US 12,395,433 B2
Machine learning segment routing for multiple traffic matrices
Muralidharan Kodialam, Austin, TX (US); and Tv Lakshman, Morganville, NJ (US)
Assigned to Nokia Solutions and Networks Oy, Espoo (FI)
Filed by Nokia Solutions and Networks Oy, Espoo (FI)
Filed on Apr. 11, 2023, as Appl. No. 18/298,660.
Prior Publication US 2024/0348547 A1, Oct. 17, 2024
Int. Cl. H04L 45/24 (2022.01); H04L 41/16 (2022.01); H04L 45/00 (2022.01); H04L 47/12 (2022.01); H04W 28/02 (2009.01); H04W 28/20 (2009.01); H04W 28/26 (2009.01); H04W 36/22 (2009.01)
CPC H04L 47/12 (2013.01) [H04L 41/16 (2013.01); H04L 45/34 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, as a first input to a machine learning model, a first traffic matrix, wherein the first traffic matrix indicates traffic demand between at least a source node and a destination node of a network;
receiving, as a second input to the machine learning model, information regarding links associated with each segment of the network, wherein each segment corresponds to a pair of nodes of the network;
transforming, by the machine learning model, at least one deflection parameter into at least one non-linear deflection parameter, wherein the at least one deflection parameter indicates a fractional amount of traffic that is carried between the source node and the destination node and deflected to an intermediary node;
determining, by the machine learning model, a total amount of segment flow using the at least one non-linear deflection parameter applied to the traffic demand of the first traffic matrix;
determining, by the machine learning model, a link flow for each of the links using the total amount of segment flow and the second input to the machine learning model;
determining, by the machine learning model, link utilization for each of the links using the link flows and a capacity for each of the links;
learning, by the machine learning model using a gradient descent, a minimum of a maximum amount of the link utilization over the links by at least adjusting a value of the at least one non-linear deflection parameter; and
providing, by the machine learning model, an output comprising the at least one non-linear deflection parameter and/or the at least one non-linear deflection parameter transformed back into a linear domain.