Machine learning-based detection and remediation of latency-related performance issues in storage systems

A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

• to obtain monitoring data for a storage system, the monitoring data comprising telemetry data for a set of two or more metrics, the set of two or more metrics including a latency metric and one or more additional metrics;
• to determine, for each metric in the set of two or more metrics, one or more metric-specific anomalous data points utilizing a machine learning-based univariate anomaly detector trained utilizing historical telemetry data for that metric;
• to determine, for the set of two or more metrics collectively, one or more metric-generic anomalous data points utilizing a machine learning-based multivariate anomaly detector trained to identify structural anomalies across the set of two or more metrics;
• to identify a given one of the one or more metric-specific anomalous data points for the latency metric which corresponds to one of the one or more metric-generic anomalous data points;
• to determine whether the given metric-specific anomalous data point for the latency metric represents a performance issue for the storage system based at least in part on whether there are any co-occurring metric-specific anomalous data points for at least one of the one or more additional metrics;
• and
• responsive to determining that the given metric-specific anomalous data point for the latency metric represents a performance issue for the storage system, to initiate remediation of the performance issue for the storage system.

A method comprising:

• obtaining monitoring data for a storage system, the monitoring data comprising telemetry data for a set of two or more metrics, the set of two or more metrics including a latency metric and one or more additional metrics;
• determining, for each metric in the set of two or more metrics, one or more metric-specific anomalous data points utilizing a machine learning-based univariate anomaly detector trained utilizing historical telemetry data for that metric;
• determining, for the set of two or more metrics collectively, one or more metric-generic anomalous data points utilizing a machine learning-based multivariate anomaly detector trained to identify structural anomalies across the set of two or more metrics;
• identifying a given one of the one or more metric-specific anomalous data points for the latency metric which corresponds to one of the one or more metric-generic anomalous data points;
• determining whether the given metric-specific anomalous data point for the latency metric represents a performance issue for the storage system based at least in part on whether there are any co-occurring metric-specific anomalous data points for at least one of the one or more additional metrics;
• and
• responsive to determining that the given metric-specific anomalous data point for the latency metric represents a performance issue for the storage system, initiating remediation of the performance issue for the storage system;
• wherein the method is performed by at least one processing device comprising a processor coupled to a memory.