Abstract: Ransomware activity detection and data protection is implemented by a remote R2 storage array on an asynchronous remote data replication facility, on which data from a primary R1 storage array is replicated to the remote storage array. Write operations on storage volumes in a remote data replication group are collected in a capture cycle on the primary storage array, along with IO pattern metadata describing both read and write operations on the storage volumes. At the end of the capture cycle, the update and metadata is transmitted to the remote storage array. The remote storage array receives the update and metadata and temporarily stores the update prior to applying it to its copy of the storage volumes. Ransomware anomaly detection is implemented using the update and metadata, and if ransomware activity is detected, the data on the remote R2 storage array is protected, and the update is not applied.

Abstract: Architectures and techniques are described that can provide security or threat detection for a data storage system. Threat detection can be identified and potentially blocked prior to requested customer workloads reaching a backend storage device and can thus effectively be achieved in real-time. Techniques utilized herein can leverage an offload capability that operates to offload certain processing from a central processing unit (CPU) to a data processing unit (DPU). A long short-term memory (LSTM) model can be executed in the DPU to detect potential threats in real-time without consuming CPU resources.

Abstract: A system can maintain a memory pool that comprises a first tier of memory and a second tier of memory. The system can perform a group of operations at least one time, comprising: determining an autoregressive integrated moving average of first time series metrics of past accesses of the memory pool to produce second time series metrics of forecast accesses, wherein the second time series metrics are provided as input to a reinforcement learning model, wherein an output of the reinforcement learning model comprises feedback to adjust a size of mirrored and non-mirrored portions of the first tier, wherein the output comprises feedback to adjust a weight of forecasting results associated with the second time series metrics, and determining to halt performing the operations where the size satisfies a defined criterion. The system can adjust a size of the second tier of memory based on the first tier.

Abstract: A storage system configuration alternation system implements storage system reconfiguration alternation by determining a first set of storage system optimization policies to be applied during periods where the workload on the storage system is primarily transactional, and determining a second set of storage system optimization policies to be applied during periods where the workload on the storage system is primarily bandwidth intensive. The storage system configuration alternation system monitors the storage system workload to identify whether the storage system workload is primarily transactional or primarily bandwidth intensive. Based on the current monitored determination, the configuration alternation system selectively applies either the first or second set of storage system policies. A primarily bandwidth intensive workload may be determined based on a combination of a high percentage read IO operations of maximum read size, low CPU utilization, and high back-end bandwidth utilization.

Abstract: Selective packing of small block write operations is implemented prior to compression, to improve compression efficiency and hence reduce bandwidth requirements of a Remote Data Replication (RDR) facility. Compression characteristics of write IO operations are forecast, and write IO operations with similar forecast compression characteristics are pooled. Write IO operations are also grouped according to extent, device, and storage group. Write operations from a given compression pool are then preferentially selected from the extent-level grouping, next from the device-level grouping, and then from the SG-level grouping, to create an IO package. The IO package is then compressed and transmitted on the RDR facility. By creating an IO package prior to compression, it is possible to achieve greater compression than would be possible if each individual write IO operation were to be individually compressed to thereby reduce network bandwidth of the RDR facility.

Abstract: An information handling system executing a software test suite green data center policy generation system comprising a network interface device receiving data center hardware utilization analytics and a software test suite identifier for a selected software test suit and a processor to identify a reserved capacity of the data center processing system reserved for general execution of software test suites according to general quality of service requirements for input/output commands and to predict a future data center processor utilization rate and execution duration for the selected software test suite by forecasting actual use of data center resources during a future execution of the selected software test suite that is below the reserved capacity, and the network interface device to transmit an instruction to throttle power supplied to an over-allocated portion of the data center resources from the reserved capacity duration execution of the selected software test suite.

Abstract: A method and apparatus for enabling dynamic selection between compression algorithms to achieve a target data reduction ratio is provided to predict, on a per-extent basis, whether a write operation targeting a piece of data on a particular extent would be preferentially compressed using either compression algorithm A or compression algorithm B. By selectively implementing compression of particular write operations using compression algorithm B, when it is predicted to achieve a greater compression benefits, it is possible to reduce the overall data reduction ratio achieved by the compression engine while economizing the amount of resources expended by the compression engine. The dynamic selection process determines a per-extent compressibility ratio, which is used in connection with a first threshold to evaluate write operations on a per-extent basis. The dynamic selection processes also determines a per-extent size threshold to select between compression algorithms for particular write operations.

Abstract: An apparatus in an illustrative embodiment comprises at least one processing device, with the processing device being configured to receive in a storage system from a host device an indication of a data size utilized in a multi-path layer of the host device to select paths for delivery of input-output operations to different storage controllers of the storage system, to determine in the storage system a prefetch data size based at least in part on the data size indication received from the host device, and responsive to detection in the storage system of sequential data reads in input-output operations received from the host device, to prefetch from one or more backend storage devices of the storage system, into a memory associated with a particular one of the storage controllers of the storage system, an amount of data that is determined based at least in part on the prefetch data size.

Abstract: One or more aspects of the present disclosure relate to maximizing data migration bandwidth. In embodiments, one or more network characteristics corresponding to network communications between a first storage array and a second storage array is determined. Further, one or more network metrics of at least one acknowledgment communication from the second storage array to the first storage array can be analyzed. Additionally, one or more input/output (IO) messages are transmitted from the first storage array to the local storage array during an acknowledgment period corresponding to receipt of the at least one acknowledgment communication from the second storage array by the first storage array based on the one or more network metrics.

