Automated problem diagnosis on logs using anomalous telemetry analysis

A computer, comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the computer to perform a method for automated problem diagnosis, the method comprising:
• analyzing telemetry data of a system to identify a set of time ranges during which the telemetry data exhibits anomalous behavior;
• extracting, from a set of log entries generated by the system, a subset of log entries having a timestamp that is in one of the time ranges in set of time ranges;
• analyzing, by using natural language processing, the subset of log entries to identify a subset of the subset of log entries that has a high likelihood to be associated with one or problems in the system;
• extracting human-readable text from the subset of the subset of log entries;
• and
• searching a knowledge database by using the human-readable text to identify one or more solutions to resolve the one or more problems in the system.

A method for automated problem diagnosis, the method comprising:

• analyzing, by using a computer, telemetry data of a system to identify a set of time ranges during which the telemetry data exhibits anomalous behavior, wherein the telemetry data of the system comprises processor utilization, memory usage, disk load, and network traffic metrics;
• extracting, from a set of log entries generated by the system, a subset of log entries having a timestamp that is in one of the time ranges in set of time ranges, wherein the set of log entries comprises notifications, warnings, errors, and alarms generated by processes executing on the system;
• analyzing, by using natural language processing, the subset of log entries to identify a subset of the subset of log entries that has a high likelihood to be associated with one or problems in the system by:
• calculating a sentiment score for each log entry in the set of log entries;
• and
• identifying the subset of the subset of log entries by selecting log entries whose sentiment scores are lower than a threshold sentiment score;
• extracting human-readable text from the subset of the subset of log entries;
• and
• searching a knowledge database by using the human-readable text to identify one or more solutions to resolve the one or more problems in the system.