Real-Time Load Test Dashboard with Statistical Correlation
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Solution Overview
Problem
Current load testing of web-based applications faces challenges in efficiently processing and aggregating large volumes of test results data from multiple load servers, leading to resource overload and complex manual processing for combining and correlating test results into meaningful charts.
Innovation Solution
The implementation of a CloudTest system that utilizes a graphical user interface (GUI) for real-time data aggregation and correlation, allowing users to easily combine and correlate test results data by dragging and dropping charts, and automatically generating multi-axis charts with statistical correlations, leveraging cloud resources and grid computing to streamline the load testing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time data aggregation from multiple load servers is implemented, then test results processing speed is improved, but server resource overload occurs
Solution Approach 1:
The patent divides the data aggregation process into multiple tiers: load generators generate test data, load controllers collect data from multiple load generators, and analytic servers process data from multiple load controllers. This hierarchical segmentation distributes the processing load across multiple servers, preventing any single server from becoming overwhelmed while maintaining real-time processing capabilities.
2Measurement precision
If complex chart combination and correlation processing is performed manually, then data accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements automated chart combination and correlation processing where the system performs data aggregation, chart generation, and statistical correlation automatically without manual intervention. The load controllers and analytic servers self-manage the complex processing tasks, eliminating the time-consuming manual steps while maintaining data accuracy through systematic automated procedures.
3Quantity of substance
If large volume test results data is transmitted from load servers, then data completeness is improved, but network overhead increases
Solution Approach 1:
The patent segments the data transmission path into hierarchical levels where load generators send data to load controllers, which aggregate and forward summarized data to analytic servers. This segmentation reduces the total volume of data transmitted across the network while preserving data completeness through progressive aggregation at each tier.
Solution Approach 2:
The patent extracts and processes data locally at each tier level before transmission. Load controllers extract key metrics from multiple load generators and forward only the aggregated results to analytic servers, eliminating the need to transmit complete raw datasets across the entire network while maintaining data completeness for analysis.
4Ease of operation
If automated chart combination is implemented, then ease of operation is improved, but system complexity increases
Solution Approach 1:
The patent uses standardized data structures and chart templates that can be replicated across multiple load controllers and analytic servers. By copying proven automated processing patterns and interface designs, the system achieves ease of operation through consistency while managing complexity through standardization rather than custom solutions.
Data Source
AI summary
A processor-implemented method includes providing an analytic dashboard with a graphical user interface (GUI) that outputs aggregated results streaming in real-time of a load test performed on a target website. Responsive to input of a user on the GUI, the input comprising selection of a source chart and a target chart, a single chart is automatically generated that represents either a combination or a statistical correlation of the source and target charts. The single chart has a left y-axis and an x-axis. The combination or the statistical correlation of the single chart changing in real-time as the load test progresses. A visual representation of the single chart is then produced on the analytic dashboard.