Reinforcement Learning Optical Network Re-Grooming
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Solution Overview
Problem
Existing methods for optical network re-grooming, such as Integer Linear Programming (ILP) and Genetic Algorithms (GA), are limited in adaptability and cannot effectively handle dynamic changes in optical networks, failing to consider multiple signals and topology information, which hampers their ability to make incremental changes and prioritize services based on various factors like latency and historical challenges.
Innovation Solution
The implementation of Reinforcement Learning (RL) for optical network re-grooming, utilizing a per-edge fragmentation metric and edge-crossing vectors to evaluate actions, allows for adaptable re-grooming strategies that consider network topology and provide quantitative benefits for each possible action, enabling the network to adapt to changing conditions and prioritize services optimally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Genetic Algorithms or Integer Linear Programming are used for network re-grooming, then spectrum recovery and link congestion reduction are improved, but adaptability to dynamic network changes and consideration of multiple signals are limited
Solution Approach 1:
The patent implements a dynamic re-grooming approach where the network continuously monitors its state and adjusts service routing in real-time based on changing conditions. The system transitions from static optimization to dynamic adaptation, allowing the network to respond to evolving traffic patterns, failures, and capacity changes without requiring complete re-optimization.
Solution Approach 2:
The patent introduces feedback mechanisms where network performance metrics (utilization, congestion, service quality) are continuously measured and fed back into the control system. This feedback loop enables the system to learn from past actions and adjust future re-grooming decisions, improving adaptability to dynamic conditions while maintaining spectrum recovery benefits.
2Reliability
If comprehensive re-grooming operations are performed to optimize network routing, then network de-fragmentation is improved, but operation time and disruption to services increase
Solution Approach 1:
The patent applies partial re-grooming by selectively optimizing only the most critical services or network segments rather than performing comprehensive re-optimization of all services. This partial action approach achieves significant de-fragmentation benefits while minimizing the time and disruption required, focusing resources on high-impact areas only.
Solution Approach 2:
The system performs preliminary analysis and planning of re-grooming operations to identify optimal service migration paths and timing. By preparing re-grooming plans in advance and scheduling them during appropriate maintenance windows, the system reduces actual operation time and minimizes service disruption while achieving thorough network optimization.
3Productivity
If re-grooming operations are performed to rearrange services optimally, then network capacity is improved, but complexity of coordinating multiple services and maintenance windows increases
Solution Approach 1:
The patent segments the network into manageable zones or segments and handles re-grooming operations independently in each segment. This segmentation reduces the coordination complexity by localizing optimization decisions to smaller sub-problems, making it easier to manage maintenance windows and service migrations across the entire network while maintaining overall capacity improvements.
Data Source
AI summary
Systems and methods include obtaining a network state of a network having a plurality of nodes interconnected by a plurality of links and with services configured between the plurality of nodes on the plurality of links; utilizing a reinforcement learning engine to analyze the services and the network state to determine modifications to one or more candidate services of the services to increase a value of the network state; and, responsive to implementation of the modification to the one or more candidate services, updating the network state based thereon. The modifications can include changes to any of routing, modulation, and spectral assignment to the one or more candidate services.