Machine learning methods and systems for discovering problem incidents in a distributed computer system

A computer system for discovering problem incidents in a distributed computing system, the 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 controls the system to execute operations comprising:
• retrieving sets of historical problem incidents of the distributed computing system from the one or more data-storage devices;
• determining representative feature vectors for representative events of the historical problem incidents;
• determining a runtime feature vector for runtime events of a runtime problem incident in the distributed computing system;
• classifying the runtime problem as corresponding to one of the historical problem incidents with a representative feature vector that is closest to the runtime feature vector;
• and
• applying remedial measures to correct the runtime problem incident based on remedial measures used to correct the historical problem incident.

A non-transitory computer-readable medium encoded with machine-readable instructions that when executed by one or more processors of a computer system perform operations comprising:

• determining set of representative events of historical problem incidents for a distributed computing system;
• determining a representative feature vector for each set of representative events;
• determining a runtime feature vector for runtime events of a runtime problem incident;
• classifying the runtime problem incident as corresponding to a historical problem incident with a representative feature vector that is closest to the runtime feature vector;
• and
• applying remedial measures to correct the runtime problem incident based on remedial measures used to correct the historical problem incident.