PREDICTIVE RESOURCE ALLOCATION IN AN EDGE COMPUTING NETWORK UTILIZING MACHINE LEARNING

Abstract: the present technology relates to improving computing services in a distributed network of remote computing resources, such as edge nodes in an edge compute network. in an aspect, the technology relates to a method that includes aggregating historical request data for a plurality of requests, wherein the aggregated historical request data a time of the request, a location of a device from which the request originated, and/or a type of service being requested. the method also incudes training a machine learning model based on the aggregated historical request data; generating, from the trained machine learning model, a prediction for a type of service to be request; identifying an edge node, from a plurality of edge nodes, based on a physical location of the edge node; and based on predicted service, allocating computing resources for the computing service on the identified edge node.

Inventor(s): Steven M. Casey, Felipe Castro, Stephen Opferman, Kevin M. McBride

CPC Classification: G06F9/5072 (Allocation of resources, e.g. of the central processing unit [CPU])

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