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

A computer system for resolving probable root causes of performance problems with an object executing in a data center, the computer system comprising:

• one or more processors;
• one or more data-storage devices;
• and
• machine-readable instructions stored in the one or more data-storage devices that when executed using the one or more processors control the system to perform operations comprising:
• using machine learning to train a model that comprises rules defining relationships between probabilities of event types of log messages and values of a key performance indicator (′KPI″) of the object over a historical time period;
• in response to detecting a run-time KPI violation of a KPI threshold, determining probabilities of event types of log messages recorded in a run-time interval;
• using the rules to evaluate the probabilities of event types of the log messages recorded in the run-time interval for important event types;
• identifying log messages with the important event types generated in the run-time interval;
• displaying an alert identifying the KPI threshold violation and the log messages in a graphical user interface of an electronic display device, the log messages describing the probable root cause of the performance problem;
• and
• executing remedial measures to correct the performance problem, the remedial measures including spinning up one or more clones of the object on one or more server computers and migrating the object to a server computer to optimize use of data center resources.

A non-transitory computer-readable medium having instructions encoded thereon for enabling one or more processors of a computer system to perform operations comprising:

• using machine learning to train a model that comprises rules defining relationships between probabilities of event types of log messages and values of a key performance indicator (“KPI”) of an object of a data center over a historical time period:
• in response to detecting a run-time KPI violation of a KPI threshold, determining probabilities of event types of log messages recorded in a run-time interval;
• using the rules to evaluate the probabilities of event types of the log messages recorded in the run-time interval for important event types;
• identifying log messages with the important event types generated in the run-time interval;
• displaying an alert identifying the KPI threshold violation and the log messages in a graphical user interface of an electronic display device, the log messages describing the probable root cause of the performance problem;
• and
• executing remedial measures to correct the performance problem, the remedial measures including spinning up one or more clones of the object on one or more server computers and migrating the object to a server computer to optimize use of data center resources.