Inter-Message Timing Modeling for Real-Time Network Latency
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Solution Overview
Problem
Existing methods for predicting network latency lack precision and technical granularity, especially in low-latency environments, and are impractical for real-time large-scale applications due to high computation overhead.
Innovation Solution
A computer-implemented method using machine learning algorithms that utilize inter-message timing metrics as feature parameters to predict and diagnose latency fluctuations, employing a dynamic session manager to adjust network parameters for optimal latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If KNN algorithm is used to predict network latency, then latency prediction capability is provided, but computation overhead becomes high and real-time large-scale application becomes impractical
Solution Approach 1:
The patent transforms the latency prediction problem by changing the parameter representation from raw message timing data to engineered features including inter-message timing metrics, message size, and compression ratios. This parameter transformation enables the use of more efficient machine learning models (random forest, gradient boosting, neural networks) that have lower computational overhead compared to KNN, while maintaining prediction accuracy in low-latency environments
Solution Approach 2:
The patent replaces the mechanical distance-calculation mechanism of KNN with machine learning model inference. Instead of computing distances between query points and all training samples (O(n) complexity), the trained ML models provide constant-time or logarithmic-time predictions, substituting the computationally intensive mechanical process with a more efficient algorithmic approach
2Measurement precision
If KNN algorithm is used to predict network latency, then latency prediction capability is provided, but it becomes impractical for real-time applications
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline using historical network data. The models are trained during non-critical periods and then deployed for real-time prediction. This separates the computationally intensive training phase from the real-time inference phase, enabling fast predictions without the overhead of real-time distance calculations required by KNN
Solution Approach 2:
The patent changes the computational parameters by using engineered features (inter-message timing, message size, compression ratios) that capture the essential patterns in network latency. These pre-computed features enable the ML models to make accurate predictions with minimal real-time computation, achieving the required real-time performance
3Quantity of substance
If existing latency prediction methods are used, then general latency measurement is provided, but precision and technical granularity are insufficient especially in low-latency environments
Solution Approach 1:
The patent segments the latency measurement into multiple components by introducing inter-message timing metrics that capture different aspects of network behavior. Instead of measuring only end-to-end latency, the system segments timing measurements into inter-message intervals, round-trip times, and processing delays, providing both broad coverage and fine-grained precision simultaneously
Solution Approach 2:
The patent adds another dimension to latency measurement by incorporating multiple feature dimensions (inter-message timing, message size, compression ratios, session information) beyond traditional single-value latency measurements. This multi-dimensional approach enables precise prediction while maintaining comprehensive coverage of network conditions
Data Source
AI summary
A method for reducing latency in communication over a network incudes obtaining historical data concerning communications including latency, inter-message timing, volume and number of sessions, converting the obtained data into feature parameters, including an inter-message timing metric, for use as inputs to a machine learning algorithm, training the machine learning algorithm using the feature parameters to predict communication latency. After training the network, providing new feature parameters including at least an inter-message timing metric and a number of sessions, concerning a current communication scenario, executing the trained machine learning algorithm to predict latency based on the new feature parameters, determining a predicted minimal latency for the current communication scenario by iteratively executing the machine learning algorithm while adjusting inter-message timing and session parameters, and setting the inter-message timing and session parameters current scenario at values which provide the predicted minimal round trip latency.