System and method for moveable cloud cluster functionality usage and location forecasting

Abstract

A method includes collecting data relating to an event, the data including a timing of the event and a location of the event, predicting attendance at the event based on the collected data, predicting network usage based on the predicted attendance of the event, instantiating virtual network resources based on the predicted network usage, collecting post event data relating to actual attendance and network metrics, comparing the post event data with the predicted attendance and predicted network usage and updating a prediction algorithm based on the comparing step.

1 . A method comprising:
 predicting, using a machine learning model, by a processing system including a processor, wireless communication network usage at an event based on a predicted attendance of the event and historical wireless communication network usage of potential attendees of the event to determine predicted wireless communication network usage;   instantiating, by the processing system, virtual network resources based on the predicted wireless communication network usage;   collecting, by the processing system, post event wireless communication network metrics; and   updating, by the processing system, the machine learning model using the post event wireless communication network metrics as training data.   
     
     
         2 . The method of  claim 1 , wherein the predicting the wireless communication network usage is further based on personalized information of the potential attendees. 
     
     
         3 . The method of  claim 1 , further comprising:
 assigning, by the processing system, weights of likelihood of attendance to the potential attendees, wherein the weights of likelihood of attendance are based at least in part on internet searching behavior of the potential attendees; and   compiling, by the processing system, a listing of potential attendees based on the weights of likelihood of attendance.   
     
     
         4 . The method of  claim 1 , wherein the predicted wireless communication network usage includes predicted usage type. 
     
     
         5 . The method of  claim 1 , wherein the predicted wireless communication network usage includes predicted usage duration. 
     
     
         6 . The method of  claim 1 , wherein the predicting the wireless communication network usage is further based on text messages of the potential attendees. 
     
     
         7 . The method of  claim 1 , wherein the predicting the wireless communication network usage is further based on social media posts of the potential attendees. 
     
     
         8 . The method of  claim 1 , wherein the predicting the wireless communication network usage is further based on internet searches of the potential attendees. 
     
     
         9 . The method of  claim 1 , wherein the predicting the wireless communication network usage is further based on internet searches of the potential attendees. 
     
     
         10 . The method of  claim 1 , wherein the predicting the wireless communication network usage is further based on a location of the event. 
     
     
         11 . The method of  claim 1 , wherein the predicting the wireless communication network usage is further based on a timing of the event. 
     
     
         12 . The method of  claim 1 , wherein the predicting the wireless communication network usage is further based on a timing of the event. 
     
     
         13 . The method of  claim 1 , wherein the predicting the wireless communication network usage is further based on traffic patterns in a vicinity of the event. 
     
     
         14 . A device comprising:
 a processing system including a processor; and   a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:   predicting, using a machine learning model, wireless communication network usage at an event based on historical wireless communication network usage of potential attendees of the event to determine predicted wireless communication network usage;   instantiating virtual network resources based on the predicted wireless communication network usage;   collecting post event wireless communication network metrics; and   updating the machine learning model using the post event wireless communication network metrics as training data.   
     
     
         15 . The device of  claim 14 , wherein the post event wireless communication network metrics include actual attendance at the event. 
     
     
         16 . The device of  claim 14 , wherein the post event wireless communication network metrics include actual wireless communication network usage at the event. 
     
     
         17 . A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:
 predicting, using a machine learning model, wireless communication network usage at an event based on historical wireless communication network usage of potential attendees of the event to determine predicted wireless communication network usage;   instantiating virtual network resources based on the predicted wireless communication network usage;   collecting post event wireless communication network metrics, wherein the post event wireless communication network metrics include actual wireless communication network usage at the event; and   updating the machine learning model using the post event wireless communication network metrics as training data.   
     
     
         18 . The non-transitory machine-readable medium of  claim 17 , wherein the predicting the wireless communication network usage is further based on text messages of the potential attendees. 
     
     
         19 . The non-transitory machine-readable medium of  claim 17 , wherein the predicting the wireless communication network usage is further based on social media posts of the potential attendees. 
     
     
         20 . The non-transitory machine-readable medium of  claim 17 , wherein the predicting the wireless communication network usage is further based on internet searches of the potential attendees.