Application-Specific Network Routing for Latency and Jitter
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network routing configurations do not optimize software application performance effectively, leading to suboptimal data traffic routing and inefficient resource utilization.
Innovation Solution
A method and system that generates randomized sets of network routing configurations, monitors application performance, and selects the most optimized configuration based on specific performance metrics, using machine-learning techniques to intelligently route data traffic for improved application performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional network routing configurations are used, then network connectivity is maintained, but application performance is not optimized
Solution Approach 1:
The system dynamically generates randomized routing configurations and adapts to network conditions in real-time, transitioning from static traditional routing to dynamic optimized routing. The machine learning model continuously learns from performance metrics and adjusts routing decisions, enabling the system to adapt to changing network conditions and optimize application performance dynamically.
Solution Approach 2:
The system employs self-service mechanisms where the machine learning model automatically selects optimal routing configurations without manual intervention. The system monitors its own performance metrics, generates new configurations, evaluates them, and implements improvements autonomously, reducing the need for manual network administration while enhancing application performance.
2Productivity
If randomized routing configurations are generated and tested, then optimal performance is achieved, but time and computational resources are consumed
Solution Approach 1:
Instead of exhaustively testing all possible routing configurations, the system generates and evaluates a randomized subset of configurations. This partial action approach finds sufficiently good solutions without the time cost of complete enumeration, balancing optimization quality with evaluation time by sampling the configuration space strategically.
Solution Approach 2:
The system continuously monitors application performance metrics and maintains ongoing optimization efforts. Rather than performing discrete, time-consuming optimization cycles, the machine learning model continuously learns from incoming performance data and adjusts routing configurations in real-time, keeping the optimization process continuous and useful throughout system operation.
3Productivity
If machine learning techniques are used to optimize routing, then application performance improves, but system complexity increases
Solution Approach 1:
The machine learning model serves as an intermediary layer between the network infrastructure and the application. Rather than making the entire system complex, the ML model is introduced as a focused component that translates network conditions and configuration options into optimized routing decisions, managing complexity locally while delivering performance benefits system-wide.
Solution Approach 2:
The system optimizes performance by changing routing parameters such as source IP addresses, destination IP addresses, and protocol selections. Rather than redesigning the entire network architecture, the machine learning model adjusts these existing parameters dynamically, achieving performance improvement through parameter optimization rather than structural complexity increases.
Data Source
AI summary
Aspects herein provide systems, methods, and media for optimizing network routing configuration for specific applications. In aspects, the data traffic of an application can be intelligently and selectively routed using specific network “slices” or “paths” that optimize the application's performance for a metric, such as jitter or latency. As such, various network routing configurations can be monitored and evaluated to examine the application's performance over particular network slices, and further optimize configurations for specific data traffic types and volumes, particular devices, and more.