Distributed performance test method and system based on dynamic load prediction

Through dynamic load prediction and intelligent resource scheduling, combined with bidirectional buried point probes and density clustering algorithms, the problems of unreasonable resource scheduling and untimely abnormal detection in traditional distributed performance tests are solved, and efficient performance testing and rapid problem positioning are achieved.

CN120429217AActive Publication Date: 2025-08-05BEIJING YIYUANKU TECH CO LTD
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Patent Information

Application Number
CN202510941908.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-09
Publication Date
2025-08-05
Estimated Expiration
2045-07-09

AI Technical Summary

Technical Problem

Traditional distributed performance testing systems cannot dynamically adjust according to the system's real-time load conditions, resulting in low resource utilization, low testing efficiency, and difficulty in digging deep into the internal connection between traffic and resource data, and unable to quickly locate performance bottlenecks.

Method used

Using a method based on dynamic load prediction, we periodically collect data from node resource utilization, use dynamic threshold verification algorithm to judge resource status abnormalities, implement priority migration strategies, combine two-way buried point probes and density clustering algorithms to associate traffic with resource data, and display a full-link diagnostic view in real time.

Benefits of technology

Real-time response to test load changes is achieved, resource scheduling efficiency is improved, performance bottlenecks are automatically identified, stability and interpretability of the test system are improved, and manual inspection costs are reduced.

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Abstract

The invention provides a distributed performance test method and system based on dynamic load prediction, and the method comprises the steps: periodically collecting the resource utilization data of a current slave node as the data of a current time point, and predicting the resource utilization data of a next time point; judging whether the resource utilization data at the next time point is abnormal or not based on a dynamic threshold verification algorithm, and starting a process migration mechanism based on a priority migration strategy; a bidirectional buried point probe is deployed at the slave node and the tested system, a density clustering algorithm is adopted to analyze and cluster the associated flow and resource data, and an abnormal problem mode is identified according to a clustering result; and displaying the relationship between the pressure request quantity of each test interface and the resource index as well as the abnormal problem mode in real time, and forming a full-link diagnosis view of pressure input-system response. According to the method, the response speed of the distributed performance test and the slave node resource scheduling efficiency are greatly improved, second-level time alignment is carried out on the flow data and the resource data, and bottom layer data support is provided for upstream evaluation analysis.
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