Abstract: A software analysis system collects a first set of time series counter values from an original version of software instrumented with software telemetry counters, and collects a second set of time series counter values from a modified version of the software that also is instrumented with the software telemetry counters. The first time series become dimensions input to train a weight matrix of a first self-organizing map, to cause the first self-organizing map to cluster the time series into a group of first clusters describing execution of the original software. The second time series become dimensions input to train a weight matrix of a second self-organizing map, to cause the second self-organizing map to cluster the time series into a group of second clusters describing execution of the modified software. Deviation analysis between the first and second groups of clusters is used to identify execution differences between the software versions.

Abstract: A method of predicting usage levels of solid-state drives (SSDS) includes receiving time series usage data from each SSD over a plurality of monitoring intervals (lags), and using a first portion of the time series usage data to train (fit) an ARIMA model to the time series data. A unique ARIMA model (order) is determined for each SSD from the unique time series % usage data of each SSD. The ARMIA model is then fit to the time series % usage data and used in a predictive manner to predict a future date when the % usage will exceed a threshold % usage value. By predicting when the SSDs will meet particular thresholds, it is possible to plan for and procure replacement SSDs to enable currently installed SSDs to be removed from service before the currently installed SSD % usage levels exceed threshold values, thus enabling the currently installed SSDs to be repurposed.

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: A method of predicting usage levels of solid-state drives (SSDS) includes receiving time series usage data from each SSD over a plurality of monitoring intervals (lags), and using a first portion of the time series usage data to train (fit) an ARIMA model to the time series data. A unique ARIMA model (order) is determined for each SSD from the unique time series % usage data of each SSD. The ARMIA model is then fit to the time series % usage data and used in a predictive manner to predict a future date when the % usage will exceed a threshold % usage value. By predicting when the SSDs will meet particular thresholds, it is possible to plan for and procure replacement SSDs to enable currently installed SSDs to be removed from service before the currently installed SSD % usage levels exceed threshold values, thus enabling the currently installed SSDs to be repurposed.

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: 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: A method of determining feature usage on a set of storage systems deployed across multiple customer sites includes defining metrics related to the features of interest, and pushing the defined metrics to an AIM (Autonomous Infrastructure Module) of an operating system of each storage system. The AIM on each storage system collects data associated with the metrics from the operating system on the storage system. The collected data is aggregated and formatted by the AIM and then used to create an autonomous field telemetry report. Autonomous field telemetry reports are periodically forwarded on a communication network to an analytics engine. The analytics engine parses each autonomous field telemetry report to extract usage information related to the features of interest, loads the parsed data to PostgreSQL staging and historical databases, and uses the parsed data alone or in combination with the historical data to create analytics and visualizations of the analytics.

Abstract: A method of determining feature usage on a set of storage systems deployed across multiple customer sites includes defining metrics related to the features of interest, and pushing the defined metrics to an AIM (Autonomous Infrastructure Module) of an operating system of each storage system. The AIM on each storage system collects data associated with the metrics from the operating system on the storage system. The collected data is aggregated and formatted by the AIM and then used to create an autonomous field telemetry report. Autonomous field telemetry reports are periodically forwarded on a communication network to an analytics engine. The analytics engine parses each autonomous field telemetry report to extract usage information related to the features of interest, loads the parsed data to PostgreSQL staging and historical databases, and uses the parsed data alone or in combination with the historical data to create analytics and visualizations of the analytics.