Two-Tiered ML Architecture for Network Settlement Time Prediction
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Solution Overview
Problem
Conventional systems lack mechanisms to determine hard disk speed of receiving computers and network routes, making it difficult to predict communication completion times across disparate computer networks due to unknown factors and biases in existing prediction methods.
Innovation Solution
A two-tiered machine learning architecture is employed to predict communication settlement times by determining probabilities of various factors independently and aggregating them to select a rule set based on current network conditions, preventing bias and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction methods are used, then prediction simplicity is maintained, but prediction accuracy deteriorates due to unknown factors and biases
Solution Approach 1:
The prediction system is divided into multiple independent components: a first machine learning model that predicts network conditions, a second machine learning model that predicts communication settlement time, and an aggregation layer that combines their outputs. This segmentation allows each model to focus on specific factors, improving overall prediction accuracy while managing complexity through modular design.
Solution Approach 2:
The aggregation layer acts as an intermediary between the two machine learning models and the final prediction output. It receives predictions from both models, combines them according to weighted rules, and produces the final settlement time prediction. This intermediary structure enables accurate integration of multiple prediction sources while maintaining system organization.
2Reliability
If real-time network condition data is collected from multiple sources, then prediction reliability is improved, but data processing complexity increases
Solution Approach 1:
Network condition data from multiple sources (network devices, computers, storage devices) is processed by separate machine learning models rather than a single complex system. The first model handles network condition predictions while the second model handles settlement time predictions, allowing specialized processing that improves reliability without overwhelming complexity.
Solution Approach 2:
The machine learning models automatically process and interpret network condition data from multiple sources without requiring manual intervention. The models self-adjust to account for biases and uncertainties in the data, improving prediction reliability while reducing the operational complexity of data processing.
3Measurement precision
If multiple factors are considered in prediction, then prediction comprehensiveness is improved, but computational requirements increase
Solution Approach 1:
The comprehensive prediction is achieved by segmenting the analysis into two focused models: one for network conditions and one for settlement time. Each model processes a subset of factors relevant to its specific prediction task, achieving comprehensiveness through coordinated specialization rather than single-model omniscience, thus reducing overall computational energy consumption.
Data Source
AI summary
Systems for predicting communication settlement times across disparate networks store a first tier of a machine learning architecture comprising multiple machine learning models and an aggregation layer; store a second tier comprising rule sets for predicting settlement times; receive multiple data feeds corresponding to multiple communication data types; generate feature inputs based on the data feeds; input the feature inputs into the respective models to generate respective outputs; generate, using the aggregation layer, a third feature input based on the outputs; determine, based on the third feature input, a first rule set for predicting settlement times; receive a communication; predict a settlement time based on the first rule set; determine an aggregated communication load at a first time based on the settlement time; determine a performance availability requirement based on the load; determine a recommendation based on the performance availability requirement; and generate the recommendation based on the settlement time.