US 12,255,821 B2
Telemetry-based segment routing using machine learning
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 Jun. 16, 2023, as Appl. No. 18/336,675.
Prior Publication US 2024/0430202 A1, Dec. 26, 2024
Int. Cl. H04L 47/122 (2022.01); G06N 3/084 (2023.01); H04L 43/0882 (2022.01)
CPC H04L 47/122 (2013.01) [G06N 3/084 (2013.01); H04L 43/0882 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, as a first input to a machine learning model, a measured link load that is measured for a link of a network;
receiving, as a second input to the machine learning model, information indicating a network topology of the network;
receiving, as a third input to the machine learning model, at least one deflection parameter, wherein the at least one deflection parameter indicates a fractional amount of traffic that is currently being carried between a source node and a destination node and deflected through an intermediary node;
learning, by the machine learning model, a first output to provide at least one updated deflection parameter, wherein the at least one updated deflection parameter indicates the fractional amount of traffic that is to be carried between the source node and the destination node and deflected through the intermediate node; and
learning, by the machine learning model, a second output to provide dual variables that serve as a surrogate for a traffic matrix that could have generated the measured link load that is measured for the link of the network.