Application Traffic Simulation Using Captured Flows
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Solution Overview
Problem
Predicting the impact of network changes on application performance is challenging due to reliance on heuristics and manual estimations, which can lead to inaccurate predictions, especially with variables like delay, jitter, and packet loss affecting network impairments.
Innovation Solution
An application traffic capture and analysis engine generates projections of network changes' effects on application performance by using actual recorded traffic patterns and simulations, allowing network administrators to adjust parameters like delay and jitter through intuitive interfaces, providing a more accurate prediction of application behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heuristics and manual estimations are used to predict application performance, then the prediction process is simple and quick, but the prediction accuracy is low
Solution Approach 1:
The system performs preliminary actions by capturing and storing actual network traffic data and application performance metrics before predictions are needed. This pre-collected data serves as a foundation for accurate predictions without requiring complex real-time analysis infrastructure.
Solution Approach 2:
The system creates copies of actual network traffic flows and reproduces them in a virtual environment for simulation. By copying real traffic patterns and analyzing them under various network condition scenarios, the system achieves accurate predictions without directly manipulating production systems.
2Measurement precision
If simulated traffic flows are generated to predict application performance, then the prediction accuracy is improved, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by capturing and storing actual network traffic data and application performance metrics before predictions are needed. This pre-collected data serves as a foundation for accurate predictions without requiring complex real-time analysis infrastructure.
Solution Approach 2:
The system creates copies of actual network traffic flows and reproduces them in a virtual environment for simulation. By copying real traffic patterns and analyzing them under various network condition scenarios, the system achieves accurate predictions without directly manipulating production systems.
3Reliability
If actual recorded traffic patterns are used for simulation, then the reliability of performance predictions is improved, but the data processing complexity increases
Solution Approach 1:
The system extracts only the essential characteristics and patterns from actual network traffic data that are relevant for performance prediction. By filtering and selecting only the critical traffic flow attributes, the system maintains prediction reliability while reducing processing complexity.
Solution Approach 2:
The system transforms raw traffic data into standardized parameters and metrics that are suitable for simulation and analysis. By changing the representation of traffic data into normalized parameters, the system improves reliability while simplifying subsequent processing steps.
Data Source
AI summary
Application performance can be simulated based on captured application-specific traffic flows through a managed network. Traffic flows may be captured across the managed network and associated with a particular application. The captured flows can be used to generate trend lines and models. The generated trend lines and models may be used to simulate application performance responsive to changes in network characteristics and provided to a user through a graphical user interface as a graph. The user may then adjust simulated network characteristics through the graphical user interface to perform various hypothetical network simulations.