Traffic-Weighted Network Availability Analysis
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Solution Overview
Problem
Current methods for predicting the availability of telecommunications systems fail to accurately account for traffic distribution, leading to inaccurate assessments of system reliability, especially in call center environments with varying traffic patterns.
Innovation Solution
A method that analyzes traffic flow and volume across communications networks by dividing network components into subsets, determining intra-subset traffic flow, calculating traffic-weighted downtime, and using this data to project availability, considering the impact of traffic rerouting and redundancy requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional availability calculation methods are used that assume equal traffic distribution, then the calculation process is simple, but the accuracy of availability assessment deteriorates
Solution Approach 1:
The network is segmented into multiple subsets based on traffic patterns and component groupings. Each subset is analyzed separately with its own traffic flow characteristics, allowing accurate availability assessment without requiring uniform traffic distribution assumptions across the entire network.
Solution Approach 2:
The patent applies local quality by assigning different traffic flow characteristics to different network subsets based on their specific traffic patterns. High-traffic areas receive higher weighting factors in availability calculations, while low-traffic areas receive lower weighting, reflecting the actual local traffic conditions rather than using a uniform approach.
2Measurement precision
If traffic-weighted availability analysis is implemented to improve accuracy, then availability assessment precision improves, but device complexity increases
Solution Approach 1:
The network is divided into manageable subsets with distinct traffic characteristics, making the complex traffic-weighted analysis tractable by processing each subset separately rather than attempting to analyze the entire network as a single homogeneous unit.
Solution Approach 2:
The patent introduces traffic flow parameters and weighting factors as additional dimensions for analysis. By changing the parameters from simple uniform weighting to traffic-based weighting, the system achieves higher precision while managing complexity through structured parameter assignment.
3Ease of manufacture
If uniform redundancy configuration is applied across all network components, then implementation is straightforward, but outage minutes in high-traffic areas cannot be minimized
Solution Approach 1:
The patent applies local quality by configuring redundancy levels based on local traffic characteristics. High-traffic areas receive enhanced redundancy configurations with higher weighting factors, while low-traffic areas use standard redundancy, optimizing reliability where it matters most without uniform over-provisioning.
Solution Approach 2:
The redundancy configuration parameters are changed from uniform values to traffic-weighted values. The weighting factors derived from traffic flow analysis modify the redundancy allocation, allowing the system to minimize outage minutes in high-traffic areas while maintaining acceptable redundancy elsewhere.
Data Source
AI summary
A system for analyzing an availability of at least part of a communications network is provided. The network includes a set of network components, that are further divided into first and second subsets of network components. Each of the first and second subsets includes a plurality of different components in the network component set. The system includes an availability prediction assessment tool that is operable to (I) determine a first traffic flow exchanged between first and second endpoint groupings in the first subset, the first traffic flow being a first percentage of a total traffic flow in the communications network; (ii) for a plurality of first subset members involved in the first traffic flow, determine a corresponding downtime; (iii) determine a total of the downtimes corresponding to the plurality of first subset members; (iv) multiply the total of the downtimes by the first percentage to provide a first traffic weighted total downtime for the first subset; and (v) determine an availability of the first subset for the first traffic flow based on the first traffic weighted total downtime for the first subset.