Communication Link Selection Using Predictive Failure Assessment
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Solution Overview
Problem
Current communication networks face challenges in efficiently managing session admissions by not adequately predicting the impact of new sessions on existing network links, leading to potential service degradation and resource bottlenecks.
Innovation Solution
A method that uses predictive models to assess the suitability of communication links by evaluating historical performance, expected performance, and future demands, selecting the most likely to fail link, and determining the impact of new sessions on existing ones, ensuring that the network can support the session without degrading existing services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If new sessions are admitted into the network without predictive assessment, then network throughput and session acceptance increase, but network reliability and quality of service for existing sessions deteriorate
Solution Approach 1:
The patent applies preliminary action by performing predictive modeling and link failure probability assessment before admitting new sessions. The system evaluates historical performance data, current network state, and predicted future demands to identify links most likely to fail before committing resources. This advance assessment prevents admission decisions that would degrade existing services, resolving the contradiction between high session acceptance rates and maintaining quality of service reliability.
2Reliability
If predictive modeling and comprehensive link assessment are performed for every session admission, then network reliability and quality of service improve, but computational complexity and processing time increase
Solution Approach 1:
The patent applies local quality by focusing predictive assessment only on specific links identified as most likely to fail, rather than uniformly analyzing all network links for every session admission. The system calculates link failure probabilities and identifies bottleneck links, then concentrates detailed predictive modeling efforts on those specific links. This localized approach maintains high quality of service reliability while reducing overall computational complexity compared to comprehensive network-wide analysis.
3Speed
If session admission decisions are made without considering historical link performance, then processing speed increases, but measurement precision and predictive accuracy of network capacity deteriorate
Solution Approach 1:
The patent applies copying by creating simplified predictive models that replicate the essential patterns learned from historical link performance data. Instead of analyzing complete historical datasets for every admission decision, the system uses pre-processed historical information to generate predictive indicators and link failure probability estimates. This copying approach maintains high predictive accuracy by preserving key historical insights while enabling faster admission decision processing.
Data Source
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AI summary
A session admission process is provided which identifies the weakest link in a route between a first node and a second node and determines if the route is able to cope if the session is admitted. The suitability of a link is determined on the basis of: historical link performance; the predicted future performance of the link; and the predicted future demands on the link from other sessions supported by that link