US 12,112,209 B2
Application anomaly detection using trained infrastructure machine learning models
Kannan Parthasarathy, Redmond, WA (US); and Venkatanathan Krishnamachari, Woodinville, WA (US)
Assigned to MONTYCLOUD INC, Redmond, WA (US)
Filed by MONTYCLOUD INC, Redmond, WA (US)
Filed on Nov. 13, 2021, as Appl. No. 17/525,863.
Claims priority of provisional application 63/113,206, filed on Nov. 13, 2020.
Prior Publication US 2022/0156121 A1, May 19, 2022
Int. Cl. G06F 9/50 (2006.01); G06F 11/30 (2006.01); G06F 11/34 (2006.01)
CPC G06F 9/5055 (2013.01) [G06F 9/505 (2013.01); G06F 11/3051 (2013.01); G06F 11/3438 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A computing system for facilitating management of application infrastructure for plurality of users, the computing system comprising:
one or more virtualized hardware processors; and
a memory coupled to the one or more virtualized hardware processors, wherein the memory comprises a plurality of modules in the form of programmable instructions executable by the one or more virtualized hardware processors, wherein the plurality of modules comprises:
an infrastructure identification module configured to identify a set of infrastructure components corresponding to an application hosted on an application server;
a configuration determination module configured to determine configuration information associated with the application based on the identified set of infrastructure components;
a pattern identification module configured to identify a plurality of patterns of the application using a dimensionality reduction technique, based on the determined configuration information of the set of infrastructure components;
a fingerprint generation module configured to generate one or more application fingerprints corresponding to the application based on the identified plurality of patterns;
a model generation module configured to generate a trained infrastructure management based Machine Learning (ML) model, wherein in generating the trained infrastructure management based ML model, the model generation module is configured to:
capture behavioral information associated with the application;
classify the captured behavioral information into at least one of one or more reactions and one or more observations based on one or more events, wherein the one or more events comprise: patch, task execution, scaling pattern and at least one of: user initiated, environmental and observed lifecycle changes of the set of infrastructure components;
correlate the one or more application fingerprints with the classified behavioral information; and
generate the trained infrastructure management based ML model based on result of correlation;
an anomaly determination module configured to determine one or more anomalies in the application based on the generated one or more application fingerprints by using the trained infrastructure management based ML model;
a recommendation generation module configured to generate one or more recommendations for resolving the determined one or more anomalies in the application based on the generated one or more application fingerprints and prestored information by using the trained infrastructure management based ML model; and
a data output module configured to output the determined one or more anomalies and the generated one or more recommendations to one or more user devices associated with a plurality of users for managing application infrastructure.