US 12,147,886 B2
Predictive microservices activation using machine learning
Meng Wan, Beijing (CN); Li Na Guo, Beijing (CN); Wang Liu, Beijing (CN); Xue Rui Hu, Beijing (CN); Mei Qin Si, Beijing (CN); and Hong Yan Zhang, Beijing (CN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Oct. 1, 2020, as Appl. No. 17/060,165.
Prior Publication US 2022/0108147 A1, Apr. 7, 2022
Int. Cl. G06N 3/04 (2023.01); G06F 18/10 (2023.01); G06F 18/21 (2023.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06N 3/08 (2023.01); H04L 67/133 (2022.01); H04L 67/51 (2022.01)
CPC G06N 3/04 (2013.01) [G06F 18/10 (2023.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 18/2178 (2023.01); G06N 3/08 (2013.01); H04L 67/133 (2022.05); H04L 67/51 (2022.05)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
converting historical usage data for an application and its associated microservices to line graphs representing sequences of application operations, wherein the line graphs comprise points represented by two-dimensional coordinates arranged between an x-axis and a y-axis and normalized between −1 and 1, inclusive;
converting respective line graphs to a series of vectors;
converting respective series of vectors to a sequence of coordinates;
training a machine learning model using respective sequences of coordinates;
inputting a new sequence of coordinates representing a series of application operations to the machine learning model;
identifying a predicted microservice for future utilization based on an output vector generated by the machine learning model; and
activating the predicted microservice prior to the predicted microservice being called by the application.