ML Network Resource Prediction for Congestion Reduction
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Solution Overview
Problem
Current methods for determining and predicting network states, such as resource availability and usage, are inefficient due to reliance on incomplete data and lack of accurate forecasting, leading to suboptimal resource allocation and increased congestion.
Innovation Solution
A machine-learned model is trained using historical data to predict resource availability, usage, and costs across network nodes, enabling the determination of network topology and future resource demands, thereby optimizing resource allocation and reducing congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current methods for determining network states are used, then resource allocation can be performed, but the allocation is suboptimal due to reliance on incomplete data and lack of accurate forecasting
Solution Approach 1:
The patent applies preliminary action by training machine learning models in advance using historical network data to predict future network states, resource availability, and usage patterns. This pre-computed knowledge enables more accurate and efficient real-time resource allocation decisions without relying on incomplete current data alone.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based network state determination methods with machine learning-based predictive systems. The ML models analyze historical patterns and generate forecasts for network topology, resource availability, and usage, substituting manual or heuristic approaches with data-driven intelligent systems.
2Reliability
If traditional network monitoring methods are used, then basic resource tracking is possible, but congestion increases due to lack of accurate forecasting
Solution Approach 1:
The patent implements feedback mechanisms where machine learning models continuously learn from historical network data, resource usage patterns, and congestion events. The system uses this feedback to refine predictions of future network states and adjust resource allocation strategies proactively, preventing congestion before it occurs rather than merely reacting to it.
Solution Approach 2:
The system performs preliminary forecasting of network congestion and resource shortages using trained ML models. By predicting future states in advance, the system can take proactive resource allocation actions before congestion develops, maintaining network reliability and preventing harmful congestion effects.
3Productivity
If machine learning models are implemented for prediction, then resource allocation is optimized, but system complexity increases
Solution Approach 1:
The patent applies universality by developing machine learning models that perform multiple functions simultaneously: predicting network topology, forecasting resource availability, estimating usage patterns, and identifying congestion risks. This multi-functional approach consolidates what would otherwise require separate systems into a unified predictive platform, managing complexity while maximizing productivity benefits.
Data Source
AI summary
Provided are methods, systems, devices, apparatuses, and tangible non-transitory computer readable media for network topology analysis and prediction. The disclosed technology can perform operations including receiving network data including information associated with a network including a plurality of nodes respectively associated with resource availability and resource usage. Resource availability can be associated with an amount of a resource available for distribution from a portion of the plurality of nodes at an initial time interval. Further, resource usage can be associated with usage of the resource from the portion of the plurality of nodes at the initial time interval. The network topology, resource availability, and resource usage for a portion of the plurality of nodes at a time interval subsequent to the initial time interval can be determined. Furthermore, one or more predictions for the portion of the plurality of nodes can be generated based on the network data.