Identifying and remediating system anomalies through machine learning algorithms

US11514347B2 · kind B2 · utility

Assignee

• Dell Products L.P. · US

Inventors

• Hung Dinh · Austin, US
• Pravash Ranjan Panda · Rourkela, IN
• Prince Mathew · Kelaga, IN
• Tousif Mohammed · Bengaluru, IN
• Sabu K. Syed · Austin, US
• Jatin Kamlesh Thakkar · Bengaluru, IN
• Naveen Silvester Muttikal Thomas · Santa Clara, US
• John K. Maxi · New Orleans, US

Key dates

• Filing date: Feb 1, 2019
• Grant date: Nov 29, 2022
• Expiry date: Aug 6, 2041

Classification

• Technology area (CPC H): Electricity
• CPC primary: H04L43/16
• WIPO field: Digital communication
• WIPO sector: Electrical engineering

Abstract

Methods, apparatus, and processor-readable storage media for identifying and remediating anomalies through cognitively assorted machine learning algorithms are provided herein. A computer-implemented method includes: identifying, using system log data, a target variable based at least in part on correlations between a set of performance indicators of a system and the target variable, and threshold values for the performance indicators relative to the target variable; generating an inference model to predict when the system will enter an adverse state and identify one or more root causes of the system entering the adverse state; using machine reinforcement learning to determine an action policy including actions that remediate the adverse state; predicting that the system will enter the adverse state by applying the inference model to further system log data; and automatically executing one or more actions of the action policy in response to the prediction.