Path Computation in Optical Networks Using Constrained Graph Models
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Solution Overview
Problem
Photonic networks present complex topologies and constraints, such as wavelength blocking and unique nodal constraints, which complicate path computation compared to digital networks, and existing algorithms like Dijkstra fail to accurately compute paths due to these constraints.
Innovation Solution
A processor-implemented method that modifies the graph model to reflect photonic constraints by translating undirected graphs to directed graphs, applying wavelength capability and availability constraints, and nodal connectivity constraints using Dijkstra's algorithm, and computes disjoint paths using Suurballe's algorithm, while maintaining bit vectors and connectivity lists for wavelength and nodal constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If Dijkstra's algorithm is used for path computation in photonic networks, then the algorithm is simple and easy to implement, but it fails to accurately compute paths due to photonic constraints such as wavelength blocking and nodal constraints
Solution Approach 1:
The patent modifies the graph model parameters by translating from undirected to directed graphs, adding wavelength capability constraints, wavelength availability constraints, and nodal connectivity constraints. These parameter changes enable Dijkstra's algorithm to accurately compute paths in photonic networks while considering unique photonic constraints.
2Measurement precision
If the graph model is modified to reflect photonic constraints by translating undirected graphs to directed graphs, then path computation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the graph model into distinct components: vertices representing nodes, directed edges representing links with wavelength constraints, and connectivity pairs representing nodal connectivity constraints. This segmentation allows systematic handling of photonic constraints while maintaining computational tractability.
Solution Approach 2:
The patent transforms the graph model from undirected to directed, adding a directional dimension to edges. This dimensional change enables the model to represent wavelength blocking and nodal connectivity constraints that are inherently directional in photonic networks.
3Reliability
If bit vectors and connectivity lists are maintained for wavelength and nodal constraints, then constraint tracking is improved, but memory usage and processing overhead increase
Solution Approach 1:
The patent uses bit vectors to represent wavelength capability and availability constraints, where each bit corresponds to a wavelength. This compact representation efficiently tracks constraint states while minimizing memory usage compared to storing full constraint information for each wavelength.
Data Source
AI summary
A path computation method includes defining photonic constraints associated with a network, wherein the photonic constraints include wavelength capability constraints at each node in the network, wavelength availability constraints at each node in the network, and nodal connectivity constraints of each node in the network, and performing a constrained path computation in the network using Dijkstra's algorithm on a graph model of the network with the photonic constraints considered therein. An optical network includes a plurality of interconnected nodes each including wavelength capability constraints, wavelength availability constraints, and nodal connectivity constraints, and a path computation element associated with the plurality of interconnected photonic nodes, wherein the path computation element is configured to perform a constrained path computation through the plurality of interconnected nodes using Dijkstra's algorithm on a graph model with the photonic constraints considered therein.