stability metrics: number of request timeouts, number of protocol-related reconnections, and number of data consistency verification failures (for storage-type middleware); Resource degradation metrics: disk fragmentation rate, number of network connection leaks, number of zombie threads in the thread pool; The preset thresholds are refined (e.g., disk fragmentation growth rate > 10% / 24h, number of zombie threads in thread pool > 5), and a snapshot of all dimensions of the decay node is recorded synchronously when load degradation is triggered.

[0067] refer to Figure 26 As shown, the fault tolerance and continued testing mechanisms have been optimized: Enhanced breakpoint resume testing: Supports "cross-node resume testing" (automatically switches to a backup node to resume testing when the original test node fails), and automatically aligns with historical indicator time series after resume testing to ensure data continuity; Tiered load degradation: Three levels of degradation based on the degree of attenuation (mild: reduce the frequency of sudden loads; moderate: suspend peak load periods; severe: reduce to 80% of the baseline load), while activating "emergency optimization" (temporarily enabling preset stability configurations). Alarm escalation mechanism: The first alarm is notified via SMS and email. If it is not handled within 15 minutes, the test manager will be called automatically to prevent the fault from escalating.

[0068] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0069] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A performance testing method for server middleware, characterized in that: Includes the following steps: S1: Build a dynamic business scenario model. Based on the historical business logs and real-time traffic data of the target server middleware, extract core load characteristics and generate a continuous dynamic load curve with multiple load segments. It also supports customized test scenarios. S1.5: Configure baseline calibration and parameter snapshot, export the complete middleware configuration to generate an immutable snapshot, collect the benchmark values ​​of core indicators through benchmark load testing, correct the preset thresholds of indicators and mark the optimizable variables to establish the test benchmark; S2: Deploy a distributed test node cluster, build a cluster containing a master control node and at least 3 hierarchically deployed slave test nodes, connected through an encrypted communication link, with each slave test node equipped with an independent module to enable collaborative execution of test tasks; S3: Real-time collection of multi-dimensional metrics, dynamic adjustment of collection frequency according to load stage, synchronous collection of metrics from business layer, middleware kernel layer, and system resource layer, support for filtering metric range as needed, and customization of exclusive metric sets; S4: Adaptive load adjustment, based on the PID algorithm to build a control model, automatically takes measures such as reducing the frequency of sudden loads, increasing the number of connections, and diverting load according to the abnormal types of multi-dimensional indicators to ensure test stability; S5: Full-link performance analysis and result output, link analysis of multi-dimensional indicators to locate performance bottlenecks, generate test result files including dynamic load curves, bottleneck reports, etc., and support export in multiple formats and single-point indicator linkage calculation. S6: Test result verification. Play back real business traffic to verify the optimization suggestions. Compare the indicators before and after optimization. If the core indicators improve by ≥20%, the test results are considered valid. S7: Long-term stability iterative testing, based on optimized configuration, conducts 72-hour long-term dynamic load testing, regularly performs configuration snapshot comparison to prevent drift, collects performance degradation indicators and triggers abnormal degradation alarms, and finally outputs a long-term stability analysis report.

2. The performance testing method for server middleware according to claim 1, characterized in that: S1, the dynamic business scenario model is trained on historical business logs using an LSTM neural network to predict the load fluctuation pattern in different time periods. The historical business logs are collected over a period of no less than 7 days and the data sample size is no less than 1 million records, ensuring that the dynamic load curve fits the real business with a degree of ≥90%.

3. The performance testing method for server middleware according to claim 1, characterized in that: In step S1.5, the configuration snapshot file uses blockchain notarization technology to generate a unique hash value, which is stored in a distributed database. During subsequent testing, the integrity of the configuration file can be verified through the hash value to prevent the configuration from being maliciously tampered with during the testing process.

4. The performance testing method for server middleware according to claim 1, characterized in that: In S2, the traffic simulation module of the test node supports simulating requests for multiple communication protocols such as HTTP, TCP, and MQTT, and the maximum concurrent simulation capability of each test node is not less than 100,000 QPS. The master control node allocates test tasks through a consistent hashing algorithm to avoid uneven load on the test nodes.

5. The performance testing method for server middleware according to claim 1, characterized in that: In S3, the middleware kernel layer metrics are collected by calling the middleware's native monitoring API, and the system resource layer metrics are collected by a lightweight collection agent deployed on the server. The resource utilization rate of the collection agent is ≤1% to avoid affecting the accuracy of the test results.

6. The performance testing method for server middleware according to claim 1, characterized in that: In S4, the proportional coefficient, integral coefficient, and derivative coefficient of the PID load control model are optimized using a genetic algorithm. The optimization objective is to "maximize test coverage and minimize index fluctuations" to ensure that the response time of load adjustment is ≤500ms.

7. The performance testing method for server middleware according to claim 1, characterized in that: In S5, the correlation analysis model uses the Pearson correlation coefficient to calculate the correlation between different indicators. When the absolute value of the correlation coefficient is ≥0.7, it is determined to be a strong correlation and is automatically marked as a performance bottleneck correlation combination.

8. The performance testing method for server middleware according to claim 1, characterized in that: In step S6, the test results are verified by replaying real business traffic to verify the performance optimization suggestions output in step S5. The changes in multi-dimensional indicators before and after optimization are compared. If the core indicators are improved by ≥20% after optimization, the test results are deemed valid.

9. The performance testing method for server middleware according to claim 1, characterized in that: The S7 supports "breakpoint resumption" for long-term stability iterative testing. If the test is interrupted due to server failure or network interruption, the test process can be automatically resumed after restarting based on the most recent indicator snapshot and load phase, without having to re-execute the entire cycle.

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