Vmware, Inc.

Sam Israelyan

Ashot Nshan Harutyunyan

Arnak Poghosyan

Tigran Bunarjyan

Nelli Aghajanyan

Lilit Harutyunyan

Marine Harutyunyan

Automated computer-implemented methods and systems for resolving performance problems with objects executing in a data center are described. The automated methods use machine learning to obtain rules defining relationships between probabilities of event types of in log messages and performance problems identified by a key performance indictor ('KPI') of the object. When a KPI violates a corresponding threshold, the rules are used to evaluate run time log messages that describe the probable root cause of the performance problem. An alert identifying the KPI threshold violation, and the log messages are displayed in a graphical user interface of an electronic display device.

Methods and systems for using machine learning to resolve performance problems with objects of a data center

Automated, computer-implemented methods and systems for resolving performance problems with objects executing in a data center are described. The automated methods use machine learning to train a model that comprises rules defining relationships between probabilities of event types of in log messages and values of a key performance indictor ('KPI') of the object over a historical time period. When a KPI violates a corresponding threshold, the rules are used to evaluate run time log messages that describe the probable root cause of the performance problem. An alert identifying the KPI threshold violation, and the log messages are displayed in a graphical user interface of an electronic display device.

Methods and systems for resolving performance problems with objects of a data center

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Yerevan, Armenia

Sam Israelyan