US 12,470,464 B2
Cloud topology optimization using a graph convolutional network model
Girish Dhanakshirur, Bangalore (IN); Hemant Kumar Sivaswamy, Bangalore (IN); Vidya Chandrashekar, Bangalore (IN); Rachana Vishwanathula, Hyderabad (IN); Deepak Rai, Mangalore (IN); and Saraswathi Sailaja Perumalla, Visakhapatnam (IN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Mar. 28, 2024, as Appl. No. 18/619,928.
Prior Publication US 2025/0310204 A1, Oct. 2, 2025
Int. Cl. H04L 41/12 (2022.01); H04L 41/16 (2022.01); H04L 41/5009 (2022.01)
CPC H04L 41/12 (2013.01) [H04L 41/16 (2013.01); H04L 41/5009 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for network topology optimization, said method comprising:
training, by one or more processors of a computer system, a graph convolutional network (GCN) model using training network topology datasets as input,
wherein a collection of network topology datasets comprises the training network topology datasets,
wherein each network topology dataset in the collection includes: (i) a specified network topology and an associated optimal network topology and an optimality function value for the optimal network topology, (ii) relative weights of optimality parameters including performance (p), availability (a), and scalability(s), and (iii) an identification of an optimality function of the optimality parameters weighted by the relative weights,
wherein the specified network topology in each network topology dataset comprises components, relationships between the components, and metadata pertaining to the components,
wherein each output node in an output layer of the GCN model includes an optimality function value of the optimality function for a different candidate network topology selected from the group consisting of an expanded network topology relative to the specified network topology and a contracted network topology relative to the specified network topology, and
wherein one of the output nodes identifies an optimum network topology relative to the specified network topology as being the candidate network topology having a highest optimality function value in comparison with the optimality function value in all of the other output nodes.