System operational analytics using normalized likelihood scores

A system, comprising:

• a memory;
• and
• at least one processing device, coupled to the memory, operative to implement the following steps:
• obtaining data from one or more data sources associated with a monitored system;
• applying at least one function to the data to obtain a plurality of time-series counters for a plurality of distinct features within the data;
• processing the plurality of time-series counters using at least one machine learning model to obtain a plurality of log likelihood values representing a behavior of the monitored system over time;
• determining a z-score for each of the plurality of log likelihood values over a predefined short-term time window;
• monitoring a distribution of the z-scores for the plurality of log likelihood values over a predefined long-term time window to map the z-scores to percentile values;
• and
• mapping the percentile values to a health score for the monitored system based on one or more of predefined percentile ranges and at least one transformation function.

A computer program product, comprising a non-transitory machine-readable storage medium having encoded therein executable code of one or more software programs, wherein the one or more software programs when executed by at least one processing device perform the following steps:

• obtaining data from one or more data sources associated with a monitored system;
• applying at least one function to the data to obtain a plurality of time-series counters for a plurality of distinct features within the data;
• processing the plurality of time-series counters using at least one machine learning model to obtain a plurality of log likelihood values representing a behavior of the monitored system over time;
• determining a z-score for each of the plurality of log likelihood values over a predefined short-term time window;
• monitoring a distribution of the z-scores for the plurality of log likelihood values over a predefined long-term time window to map the z-scores to percentile values;
• and
• mapping the percentile values to a health score for the monitored system based on one or more of predefined percentile ranges and at least one transformation function.