Network Traffic Prediction for Proactive Bandwidth Reallocation
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Solution Overview
Problem
Existing data networks face challenges in managing bandwidth efficiently due to increasing digital content demands, leading to congestion and suboptimal use of network capacity.
Innovation Solution
Predicting future network traffic using historical data and clustering algorithms to group similar traffic patterns, allowing for preemptive bandwidth reallocation based on probability distributions and quality of service thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bandwidth is allocated to maintain quality of service for all traffic patterns, then service quality is improved, but network capacity utilization deteriorates due to conservative allocation
Solution Approach 1:
The system performs preliminary actions by predicting future traffic patterns using historical data and clustering algorithms. It proactively reallocates bandwidth before congestion occurs, allowing the network to prepare for anticipated traffic surges and maintain QoS without permanently over-provisioning resources.
Solution Approach 2:
The system implements dynamic bandwidth reallocation based on predicted traffic patterns. Instead of static allocation, the network continuously adapts bandwidth distribution to match forecasted demands, optimizing the balance between maintaining QoS and maximizing capacity utilization through probabilistic predictions.
2Productivity
If bandwidth is reallocated to optimize network capacity, then productivity is improved, but reliability deteriorates due to potential QoS violations
Solution Approach 1:
The system uses feedback mechanisms by continuously monitoring actual traffic patterns and comparing them with predictions. This feedback loop allows the system to refine its probability distributions and clustering models, improving the accuracy of predictions and enabling more confident bandwidth reallocation decisions that maintain QoS while optimizing capacity.
Solution Approach 2:
The system changes parameters by using probability distributions and statistical parameters (mean, median, percentiles) to characterize traffic patterns. By adjusting bandwidth allocation based on predicted statistical parameters rather than fixed values, the system can optimize capacity utilization while maintaining acceptable QoS levels with known probabilities.
3Productivity
If historical traffic data is collected and analyzed to predict future patterns, then productivity is improved through optimization, but device complexity increases due to clustering algorithms and probability distributions
Solution Approach 1:
The system implements self-service by automatically collecting, analyzing, and processing traffic data without requiring manual intervention. The clustering algorithms and probability distribution models operate autonomously to generate predictions and trigger bandwidth reallocation decisions, reducing operational complexity despite the sophisticated analytics employed.
Solution Approach 2:
The system creates simplified representations (copies) of complex traffic patterns through clustering. By grouping similar traffic histories into clusters and representing them with statistical parameters, the system manages complexity by working with condensed models rather than raw, high-dimensional traffic data.
Data Source
AI summary
The present disclosure generally relates to apparatus, software and methods for predicting future network traffic. The disclosed apparatus, software and methods alleviate congestion and/or increase overall traffic flow by providing methods for reallocating future idle capacity.