METHODS AND SYSTEMS FOR DYNAMIC RE-CLUSTERING OF NODES IN COMPUTER NETWORKS USING MACHINE LEARNING MODELS

Abstract: methods and systems for the dynamically re-clustering of nodes in clusters to provide optimal performance and/or the most efficient use of resources through the use of machine learning models. specifically, the methods and systems may determine a cluster that optimally performs and/or has the most efficient use of resources based on a first machine learning model. the methods and system may then retrieve available substitute nodes from other domains and/or networks that may lie outside the cluster, but may nonetheless be available to, or accessed by the cluster. the methods and systems may then generate an additional plurality of clusters using one or more of the original nodes of the originally selected clusters and/or one or more of the available substitute nodes.

Inventor(s): Austin Walters, Mark Watson, Galen Rafferty, Jeremy Goodsitt

CPC Classification: H04L41/16 (using machine learning or artificial intelligence)

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