Dynamic Auto-Routing for Communication Systems
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Solution Overview
Problem
Existing communication systems face bottlenecks and reduced throughput due to network devices becoming overloaded with data, leading to inefficient resource utilization and potential system halts.
Innovation Solution
A communication system employing machine learning to dynamically route and reroute messages based on current message volume and bandwidth, using a network analysis device to classify traffic volume and provide routing recommendations, thereby optimizing resource utilization and preventing bottlenecks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data transmission continues to network devices, then message volume increases, but network devices become overloaded and memory resources are depleted
Solution Approach 1:
The patent implements dynamic routing that automatically adjusts message distribution based on real-time network device memory status. The system transitions from static routing to dynamic routing, where the network analysis device continuously monitors memory resources and reroutes messages away from overloaded devices, preventing system halts while maintaining high throughput
Solution Approach 2:
The system employs feedback mechanisms where the network analysis device receives memory status information from network devices, processes this data through machine learning models to predict overload conditions, and sends routing instructions back to the routing device. This closed-loop feedback prevents bottlenecks before they occur, maintaining both high productivity and system reliability
2Productivity
If routing is optimized to prevent bottlenecks, then resource utilization improves, but system complexity increases due to machine learning models
Solution Approach 1:
The patent introduces a network analysis device as an intermediary between the routing device and network devices. This intermediary contains the machine learning models and processes, isolating the complexity from the core routing function. The routing device simply follows instructions from the network analysis device, maintaining simplicity while achieving optimized resource utilization through the intelligent intermediary
Solution Approach 2:
The system segments the routing function into separate components: the routing device that handles message forwarding, the network analysis device that monitors status and generates routing instructions, and machine learning models that predict overload conditions. This segmentation allows each component to remain relatively simple while the collective system achieves sophisticated load balancing and high resource utilization
Data Source
AI summary
A system that includes a data source, a plurality of network devices, a routing device, and a network analysis device. The network analysis device is configured to obtain metric information that is associated with a plurality of messages and to input the metric information into a first machine learning model that outputs a traffic volume classification based on the metric information. The network analysis device is further configured to obtain bandwidth information that is associated with the plurality of network devices and to input the bandwidth information and the traffic volume classification into a second machine learning model that outputs routing recommendations based on the bandwidth information and the traffic volume classification. The network analysis device is further configured to generate routing instructions based on the routing recommendations and to reconfigure the routing device based on the routing instructions.