Predictive resource allocation in an edge computing network utilizing machine learning

A system for reducing latency in providing a computing service, the system comprising:

• a plurality of edge nodes having different physical locations;
• at least one processor;
• and
• memory, operatively connected to the at least one processor and storing instructions that, when executed by the at least one processor, cause the at least one processor to perform a set of operations comprising:
• training a machine learning model based on historical request data for a plurality of requests;
• assigning a unique identifier to one or more mobile computing devices to allow for tracking of the mobile computing device;
• generating, from the trained machine learning model, a prediction for a type of computing service to be requested at a predicted time and a predicted location based on at least geographic location data obtained by the tracking of the one or more mobile computing devices;
• based on the generated predicted location, identifying an edge node, from a plurality of edge nodes, based on a physical location of the edge node;
• and
• based on the generated predicted type of computing service and the predicted time, allocating computing resources for the computing service on the identified edge node prior to the predicted time.