Performance anomaly detection in storage systems using autoencoder with hierarchical multi-frequency decoding

A non-transitory machine-readable medium, comprising executable instructions that, when executed by at least one processor, facilitate performance of operations, comprising:

• based on defined segments segmented from storage system performance data comprising a group of different time frequencies and using a performance data encoder-decoder model, generating reconstructed time series data, wherein generating the reconstructed time series data comprises generating the reconstructed time series data using a defined hierarchical decoding process that reconstructs the storage system performance data at multiple frequency resolutions, wherein the reconstructed time series data comprises a combined output of the multiple frequency resolutions, wherein the performance data encoder-decoder model was trained based on normalized training data and using mean squared error and dynamic time warping, and wherein the normalized training data was generated from the defined segments that were normalized;
• based on the reconstructed time series data and the defined segments, determining reconstruction errors applicable to the reconstructed time series data;
• determining a normal distribution that fits the reconstruction errors;
• based on the normal distribution, determining a defined threshold for anomaly scores applicable to the reconstruction errors;
• and
• in response to a determination that a reconstruction value of the reconstruction errors exceeds the defined threshold, generating an output indicative of the reconstruction value being determined to be an anomaly.

A method, comprising:

• based on defined segments segmented from storage system performance data comprising a group of different time frequencies and using a performance data encoder-decoder model, generating, by a system comprising at least one processor, reconstructed time series data, wherein generating the reconstructed time series data comprises generating the reconstructed time series data using a defined hierarchical decoding process that reconstructs the storage system performance data at multiple frequency resolutions, wherein the reconstructed time series data comprises a combined output of the multiple frequency resolutions, wherein the performance data encoder-decoder model was trained based on normalized training data and using mean squared error and dynamic time warping, and wherein the normalized training data was generated from the defined segments that were normalized;
• based on the reconstructed time series data and the defined segments, determining, by the system, reconstruction errors applicable to the reconstructed time series data;
• fitting, by the system, a normal distribution to the reconstruction errors;
• based on the normal distribution, determining, by the system, a defined threshold for anomaly scores applicable to the reconstruction errors;
• and
• in response to a determination that a reconstruction value of the reconstruction errors exceeds the defined threshold, generating, by the system, an output indicative of the reconstruction value being determined to be an anomaly.