Automated Network Traffic Characterization and Script Generation
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Solution Overview
Problem
Current network testing systems require extensive human intervention, leading to high operational costs and errors due to the separation of statistical analysis, modeling, and traffic generation capabilities, which complicates network device and application testing.
Innovation Solution
An automated network testing system that integrates statistical analysis, modeling, and traffic generation, allowing for the automatic creation of network traffic scripts that reflect real traffic behavior, reducing user involvement and minimizing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If current network testing systems use separate tools for statistical analysis, modeling, and traffic generation, then each component can be optimized independently, but the overall system complexity increases and requires extensive human intervention
Solution Approach 1:
The patent combines separate network testing functions (data collection, statistical analysis, modeling, and traffic generation) into a single integrated system. The system merges previously separate tools and processes, allowing automated workflows where traffic generation scripts are automatically created based on statistical analysis and modeling of real network traffic, eliminating the need for manual intervention between separate components.
Solution Approach 2:
The integrated system performs multiple functions through a single platform: it collects network data, performs statistical analysis, creates traffic models, and generates test traffic. This multi-functional approach allows one system to replace multiple separate tools, reducing overall system complexity while maintaining the optimization capabilities of individual components.
2Adaptability or versatility
If manual intervention is used in network testing processes, then flexibility and adaptability are maintained, but operational costs increase and human errors are introduced
Solution Approach 1:
The system performs self-service automation where the statistical analysis module automatically processes collected data, the modeling module automatically creates traffic models based on statistical results, and the traffic generation module automatically creates test scripts. This automated self-service workflow eliminates manual intervention while maintaining adaptability through configurable parameters and automatic adjustment of test scenarios based on real traffic patterns.
Solution Approach 2:
The system incorporates feedback loops where real network traffic data is continuously collected, statistically analyzed, and used to automatically adjust traffic generation parameters. The feedback mechanism allows the system to adapt to changing network conditions automatically, maintaining flexibility while improving operational efficiency through automated response to real-time data.
3Adaptability or versatility
If multiple separate products are used for network testing, then specialized functionality is available for each task, but the learning curve increases and requires highly trained personnel
Solution Approach 1:
The patent merges multiple specialized tools into a single integrated system that provides all necessary network testing functions. This consolidation maintains specialized functionality for each task (data collection, analysis, modeling, generation) while presenting a unified user interface and automated workflow that reduces the learning curve and eliminates the need for highly trained personnel to manage multiple separate products.
4Productivity
If automated scripting is implemented, then operational costs are reduced and errors are minimized, but the system requires sophisticated algorithms for traffic generation
Solution Approach 1:
The system performs preliminary action by collecting real network traffic data and performing statistical analysis before generating test traffic. The modeling module creates traffic models in advance based on historical data patterns, which then guide the automated traffic generation. This preliminary preparation allows sophisticated algorithms to work with pre-processed, statistically significant data, improving operational efficiency while managing algorithm complexity through structured data preparation.
Data Source
AI summary
Automated characterization of network traffic. A method may include compiling and/or receiving network traffic data based on the network traffic, preparing a network traffic characterization based on the network traffic data, and generating outgoing network traffic scripts based on the network traffic characterization. A method may also include capturing network traffic and generating test network traffic based on the scripts. A system on which the method may be executed may include one or more chassis and/or computing devices, each having one or more network cards. The chassis and/or computing devices may be connected to one or more networks and to one another. The networks may include a production network and a test network. A system may include a data collector, a characterization engine, and a script generator, and may also include a traffic generator.