Dell Products Inc.

Emc IP Holding Company LLC

John Kenneth Maxi

Hung The Dinh

Sabu K Syed

Tousif Mohammed

Vinod Kumar

Jatin Kamlesh Thakkar

Reddeppa Kollu

Abhijit Mishra

Panguluru Vijaya Sekhar

Satish Ranjan Das

Manikandan Pammal Rathinavelu

Krishna Mohan Akkinapalli

Vinay Sathyanarayana

Nagireddy Bonthu

Craig J Van Der Bogart

Chakradhar Kommana

Gnanesh Gowda

Karan Kapoor

Pravash Ranjan Panda

Madhusudhana Reddy Chilipi

Vellore Mohammed Imran

Venkat Allaka

Jyothi K R

Anu Bala Thakur

Abhishek Joshi

Puttaraju Bommanna Chikkanna

Prince Mathew

Naveen Silvester Muttikal Thomas

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.

Identifying and remediating system anomalies through machine learning algorithms

A method is disclosed including: obtaining one or more values of a system metric, the system metric being associated with a hardware resource of a computing device; detecting whether the system metric is approaching a threshold, the threshold being associated with a key performance indicator (KPI) of the computing device, the detecting being performed based on the obtained values of the system metric; calculating a predicted value of the system metric in response to detecting that the system metric is approaching the threshold, the predicted value of the system metric being calculated by using a linear predictor that is trained using unevenly-sampled training data; detecting whether the predicted value of the system metric exceeds the threshold; and reconfiguring the computing device to prevent the system metric from reaching the predicted value in response to detecting that the predicted value exceeds the threshold.

Time series forecasting classification

An apparatus in one embodiment comprises at least one processing platform comprising a plurality of processing devices. The at least one processing platform is configured to provide a plurality of applications with centralized access to a plurality of message oriented middleware (MOM) servers via a connectivity layer, to establish a connection between a given one of the plurality of applications and a given one of the plurality of MOM servers via the connectivity layer, and to exchange data between the given one of the plurality of applications and the given one of the plurality of MOM servers via the connectivity layer.

Messaging abstraction layer for integration with message oriented middleware platforms

Growing community of inventors

New Orleans, LA, United States of America

John Kenneth Maxi