SDN Prediction Module for Dynamic Route Optimization
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Solution Overview
Problem
Current data flow optimization in software-defined networks (SDNs) is limited by the need for manual configuration updates and reliance on predefined models, which fail to adapt effectively to changing network conditions, leading to suboptimal performance and inefficiencies.
Innovation Solution
A system and method that utilize a prediction module within an SDN application to evaluate current and past input parameters, predicting future route configurations and dynamically reconfiguring routes based on these predictions, incorporating multiple prediction models and machine learning techniques to adapt to changing network conditions autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration updates are used for route optimization, then system stability is maintained, but adaptability to changing network conditions deteriorates
Solution Approach 1:
The system enables self-service optimization by having the prediction module automatically analyze network conditions and generate route configuration recommendations without requiring manual intervention. The application component autonomously evaluates prediction results and updates routes based on predicted network states, allowing the system to optimize itself in response to changing conditions.
Solution Approach 2:
The prediction module performs preliminary analysis of network conditions by evaluating current and past input parameters to forecast future network states. This preliminary action enables the system to proactively adjust routes before actual network changes occur, improving adaptability while maintaining stability through advance preparation.
2Productivity
If predefined models are used for route configuration, then implementation simplicity is maintained, but performance optimization deteriorates
Solution Approach 1:
The system dynamically changes parameters by switching between different prediction models based on current network conditions. The application component selects appropriate models from multiple available options and adjusts their parameters to optimize performance for specific scenarios, achieving high optimization without being locked into a single predefined model.
Solution Approach 2:
The prediction module implements dynamics by enabling flexible switching between multiple prediction models based on real-time network conditions. This dynamic approach allows the system to adapt its complexity level and model selection to match current operational requirements, optimizing performance while managing complexity through conditional activation of models.
3Reliability
If static route configurations are used, then system stability is maintained, but responsiveness to dynamic conditions deteriorates
Solution Approach 1:
The prediction module performs preliminary evaluation of network conditions and generates route configuration recommendations in advance. This allows the system to maintain stability by preparing changes before they are needed, while achieving rapid responsiveness when actual network changes occur by having pre-computed optimization paths ready for immediate implementation.
Solution Approach 2:
The system implements feedback mechanisms where the prediction module continuously monitors network conditions and adjusts route configurations based on actual performance. This feedback loop enables the system to maintain stability through controlled adjustments while achieving rapid responsiveness by continuously adapting to real-time network changes based on observed performance data.
Data Source
AI summary
The disclosure provides a networked computing system, comprising at least one network communication interface connected to at least one network, the at least one network communication interface being configured to receive data from and to send data to the at least one network, a control component, wherein the control component is adapted to configure routes, wherein the control component is configured to provide current input parameters on the routes, and wherein an application component is configured to output predicted configuration parameters for future route configurations based on predictions, based on the predicted configuration parameters output by the application component.