Protocol Determination via Machine Learning
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Solution Overview
Problem
Load testing tools face challenges in determining the correct communication protocols for applications, especially when limited information is available about the application's features and protocols, leading to difficulties in simulating real-user interactions effectively.
Innovation Solution
A system utilizing machine learning methods, such as k-nearest neighbors (KNN), to analyze data from monitored interactions and categorize features, enabling the selection of compatible communication protocols by assigning values based on usage and predicting protocols that correspond to the application's properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional protocol selection methods are used with limited application information, then the simplicity of operation is maintained, but the measurement precision of protocol compatibility deteriorates
Solution Approach 1:
A protocol advisor system acts as an intermediary between the load testing tool and the application. The protocol advisor collects data from multiple sources including application logs, network traffic, and module information, then uses machine learning algorithms to predict compatible protocols. This intermediary layer enables accurate protocol selection without requiring the load testing tool itself to become more complex.
Solution Approach 2:
The patent replaces traditional manual or rule-based protocol selection mechanisms with a machine learning-based predictive system. Instead of relying on engineers to manually analyze application characteristics and select protocols, the system uses trained models that automatically predict compatible protocols based on input data features, thereby improving accuracy while managing complexity through automation.
2Measurement precision
If multiple features are analyzed using machine learning methods, then the measurement precision of protocol determination is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The protocol advisor is designed as a multi-functional system that simultaneously collects and analyzes multiple types of data including application logs, network traffic patterns, module loading information, and runtime behaviors. This universal approach allows the system to gather comprehensive features from diverse sources through unified data collection mechanisms, improving protocol determination accuracy while managing the complexity of multi-feature analysis.
3Reliability
If limited data from monitored interactions is used, then the ease of operation is maintained, but the reliability of protocol prediction deteriorates
Solution Approach 1:
The system performs preliminary data collection and analysis during the application's normal operation before the actual load testing begins. The protocol advisor continuously monitors and stores protocol-related data, network traffic patterns, and application behaviors in advance, building a training dataset that can be used to improve prediction reliability. This preliminary action ensures that sufficient data is available without complicating the actual load testing operation.
Data Source
AI summary
In one implementation, a system for protocol determination includes a monitor engine to collect data relating to interactions with an application. In addition, the system includes a data engine to store loaded scripts to the application and corresponding interactions relating to the loaded scripts. In addition, the system includes a prediction engine to assign a value to each feature of the stored loaded scripts for the application, wherein the value is based on the usage of the application. In addition, the system includes a protocol engine to select a protocol based on the value.