Abstract: The lifespans of the solid stated drives (SSDs) of a storage array are modelled using linear regression with monitored wear level and power-on time. The models predict when individual SSDs will reach a wear level corresponding to readiness for replacement. A drive replacement process makes efficient use of available empty drive slots to replace SSDs in batches. SSDs that are ready for replacement are ranked in terms of priority for replacement. If the number of SSDs that are ready for replacement exceeds the number of available empty drive slots, then ranking us used to assign individual SSDs to different batches for replacement.

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: A multivariate time series model such as a Vector Auto Regression (VAR) model is built using fabric utilization, disk utilization, and CPU utilization time series data. The VAR model leverages interdependencies between multiple time-dependent variables to predict the start and length of an aperiodic backup time window, and to cause backup operations to occur during the aperiodic backup time window to thereby exploit the aperiodic backup time window for use in connection with backup operations. By automatically starting backup operations during predicted aperiodic backup time windows where the CPU, disk, and fabric utilization values are predicted to be low, it is possible to implement backup operations during time windows where the backup operations are less likely to interfere with primary application workloads, or system application workloads that need to be implemented to maintain optimal operation of the storage system.

Abstract: A data storage node includes a plurality of compute nodes that allocate portions of local memory to a shared cache. The shared cache is configured with mirrored and non-mirrored segments that are sized as a function of the percentage of write IOs and read 10s in a historical traffic workload profile specific to an organization or storage node. The mirrored and non-mirrored segments are separately configured with pools of data slots. Within each segment, each pool is associated with same-size data slots that differ in size relative to the data slots of other pools. The sizes of the pools in the mirrored segment are set based on write IO size distribution in the historical traffic workload profile. The sizes of the pools in the non-mirrored segment are set based on read IO size distribution in the historical traffic workload profile.

Abstract: A data storage node includes a plurality of compute nodes that allocate portions of local memory to a shared cache. The shared cache is configured with mirrored and non- mirrored segments that are sized as a function of the percentage of write IOs and read IOs in a historical traffic workload profile specific to an organization or storage node. The mirrored and non-mirrored segments are separately configured with pools of data slots. Within each segment, each pool is associated with same-size data slots that differ in size relative to the data slots of other pools. The sizes of the pools in the mirrored segment are set based on write IO size distribution in the historical traffic workload profile. The sizes of the pools in the non-mirrored segment are set based on read IO size distribution in the historical traffic workload profile.

Abstract: A multivariate time series model such as a Vector Auto Regression (VAR) model is built using fabric utilization, disk utilization, and CPU utilization time series data. The VAR model leverages interdependencies between multiple time-dependent variables to predict the start and length of an aperiodic backup time window, and to cause backup operations to occur during the aperiodic backup time window to thereby exploit the aperiodic backup time window for use in connection with backup operations. By automatically starting backup operations during predicted aperiodic backup time windows where the CPU, disk, and fabric utilization values are predicted to be low, it is possible to implement backup operations during time windows where the backup operations are less likely to interfere with primary application workloads, or system application workloads that need to be implemented to maintain optimal operation of the storage system.

Abstract: The lifespans of the solid stated drives (SSDs) of a storage array are modeled using linear regression with monitored wear level and power-on time. The models predict when individual SSDs will reach a wear level corresponding to readiness for replacement. A drive replacement process makes efficient use of available empty drive slots to replace SSDs in batches. SSDs that are ready for replacement are ranked in terms of priority for replacement. If the number of SSDs that are ready for replacement exceeds the number of available empty drive slots, then ranking us used to assign individual SSDs to different batches for replacement.

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: Embodiments of the present disclosure relate to throttling processing threads of a storage device. One or more input/output (I/O) workloads of a storage device can be monitored. One or more resources consumed by each thread of each storage device component to process each operation included in a workload can be analyzed. Based on the analysis, consumption of each resource consumed by each thread can be controlled.

Abstract: Aspects of the present disclosure relate to asynchronous memory management. In embodiments, an input/output (IO) workload is received at a storage array. Further, one or more read-miss events corresponding to the IO workload are identified. Additionally, at least one of the storage array's cache slots is bound to a track identifier (TID) corresponding to the read-miss events based on one or more of the read-miss events' two-dimensional metrics.

Abstract: Testcase recommendations are generated for a testcase creator application by training a learning function using metadata of previously generated testcases by parsing the metadata into steptasks, and providing the parsed metadata to the learning function to enable the learning function to determine relationships between the steptasks of the previously generated testcases, and using, by the testcase creator application, the trained learning function to obtain a predicted subsequent steptask for a given type of testcase to be generated. Each steptask describes one of the steps of the testcase using a concatenation of a step number of the one of the steps of the testcase, a module and a submodule to be used to perform of the one of the steps of the testcase, and a function to be performed at the one of the steps of the testcase.

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 processing test execution logs to determine error location and source includes creating a set of training examples based on previously processed test execution logs, clustering the training examples into a set of clusters using an unsupervised learning process, and using training examples of each cluster to train a respective supervised learning process to label data where each generated cluster is used as a class/label to identify the type of errors in the test execution log. The labeled data is then processed by supervised learning processes, specifically a classification algorithm. Once the classification model is built it is used to predict the type of the errors in future/unseen test execution logs. In some embodiments, the unsupervised learning process is a density-based spatial clustering of applications with noise clustering application, and the supervised learning processes are random forest deep neural networks.

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: Embodiments of the present disclosure measure a state of a storage group within a storage array. The embodiments also increase or decrease a compression ratio corresponding to input/output (I/O) operations on the storage group based on a target data reduction ratio (DRR) of the storage array, an expected performance envelope, and a compressibility factor of the storage group.

Abstract: An aspect of the present disclosure relates to one or more data decryption techniques. In embodiments, an input/output operation (IO) stream including one or more encrypted IOs is received by a storage array. Each encrypted IO is assigned an encryption classification. Further, each encrypted IO is processed based on its assigned encryption classification.

Abstract: Aspects of the present disclosure relate to data deduplication (dedupe). In embodiments, an input/output operation (IO) stream is received by a storage array. In addition, a received IO sequence in the IO stream that matches a previously received IO sequence is identified. Further, a data deduplication (dedupe) technique is performed based on a selected data dedupe policy. The data dedupe policy can be selected based on a comparison of service quality (QoS) related to the received IO sequence and a QoS related to the previously received IO sequence.

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: Estimating maintenance for a storage system includes accessing a model that outputs time and materials estimates based on input configuration data, providing configuration data of the storage system to the model, and obtaining an estimate of maintenance time and materials based on the configuration data provided to the model. The model may be provided by a neural network, which may be a self-organized map. Weights of neurons of the self-organized map may be initialized randomly. The model may be initially configured using training data that may include an I/O load of the storage system, memory size of the storage system, a drive count of the storage system, and/or size and parameter information corresponding to hardware being added for the maintenance operation. The training data may include actual time and materials for prior storage system maintenance operations used for the training data. The model may be provided on the storage system.

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.