Predictive Network Resource Orchestration for Event Attendance
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Solution Overview
Problem
Existing telecommunications systems face challenges in predicting and managing network demand during large gatherings, leading to quality of service issues such as network access, capacity, and latency, as they struggle to accurately forecast attendance and network usage.
Innovation Solution
A system and method that collect data on potential attendees, predict attendance and network usage using machine learning algorithms, instantiate virtual network resources based on predictions, and update algorithms based on actual event data to improve forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional telecommunications infrastructure is used during large gatherings, then network access and capacity are limited, but service providers cannot accurately predict and provision sufficient resources in advance
Solution Approach 1:
The system performs preliminary actions by collecting data from multiple sources (social media, search engines, ticketing systems) and using machine learning algorithms to predict attendance and network usage before events occur. This allows service providers to provision network resources in advance, ensuring quality of service during large gatherings without relying on traditional reactive infrastructure scaling.
2Reliability
If service providers provision excess network resources for large gatherings, then quality of service is maintained, but resource waste and increased costs occur
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring actual event attendance and network usage, then using this data to refine and retrain machine learning models. This closed-loop feedback enables more accurate predictions for future events, allowing service providers to optimize resource provisioning and avoid both over-provisioning and under-provisioning scenarios.
Solution Approach 2:
The system dynamically adjusts prediction parameters and resource allocation based on multiple data sources including weather forecasts, event type classifications, historical attendance patterns, and real-time social media sentiment analysis. These parameter changes enable precise resource provisioning that matches actual demand without waste.
3Measurement precision
If machine learning algorithms are used to predict attendance and network usage, then resource provisioning accuracy improves, but system complexity increases
Solution Approach 1:
The system introduces intermediary components including data normalization layers, feature extraction modules, and ensemble learning architectures that bridge raw multi-source data and final predictions. These intermediaries manage system complexity by breaking down the prediction task into manageable stages while maintaining high accuracy through coordinated processing across multiple algorithmic layers.
4Measurement precision
If real-time data collection from multiple sources is performed, then prediction accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system segments data collection and processing into distinct modules: social media monitoring, search engine analysis, ticketing system integration, weather data acquisition, and historical data retrieval. Each segment processes specific data types independently using optimized algorithms, then combines results through weighted aggregation. This segmentation reduces overall processing time while maintaining comprehensive data analysis for accurate predictions.
Data Source
AI summary
A method includes collecting data relating to an event, the data including a timing of the event and a location of the event, predicting attendance at the event based on the collected data, predicting network usage based on the predicted attendance of the event, instantiating virtual network resources based on the predicted network usage, collecting post event data relating to actual attendance and network metrics, comparing the post event data with the predicted attendance and predicted network usage and updating a prediction algorithm based on the comparing step.