Autonomous AI Controller for SDN Routing Optimization
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Solution Overview
Problem
Centralized network control in Software-Defined Networks (SDNs) faces challenges with long reaction times, fragility, and poor scalability due to manual configuration, making it difficult to optimize routing paths efficiently and adapt to changing network conditions.
Innovation Solution
An autonomous AI controller is used to determine optimal routing paths by selecting pivotal nodes based on relative importance, utilizing machine learning models to automatically configure and refine routing configurations in real-time, reducing the search space and adapting to network changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If centralized manual configuration is used for network control, then network routing paths can be configured, but reaction time is long and scalability is poor
Solution Approach 1:
The system employs machine learning models that automatically learn optimal routing configurations from network data without human intervention. The models self-train on historical network performance data and autonomously generate routing decisions, eliminating the need for manual configuration and significantly reducing reaction time to network changes.
Solution Approach 2:
The patent replaces manual mechanical configuration processes with automated machine learning systems. Instead of operators manually configuring routing paths, ML models process network data and automatically determine optimal routes, substituting human-operated mechanical systems with intelligent automated systems that respond instantly to network conditions.
2Measurement precision
If comprehensive network topology is analyzed for routing optimization, then optimal paths can be determined, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the network topology analysis into manageable components by training separate machine learning models on different aspects of network data. Instead of analyzing the entire network at once, the system divides the problem into smaller learning tasks that can be processed independently and then combined, reducing computational complexity while maintaining optimization accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models on historical network data before actual routing decisions are needed. This advance preparation allows the models to quickly make routing decisions without performing comprehensive analysis in real-time, reducing processing time while maintaining accuracy.
3Reliability
If machine learning models are trained on complete network data, then accurate routing decisions can be made, but training time and computational resources increase
Solution Approach 1:
The patent applies partial action by training machine learning models on selected subsets of network data that are most relevant to routing decisions, rather than using complete network data. This approach achieves sufficient training accuracy with reduced computational resources and shorter training time, as the system identifies and focuses on the most critical features and patterns in the data.
Data Source
AI summary
An autonomous controller for SDN, virtual, and/or physical networks can be used to optimize a network automatically and determine new optimizations as a network scales. The controller trains models that can determine in real-time the optimal path for the flow of data from node A to B in an arbitrary network. The controller processes a network topology to determine relative importance of nodes in the network. The controller reduces a search space for a machine learning model by selecting pivotal nodes based on the determined relative importance. When a demand to transfer traffic between two hosts is detected, the controller utilizes an AI model to determine one or more of the pivotal nodes to be used in routing the traffic between the two hosts. The controller determines a path between the two hosts which comprises the selected pivotal nodes and deploys a routing configuration for the path to the network.