Test Traffic Configuration via ML-Based Pattern Replication
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Solution Overview
Problem
Current network traffic generators lack diversity in simulating real-world traffic patterns, making it time-consuming and arduous to scale for multiple applications, as they primarily rely on configurable parameters that do not accurately replicate the ever-changing, non-deterministic nature of real-world traffic.
Innovation Solution
A method using a traffic profiler with a machine learning engine to analyze data streams from a production network, generate a test traffic configuration, and replicate real-world traffic patterns by configuring test traffic sources to simulate the observed conditions, including parameters like requests per second, latency, and packet loss, thereby enabling more robust application testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional configurable traffic generators are used to simulate network traffic, then basic stress testing can be performed, but the ability to accurately replicate real-world traffic patterns is insufficient
Solution Approach 1:
The system captures actual traffic from a production environment and creates a copy that can be replayed in the test environment. The traffic capture module records real traffic patterns, and the traffic replay module reproduces these patterns, allowing accurate replication of real-world conditions without manually configuring each parameter.
Solution Approach 2:
The patent replaces manual configuration of traffic parameters with an automated machine learning-based system. The ML model automatically analyzes captured traffic and generates appropriate test configurations, substituting the mechanical process of manual parameter tuning with an intelligent automated system.
2Reliability
If manual configuration of traffic parameters is used to emulate real-world patterns, then some level of traffic simulation is achieved, but the process becomes time-consuming and difficult to scale
Solution Approach 1:
The system performs self-service by automatically capturing traffic from production, analyzing it through ML models, and generating test configurations without human intervention. The traffic profiler and ML model work autonomously to create realistic test scenarios, eliminating the need for manual parameter configuration and reducing time loss.
Solution Approach 2:
The system performs preliminary action by capturing and analyzing traffic patterns before actual testing begins. The traffic capture and ML analysis occur in advance to prepare realistic test configurations, so that when testing starts, the parameters are already optimized based on real-world data rather than requiring time-consuming manual adjustment.
3Ease of operation
If deterministic traffic patterns are used for testing, then controlled test conditions are achieved, but the non-deterministic nature of real-world traffic is not captured
Solution Approach 1:
The system introduces dynamics by using ML models to generate traffic patterns that adapt and vary over time, mimicking the non-deterministic nature of real-world traffic. Rather than static deterministic patterns, the system creates dynamic traffic scenarios that change based on learned patterns from production environments, maintaining both control and realism.
Data Source
AI summary
Some embodiments provide a method for generating a test traffic configuration for testing a first network. From a second network, the method receives a set of data streams representing data traffic observed in the second network. The method uses a machine learning engine to analyze the set of data streams in order to determine traffic patterns in the second network. The method generates the test traffic configuration for testing the first network by replicating the traffic patterns of the second network in the first network.