Proactive Traffic Restoration via Forecasting Engine
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Solution Overview
Problem
Conventional traffic restoration mechanisms in networks are reactive, leading to service disruptions and do not account for progressive worsening conditions of signal paths, resulting in pessimistic system design and limited global optimization possibilities.
Innovation Solution
A system employing a model-driven approach for proactive traffic restoration, using a forecasting engine to predict traffic quality metrics and trigger restoration actions before failures occur, allowing for real-time modeling and forecasting of traffic patterns, and considering instantaneous network states, with the ability to handle any rate of metric evolution and both network recovery and new service configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional reactive traffic restoration mechanisms are used, then service disruptions occur after failures, but system design becomes pessimistic and limited in global optimization
Solution Approach 1:
The system performs preliminary actions by proactively detecting degradation trends in signal quality metrics (OSNR, BER, Q-factor) and initiating restoration procedures before actual failures occur. The forecasting engine analyzes historical and real-time data to predict future failures, allowing the system to switch traffic to protection paths in advance, thereby avoiding service disruptions without requiring pessimistic worst-case design margins.
Solution Approach 2:
The system implements continuous feedback mechanisms by monitoring signal quality metrics in real-time and using this information to dynamically adjust restoration decisions. The forecasting engine receives ongoing measurements of OSNR, BER, and Q-factor, updates its predictions accordingly, and triggers restoration when degradation trends indicate imminent failure. This feedback-driven approach enables optimized restoration timing rather than relying on static thresholds or reactive responses.
2Reliability
If reactive restoration approach is taken, then service disruptions occur, but real-time view and proactive actions on traffic and link states are not provided
Solution Approach 1:
The system performs preliminary restoration actions by detecting degradation trends in signal quality metrics and initiating recovery procedures before actual failures occur. The forecasting engine analyzes historical and real-time data to predict future failures, allowing the system to switch traffic to protection paths in advance, thereby avoiding service disruptions and eliminating response time delays associated with reactive restoration.
Solution Approach 2:
The system implements continuous feedback mechanisms by monitoring signal quality metrics (OSNR, BER, Q-factor) in real-time and using this information to dynamically adjust restoration decisions. This feedback-driven approach enables the system to maintain an up-to-date real-time view of traffic and link states, triggering proactive restoration when degradation trends indicate imminent failure rather than waiting for actual service disruption.
3Reliability
If conventional threshold-based failure prediction is used, then restoration is triggered after failure, but progressive worsening conditions of working signal path are not accounted for
Solution Approach 1:
The system implements continuous feedback mechanisms by monitoring signal quality metrics (OSNR, BER, Q-factor) in real-time and using this information to dynamically adjust restoration decisions. This feedback-driven approach enables the system to detect progressive worsening conditions by analyzing trends in degradation over time, rather than relying on static thresholds that only trigger after failure occurs.
Solution Approach 2:
The system changes the approach from using fixed threshold parameters to analyzing dynamic parameter trends. The forecasting engine examines the rate of change and progression of signal quality metrics (OSNR degradation, BER increase, Q-factor decline) over time, allowing it to predict future failures based on worsening conditions rather than waiting for thresholds to be exceeded. This enables restoration to be triggered based on degradation trends before actual failure occurs.
Data Source
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AI summary
A system for proactive traffic restoration in a network, said system (1) comprising: at least one forecasting engine (2) adapted to model and forecast traffic patterns of at least one traffic channel along a signal path of said network to provide forecast traffic quality metrics, y; and at least one time-to-failure, TTF, analyzer (3) adapted to calculate a time-to-failure, TTF, forecast for the traffic channel based on the forecast traffic quality metrics, y, wherein the calculated time-to-failure, TTF, forecast is evaluated to trigger a proactive network traffic restoration.