Method, apparatus, and computer program product for system resource volume prediction

Abstract

Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting system resource volumes for future network time intervals based upon predicted likelihoods of termination transactions for the future network time interval.

1 - 21 . (canceled) 
     
     
         22 . An apparatus comprising at least one processor and at least one non-transitory computer-readable storage medium storing instructions that, with the at least one processor, cause the apparatus to:
 receive system transaction data, wherein the system transaction data comprises a network time interval and a system wide transaction count occurring during the network time interval;   for each device rendered object of a plurality of device rendered objects,
 determine, based on the system transaction data, and first and second transaction data associated with the device rendered object, a prediction value that indicates a programmatically expected number of system wide transaction terminations for a future network time interval; and 
   responsive to determining that system resources are required to fulfill transaction terminations associated with the prediction values exceeding a system resource allocation threshold, allocate a system resource volume for correcting transaction terminations during the future network time interval.   
     
     
         23 . The apparatus of  claim 22 , wherein the at least one non-transitory computer-readable storage medium stores instructions that, with the at least one processor, further cause the apparatus to:
 for each device rendered object of the plurality of device rendered objects,
 receive the first transaction data and second transaction data, the first transaction data comprising a first timestamp and the second transaction data comprising a second timestamp, wherein the second transaction occurred subsequent to the first transaction. 
   
     
     
         24 . The apparatus of  claim 22 , wherein the system transaction data comprises a plurality of attributes associated with a plurality of users. 
     
     
         25 . The apparatus of  claim 24 , wherein the at least one non-transitory computer-readable storage medium stores instructions that, with the at least one processor, further cause the apparatus to:
 determine, using a clustering technique, one or more attributes from the plurality of attributes associated with the system transaction data, the first transaction data, the second transaction data, and the device rendered object.   
     
     
         26 . The apparatus of  claim 25 , wherein the prediction value is further determined based on the one or more attributes. 
     
     
         27 . The apparatus of  claim 22 , wherein each transaction termination is associated with a required system resource allocation defining an aggregation of network assets that is allocated to be available for fulfillment of network asset requests. 
     
     
         28 . The apparatus of  claim 22 , wherein determining the prediction value is based at least in part on a machine learning model. 
     
     
         29 . The apparatus of  claim 22 , wherein the at least one non-transitory computer-readable storage medium stores instructions that, with the at least one processor, further cause the apparatus to:
 transmit device rendered objects to a network asset requester device based on the prediction values to influence a reduction in the programmatically expected number of termination transactions.   
     
     
         30 . A computer-implemented method, comprising:
 receiving system transaction data, wherein the system transaction data comprises a network time interval and a system wide transaction count occurring during the network time interval;   for each device rendered object of a plurality of device rendered objects,
 determining, based on the system transaction data, and first and second transaction data associated with the device rendered object, a prediction value that indicates a programmatically expected number of system wide transaction terminations for a future network time interval; and 
   responsive to determining that system resources are required to fulfill transaction terminations associated with the prediction values exceeding a system resource allocation threshold, allocating a system resource volume for correcting transaction terminations during the future network time interval.   
     
     
         31 . The method of  claim 30 , further comprising:
 for each device rendered object of the plurality of device rendered objects,
 receiving the first transaction data and second transaction data, the first transaction data comprising a first timestamp and the second transaction data comprising a second timestamp, wherein the second transaction occurred subsequent to the first transaction. 
   
     
     
         32 . The method of  claim 30 , wherein the system transaction data comprises a plurality of attributes associated with a plurality of users. 
     
     
         33 . The method of  claim 32 , further comprising:
 determining, using a clustering technique, one or more attributes from the plurality of attributes associated with the system transaction data, the first transaction data, the second transaction data, and the device rendered object.   
     
     
         34 . The method of  claim 33 , wherein the prediction value is further determined based on the one or more attributes. 
     
     
         35 . A non-transitory computer-readable storage medium comprising computer code that, when executed by at least one processor of an apparatus, cause the apparatus to:
 receive system transaction data, wherein the system transaction data comprises a network time interval and a system wide transaction count occurring during the network time interval;   for each device rendered object of a plurality of device rendered objects,
 determine, based on the system transaction data, and first and second transaction data associated with the device rendered object, a prediction value that indicates a programmatically expected number of system wide transaction terminations for a future network time interval; and 
   responsive to determining that system resources are required to fulfill transaction terminations associated with the prediction values exceeding a system resource allocation threshold, allocate a system resource volume for correcting transaction terminations during the future network time interval.   
     
     
         36 . The computer-readable storage medium of  claim 35 , comprising computer code that, when executed by at least one processor of the apparatus, further cause the apparatus to:
 for each device rendered object of the plurality of device rendered objects,
 receive the first transaction data and second transaction data, the first transaction data comprising a first timestamp and the second transaction data comprising a second timestamp, wherein the second transaction occurred subsequent to the first transaction. 
   
     
     
         37 . The computer-readable storage medium of  claim 35 , wherein the system transaction data comprises a plurality of attributes associated with a plurality of users. 
     
     
         38 . The computer-readable storage medium of  claim 37 , comprising computer code that, when executed by at least one processor of the apparatus, further cause the apparatus to:
 determine, using a clustering technique, one or more attributes from the plurality of attributes associated with the system transaction data, the first transaction data, the second transaction data, and the device rendered object.   
     
     
         39 . The computer-readable storage medium of  claim 38 , wherein the prediction value is further determined based on the one or more attributes. 
     
     
         40 . The computer-readable storage medium of  claim 35 , wherein each transaction termination is associated with a required system resource allocation defining an aggregation of network assets that is allocated to be available for fulfillment of network asset requests. 
     
     
         41 . The computer-readable storage medium of  claim 35 , wherein determining the prediction value is based at least in part on a machine learning model.