Abstract: An information handling system executing a predictive hash table and carbon dioxide (CO2) minimizing system may comprise a network interface device to receive from a remote data center deduplication process workload metrics, fingerprint hash table size metrics, and a current reserved memory size allocated for storage of a fingerprint hash table, a processor executing code instructions to predict a future workload of deduplication processes and a future fingerprint hash table size to determine a predicted amount of memory to reserve for storage of a future fingerprint hash table based on the predicted future workload of deduplication processes and fingerprint hash table size, and to determine an over-allocated portion of the current reserved memory size where the network interface device transmits an instruction to the data center to limit power provided to the over-allocated portion of the current reserved memory size.

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: A remote data replication facility includes a primary storage array and a backup storage array, on which tracks of data are replicated from the primary storage array to the backup storage array as they are received by the primary storage array. Remote data verification is implemented on the remote data replication facility by comparing track fingerprints, track temporal write metadata, and track spatial write metadata, for a given track on the primary storage array, with corresponding track fingerprints, track temporal write metadata, and track spatial write metadata, for the given track on the backup storage array. If any difference is determined in the combination of track fingerprints, track temporal write metadata, track spatial write metadata, for a given track, the integrity of the data at the backup storage array is not verified for the track.

Abstract: Techniques for program code management are disclosed. For example, a method obtains resource utilization data from a computing network comprising a plurality of computing devices. The method then utilizes a multi-variate time series model representing at least a portion of the resource utilization data to automatically compute at least one time window in which to perform a program code update on at least a subset of the plurality of computing devices.

Abstract: One or more aspects of the present disclosure relate to increasing the performance of a storage array using activity and compressibility-based data compression. In embodiments, an input/output (IO) workload is received at a storage array. Additionally, at least one address space of at least one logical storage device with an IO activity corresponding to an activity characteristic and compressibility corresponding to a compressibility characteristic is identified. Further, if a data reduction requirement (DRR) of the storage array is unsatisfied, IO write requests of the IO workload targeting the identified address spaces of each logical device are compressed.

Abstract: An intelligent predictive battery replacement system to increase disaster recovery stability uses training samples created using battery report indexes and vault condition recovery reports to train a linear regression model learning process to learn a recursion between battery post recovery charge and a set of time series battery operational parameters. Once trained, the learning process is used in a predictive manner to predict the post recovery charge state of batteries deployed in storage controllers, to provide a predictive per-battery risk assessment. The per-battery risk assessment identify batteries that may be scheduled to be replaced to increase disaster recovery stability of the storage systems.

Abstract: SSD service life is extended by monitoring wear-level and prompting relocation of unstable data out of SSDs that have reached a soft wear-level threshold such that those SSDs do not contain unstable data when those SSDs reach a hard wear-level threshold. The progression of SSD wear-level is forecasted using an ARIMA algorithm. Unstable data on SSDs between predicted times of reaching the soft and hard thresholds is replaced by stable data from SSDs that have not reached the soft wear-level threshold. The stable data may be snapshot data and deduplicated data and deduplication hashes characterized based on number of references. SSDs that reach the hard threshold without unstable data can remain in service for read IOs until being replaced.

Abstract: In a storage system in which processor cores are exclusively allocated to run process threads of individual emulations, the allocations of cores to emulations are dynamically reconfigured based on forecasted workload. A workload configuration model is created by testing different core allocation permutations with different workloads. The best performing permutations are stored in the model as workload configurations. The workload configurations are characterized by counts of tasks required to service the workloads. Actual task counts are monitored during normal operation and used to forecast changes in actual task counts. The forecasted task counts are compared with the task counts of the workload configurations of the model to select the best match. Allocation of cores is reconfigured to the best match workload configuration.

Abstract: An information handling system executing a predictive hash table and carbon dioxide (CO2) minimizing system may comprise a network interface device to receive from a remote data center deduplication process workload metrics, fingerprint hash table size metrics, and a current reserved memory size allocated for storage of a fingerprint hash table, a processor executing code instructions to predict a future workload of deduplication processes and a future fingerprint hash table size to determine a predicted amount of memory to reserve for storage of a future fingerprint hash table based on the predicted future workload of deduplication processes and fingerprint hash table size, and to determine an over-allocated portion of the current reserved memory size where the network interface device transmits an instruction to the data center to limit power provided to the over-allocated portion of the current reserved memory size.

Abstract: One or more aspects of the present disclosure relate to increasing the performance of a storage array using activity and compressibility-based data compression. In embodiments, an input/output (IO) workload is received at a storage array. Additionally, at least one address space of at least one logical storage device with an IO activity corresponding to an activity characteristic and compressibility corresponding to a compressibility characteristic is identified. Further, if a data reduction requirement (DRR) of the storage array is unsatisfied, IO write requests of the IO workload targeting the identified address spaces of each logical device are compressed.

Abstract: An information handling system executing a software test suite green data center policy generation system comprising a network interface device receiving data center hardware utilization analytics and a software test suite identifier for a selected software test suit and a processor to identify a reserved capacity of the data center processing system reserved for general execution of software test suites according to general quality of service requirements for input/output commands and to predict a future data center processor utilization rate and execution duration for the selected software test suite by forecasting actual use of data center resources during a future execution of the selected software test suite that is below the reserved capacity, and the network interface device to transmit an instruction to throttle power supplied to an over-allocated portion of the data center resources from the reserved capacity duration execution of the selected software test suite.