US 12,141,023 B2
System for monitoring computing server performance using an artificial intelligence-based plugin
Avinash Basavant Nigudkar, Mumbai (IN); Piedad Burnside, Hicksville, NY (US); Sanjay Pillay, Round Rock, TX (US); Anton Sumin, New York, NY (US); and Silvano Valle, Southold, NY (US)
Assigned to BANK OF AMERICA CORPORATION, Charlotte, NC (US)
Filed by BANK OF AMERICA CORPORATION, Charlotte, NC (US)
Filed on Jun. 1, 2022, as Appl. No. 17/829,495.
Prior Publication US 2023/0393919 A1, Dec. 7, 2023
Int. Cl. G06F 11/00 (2006.01); G06F 9/50 (2006.01); G06F 11/07 (2006.01)
CPC G06F 11/076 (2013.01) [G06F 9/505 (2013.01); G06F 11/004 (2013.01); G06F 11/0709 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for monitoring computing server performance using an artificial intelligence-based plugin, the system comprising:
at least one non-transitory storage device; and
at least one processor coupled to the at least one non-transitory storage device, wherein the at least one processor is configured to:
continuously monitor a computing device in a network to aggregate computing performance metric data associated with the computing device;
analyze the computing performance metric data using an artificial intelligence plugin, wherein the computing performance metric data comprises historical computing load data associated with the computing device;
generate one or more predictions for a future pattern of computing load for the computing device based on one or more identified patterns from the historical computing load data, wherein the one or more predictions for the future pattern of computing load for the computing device comprises a prediction that the computing load for the computing device will change during a specified timeframe;
based on the one or more predictions, generate one or more configuration changes for the computing device in response to the future pattern of computing load; and
apply the one or more configuration changes to the computing device, wherein applying the one or more configuration changes to the computing device comprises modifying computing resource allocations during the specified timeframe based on the prediction that the computing load for the computing device will change during the specified timeframe.