ML Network Configuration Controller for Predictive E2E Optimization
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Solution Overview
Problem
Existing network optimization techniques struggle to provide end-to-end predictive network optimization efficiently, especially in complex network environments with varying traffic patterns and performance requirements.
Innovation Solution
A Machine Learning (ML) based network configuration system that utilizes a configuration controller to process flow information and determine optimal network configuration settings, incorporating techniques such as predictive analytics and reinforcement learning to enhance end-to-end performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network optimization techniques are used, then network configuration can be managed, but end-to-end predictive network optimization cannot be achieved efficiently
Solution Approach 1:
The system employs machine learning models that automatically analyze network flow information and generate configuration recommendations without human intervention. The ML model self-learns from historical network data and autonomously identifies optimization opportunities, enabling the network to optimize itself predictively rather than relying on manual configuration.
Solution Approach 2:
The system performs predictive analytics by analyzing historical and current flow information to forecast future network conditions and configuration needs. By taking preliminary action based on predictions, the system proactively adjusts network configurations before performance degradation occurs, achieving efficient end-to-end optimization.
2Reliability
If machine learning based configuration is implemented, then end-to-end performance is improved, but system complexity increases
Solution Approach 1:
The patent introduces a configuration controller as an intermediary component that bridges the machine learning model and the network configuration system. This mediator translates complex ML predictions into actionable configuration parameters, managing the complexity by providing a structured interface between the intelligent system and the network infrastructure.
Solution Approach 2:
The system divides the network configuration task into separate functional modules: flow information collection, ML analysis, configuration recommendation generation, and configuration application. Each module handles a specific aspect of the optimization process, making the overall complex system manageable through functional segmentation and independent development of each component.
Data Source
AI summary
For example, a network configuration controller may monitor a plurality of node-related flow information sets based on flow information corresponding to a plurality of data flows via a network, the plurality of node-related flow information sets corresponding to a plurality of networking nodes connecting between a plurality of network inputs of the network and a plurality of network outputs of the network. For example, a node-related flow information set corresponding to a networking node of the plurality of networking nodes may include information corresponding to one or more data flows communicated via the networking node. For example, network configuration controller may determine a network-configuration setting to configure the network based on the plurality of node-related flow information sets and at least one target End to End (E2E) performance parameter corresponding to an E2E performance of the plurality of data flows.