Abstract: An amount of storage space required to maintain counter information for a storage system is reduced without reducing a temporal granularity or tracking granularity of the counter information. Rather than periodically recording actual (i.e., raw) counter values for counters, difference (i.e., delta) values may be recorded. For a given counter, a difference (delta value) between a value of the counter for a given point in time (PIT) and a value of the counter for a previous PIT may be determined, and this delta value may be stored as opposed to storing the raw counter value. This delta value may be a significantly smaller value than the raw value. To further reduce the amount of storage space required, no value may be stored for a counter for a given PIT if it is determined that there is no difference between a counter value for the given PIT and a previous PIT.

Abstract: Techniques for predicting states may include: receiving data sets of counter values, wherein each counter values denotes a number of times a particular code flow point associated with the counter value is executed at runtime during a specified time period; receiving images generated from the data sets; labeling each of the images with state information, wherein first state information associated with a first image indicates that the first image is associated with a first error state of a system or an application; training a neural network using the images to recognize the first state; receiving a next image generated from another data set; and predicting, by the neural network and in accordance with the next image, whether the system or the application is expected to transition into the first state. In at least one embodiment, the foregoing processing may optionally use matrices generated from the data sets rather than images.

Abstract: Aspects of the present disclosure relate to detecting and correcting a storage array's drive sectors to prevent data loss. In embodiments, a storage array's telemetry information is monitored. Further, one or more state or operational metrics of the storage array's storage drives are measured. Additionally, each storage drive is scrubbed based on each drive's relative scrubbing priority defined by the telemetry information and each storage drive's state or operation metrics.

Abstract: An amount of storage space required to maintain counter information for a storage system is reduced without reducing a temporal granularity or tracking granularity of the counter information. Rather than periodically recording actual (i.e., raw) counter values for counters, difference (i.e., delta) values may be recorded. For a given counter, a difference (delta value) between a value of the counter for a given point in time (PIT) and a value of the counter for a previous PIT may be determined, and this delta value may be stored as opposed to storing the raw counter value. This delta value may be a significantly smaller value than the raw value. To further reduce the amount of storage space required, no value may be stored for a counter for a given PIT if it is determined that there is no difference between a counter value for the given PIT and a previous PIT.

Abstract: An aspect of the present disclosure relates to one or more techniques to identify and resolve storage array errors. In embodiments, an error notification related to a computing device can be received. One or more threads related to the error notification can further be identified. Additionally, an error resolution technique can be performed based on each identified thread.

Abstract: Techniques for predicting states may include: receiving data sets of counter values, wherein each counter values denotes a number of times a particular code flow point associated with the counter value is executed at runtime during a specified time period; receiving images generated from the data sets; labeling each of the images with state information, wherein first state information associated with a first image indicates that the first image is associated with a first error state of a system or an application; training a neural network using the images to recognize the first state; receiving a next image generated from another data set; and predicting, by the neural network and in accordance with the next image, whether the system or the application is expected to transition into the first state. In at least one embodiment, the foregoing processing may optionally use matrices generated from the data sets rather than images.