Network Performance Prediction via Statistical Trend Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network performance prediction methods rely on passive measurements, which are inadequate for accurately forecasting performance changes over time, making it difficult to identify performance degradations or improvements promptly.
Innovation Solution
A statistical model is developed for each network application tuple, where new measurements are weighted and combined with previous values to predict performance indicators, allowing for the detection of performance transitions by comparing them to user-specified thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If passive measurements are used to monitor network performance, then the measurement process is simple, but the prediction precision of network performance is insufficient
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing historical performance measurements before actual performance degradation occurs. This enables the predictive model to analyze trends and forecast future performance states, allowing operators to take preventive actions before problems arise.
Solution Approach 2:
The system transitions from static passive measurement to dynamic predictive analysis. By implementing a predictive model that continuously updates based on new measurements and historical data, the system adapts to changing network conditions and provides dynamic performance forecasts rather than fixed snapshots.
2Reliability
If passive measurements are taken at a moment, then the measurement process is simple, but the ability to predict future performance is lost
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing historical performance measurements before actual performance degradation occurs. This enables the predictive model to analyze trends and forecast future performance states, allowing operators to take preventive actions before problems arise.
Solution Approach 2:
The system implements feedback by continuously comparing predicted performance with actual measurements. When deviations are detected, the model adjusts its predictions and triggers alerts, creating a closed-loop system that improves reliability over time through continuous validation and refinement.
3Measurement precision
If more measurements are collected to improve prediction accuracy, then the prediction precision improves, but the data processing complexity increases
Solution Approach 1:
The system extracts only the most relevant features and metrics from the collected measurements for predictive analysis. By filtering and selecting key performance indicators that have the highest correlation with future performance states, the system maintains high prediction accuracy while reducing the complexity of data processing.
Solution Approach 2:
The system transforms raw measurement data into meaningful performance parameters and trends that are more suitable for prediction. By changing the representation of data from raw measurements to processed performance indicators, the system improves prediction precision while managing data processing complexity through dimensionality reduction.
Data Source
AI summary
Systems, methods, and computer-readable media are provided for determining a change in a state of performance of a given network application. In one aspect, a method includes receiving, at a network controller, a set of measurements for an application; determining, at the controller, a predicted goodput and an application response delay for the application; based on a comparison of each of the predicted goodput and the application response delay to a respective threshold, detecting a transition in state of performance of the application; and communicating the transition in the state of performance of the application to a network monitoring terminal.