Dynamic Bandwidth Allocation via Pattern Recognition
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Solution Overview
Problem
Current network connectivity services often fail to dynamically adjust bandwidth to meet fluctuating demand, leading to inefficiencies in resource utilization, as users are not always aware of the dynamic capacity feature that could optimize bandwidth allocation based on real-time usage patterns.
Innovation Solution
A computer-implemented method using a pattern recognition algorithm to analyze network utilization data and identify patterns indicative of dynamic capacity needs, allowing for notification to users and potential adjustments in bandwidth allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static bandwidth allocation is used to meet peak demand, then service reliability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic bandwidth allocation that automatically adjusts capacity based on real-time demand patterns. The system transitions from static to dynamic resource allocation, allowing bandwidth to fluctuate according to actual usage needs rather than being fixed at peak levels, thereby improving resource utilization efficiency while maintaining service reliability.
Solution Approach 2:
The system employs machine learning models that automatically analyze usage patterns and predict future bandwidth needs without human intervention. The dynamic capacity management operates autonomously, self-adjusting resource allocation based on learned patterns from historical and real-time data, eliminating the need for manual configuration while optimizing both reliability and efficiency.
2Loss of energy
If dynamic capacity feature is implemented, then resource utilization efficiency is improved, but user awareness and adoption deteriorates
Solution Approach 1:
The system implements feedback mechanisms that notify users about dynamic capacity adjustments and their benefits. By providing visibility into how dynamic allocation optimizes resource usage and reduces costs, users become aware of and more likely to adopt the feature, transforming the information asymmetry that previously hindered adoption.
3Adaptability or versatility
If manual configuration of dynamic capacity is required, then adaptability is improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically detects usage patterns and configures dynamic capacity parameters without requiring manual user input. The machine learning models analyze historical data and autonomously determine optimal bandwidth allocation strategies, making the system both highly adaptable to different usage scenarios and easy to operate by eliminating complex configuration steps.
Data Source
AI summary
A network customer may support a plurality of network connectivity services (such as an E-line). A network connectivity service may experience spikes of traffic, and therefore, spikes of bandwidth usage. Dynamic capacity allows a network connectivity service to increase its available bandwidth during such traffic spikes. A computer-implemented method is disclosed that facilitates identifying network customers that might be interested in purchasing dynamic capacity. The method comprises collecting bandwidth utilization data of network connectivity services supported by each network customer, and identifying those connectivity services that exhibit patterns (e.g., cogent peaks) in their utilization data indicating the network connectivity service is a candidate for dynamic capacity. A trained pattern recognition algorithm is applied on collected utilization data of all network connectivity services and identifies those connectivity services that match the patterns, within a range of tolerance, in their utilization.