Storage anomaly detection

A computer-implemented method comprising:

• determining, using one or more computing devices, a hyper feature representation aggregating scores for statistics associated with a plurality of non-transitory storage devices, wherein the hyper feature representation is a reduced hyper feature representation based on an initial hyper feature representation;
• generating, using the one or more computing devices and the hyper feature representation, storage device scores for the non-transitory storage devices of the plurality of non-transitory storage devices, respectively;
• and
• identifying, using the one or more computing devices, one or more non-transitory storage devices from among the plurality of non-transitory storage devices exhibiting anomalous storage device behavior using the storage device scores.

A system comprising:

• one or more processors;
• one or more memories;
• and
• an analyzer configured to perform operations including:
• determining a hyper feature representation aggregating scores for statistics associated with a plurality of non-transitory storage devices, wherein the hyper feature representation is a reduced hyper feature representation based on an initial hyper feature representation;
• generating, using the hyper feature representation, storage device scores for the non-transitory storage devices of the plurality of non-transitory storage devices, respectively;
• and
• identifying one or more non-transitory storage devices from among the plurality of non-transitory storage devices exhibiting anomalous storage device behavior using the storage device scores.

A system comprising:

• one or more processors;
• and
• one or more memories storing instructions that, when executed by the one or more processors, cause the system to perform operations comprising:
• collecting signal data for a plurality of non-transitory storage devices, the signal data including time series for statistics associated with each of the non-transitory storage devices;
• determining a hyper feature representation from the collected signal data;
• computing, using the hyper feature representation, scores for each of the statistics associated with each of the non-transitory storage devices;
• determining a reduced hyper feature representation aggregating the scores for each of the statistics associated with each of the non-transitory storage devices;
• generating, using the reduced hyper feature representation, storage device scores for the non-transitory storage devices of the plurality, respectively;
• and
• identifying one or more non-transitory storage devices from among the plurality of non-transitory storage devices exhibiting anomalous storage device behavior using the storage device scores.

A system comprising:

• one or more processors;
• one or more memories;
• means for collecting signal data for a plurality of non-transitory storage devices, the signal data including time series for statistics associated with each of the non-transitory storage devices;
• means for determining a hyper feature representation from the collected signal data;
• means for computing, using the hyper feature representation, scores for each of the statistics associated with each of the non-transitory storage devices;
• means for determining a reduced hyper feature representation aggregating the scores for each of the statistics associated with each of the non-transitory storage devices;
• means for generating, using the reduced hyper feature representation, storage device scores for the non-transitory storage devices of the plurality, respectively;
• and
• means for identifying one or more non-transitory storage devices from among the plurality of non-transitory storage devices exhibiting anomalous storage device behavior using the storage device scores.