Methods and systems for autonomous cloud application operations

Abstract

In one aspect, a computerized method for managing autonomous cloud application operations includes the step of providing a cloud-based application. The method includes the step of implementing a discovery phase on the cloud-based application. The discovery phase comprises ingesting data from the cloud-based application and building an application graph of the cloud-based application. The application graph represents a structural topology and a set of directional dependencies within and across the layers of the cloud-based application. The method includes the step of, with the application graph, implementing anomaly detection on the cloud-based application by building a set of predictive behavior models from a predictive understanding of the complete application using a priori curated knowledge and one or more machine learning (ML) models. The set of predictive behavior models fingerprints a behavior of the cloud-based application behavior. The method predicts expected values of key indicators. The method detects one or more anomalies in the cloud-based application. The method includes the step of implementing causal analysis of the one or more detected anomalies. The causal analysis includes receiving a set of relevant labels and a set of metadata related to the one or more detected anomalies, and the structure of the application graph. The method generates a causal analysis information. The method includes the step of implementing problem classification by classifying the one or more anomalies and causal analysis information into a taxonomy. The taxonomy includes a set of details on the nature of the problem and a set of remediation actions.