Abstract: One or more aspects of the present disclosure relate to detecting viruses during input/output (I/O) operations with a storage device. One or more one or more input/output (I/O) operations can be received via at least one I/O path. At least one virus can be identified in-line with each I/O path that corresponds to the one or more I/O operations using one or more deduplication fingerprints. One or more virus mitigation actions can be performed on the at least one virus.

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: The described technology is generally directed towards caching and aggregated write operations based on predicted patterns of data transfer operations. According to an embodiment, a system can comprise a memory that can store computer executable components, and a processor that can execute the computer executable components stored in the memory. The components can comprise a pattern identifying component to identify a first pattern of data transfer operations performed on a data store, resulting in an identified first pattern, based on monitored data transfer operations. The components can further comprise a pattern predicting component to predict a second pattern of future data transfer operations performed on the data store, resulting in a predicted second pattern, based on the identified first pattern. The components can further comprise a host adapter to generate a data transfer operation to be performed on the data store based on the predicting the second pattern.

Abstract: The described technology is generally directed towards caching and aggregated write operations based on predicted patterns of data transfer operations. According to an embodiment, a system can comprise a memory that can store computer executable components, and a processor that can execute the computer executable components stored in the memory. The components can comprise a pattern identifying component to identify a first pattern of data transfer operations performed on a data store, resulting in an identified first pattern, based on monitored data transfer operations. The components can further comprise a pattern predicting component to predict a second pattern of future data transfer operations performed on the data store, resulting in a predicted second pattern, based on the identified first pattern. The components can further comprise a host adapter to generate a data transfer operation to be performed on the data store based on the predicting the second pattern.

Abstract: A method is used in managing remote replication in storage systems. The method monitors network traffic characteristics of a network. The network enables communication between a first storage system and a second storage system. The method predicts a change in at least one of an application demand of an application of a set of applications executing on the first storage server and a network state of the network, where the set of applications have been identified for performing a replication to the second storage system. Based on the prediction, the method dynamically manages replication of the set of applications in accordance with a performance target associated with each application.

Abstract: Described herein is a system, and related techniques, for predicting I/O requests that are not necessarily directed to sequential sectors of a physical storage device. In some embodiments, I/O patterns that do not involve sequential-sector access, and that may be relatively long-term patterns, may be recognized. To recognize such patterns, deep machine-learning techniques may be used, for example, using neural networks. Such neural networks may be a recurrent neural network such as, for example, an LSTM-RNN. I/O streams for a workstream may be sampled for specific I/O features to produce a time series of I/O feature values of a workstream, and this time series of data may be fed to a prediction engine, e.g., an LSTM-RNN to predict one or more future I/O features values, and I/O actions may be taken based on these predicted feature values.

Abstract: A method is used in managing remote replication in storage systems. The method monitors network traffic characteristics of a network. The network enables communication between a first storage system and a second storage system. The method predicts a change in at least one of an application demand of an application of a set of applications executing on the first storage server and a network state of the network, where the set of applications have been identified for performing a replication to the second storage system. Based on the prediction, the method dynamically manages replication of the set of applications in accordance with a performance target associated with each application.

Abstract: Determining storage of particular data in cache memory of a storage device includes using a first mechanism to determine when to remove the particular data from the cache memory and using a second mechanism, independent from the first mechanism, to inhibit the particular data from being stored in the cache memory independent of whether the first mechanism otherwise causes the particular data to be stored in the cache memory. The first mechanism may remove data from the cache memory that was least recently accessed. The second mechanism may be based, at least in part, on a prediction value of an expected benefit of storing the particular data in the cache memory. The prediction value may be determined based on input data corresponding to measured cache read hits (RH), cache write hits (WH), cache read misses (RM), cache write destage operations (WD), and prefetch reads (PR) for the particular data.

Abstract: A method of allocating storage volumes between compressed and uncompressed storage tiers includes maintaining a respective state machine for each storage volume, each state machine maintaining a current state of the storage volume, a previous state of the storage volume, and a state machine timer based on when the respective storage volume last changed state. The method further includes allocating a first subset of the storage volumes to an uncompressed storage tier and allocating a second subset of storage volumes to a compressed storage tier, and determining storage volumes to be moved between the uncompressed and compressed storage tiers using a state machine evaluation process based on the state information of the state machines.

Abstract: Techniques are described for performing data storage optimization. A first I/O workload for a first data portion of a first snapshot of a first logical device is tracked. First processing is performed by a data storage optimizer to determine a set of one or more data movement optimizations. The first processing uses the first I/O workload for the first snapshot. The set of one or more data movement optimizations include a first data movement that is any of a promotion to move data included in the first data portion from a first storage tier to a higher performance storage tier and a demotion to move data included in the first data portion from the first storage tier to a lower performance storage tier. The first data movement is performed.

Abstract: Techniques for obtaining consistent read performance are disclosed that may include: receiving measured read I/O (input/output) response times for flash storage devices; and determining, in accordance with a specified allowable variation, whether a first of the measured read I/O response times for a first of the flash storage devices is inconsistent with respect to other ones of the measured read I/O response times. Responsive to determining the first measured read I/O response time is inconsistent first processing may be performed that corrects or alleviates the inconsistency of the first measured read I/O response time. The first processing may include varying the first measured read I/O response time of the first flash storage device by enforcing, for the first flash storage device, a write I/O workload limit a read I/O workload limit and an idle capacity limit. Data portions may be ranked and selected for data movement based on read workload, write workload or idle capacity.

Abstract: A service level is assigned to each application that uses virtual memory. The service level is used to select a type of memory used when paging-in data. The service level is used to select a type of storage used when paging out data. The service level is used to select a page to evict from memory, e.g. based on service level probabilities. The service level is used to select a number of contiguous pages to page-in, e.g. based on a service level scalar. Accesses (hits) to the pages in memory may be tracked, including contiguous pages that are paged-in based on the scalar. Pages with low hit frequency may be evicted. The scalar for an application may be adjusted when at least some of the contiguous pages are infrequently accessed.

Abstract: Data storage optimization techniques determine predicted values for I/O statistics using an ARIMA (auto-regressive integrated moving average) model. The ARIMA model may be used to capture periodic patterns and trends of workload I/O access to predict the future load demand. A current set of I/O statistics is collected for a current time period T. Using the current set and one or more ARIMA models, a predicted set of I/O statistics is determined for a next time period T+1. Each of the ARIMA models is characterized by model parameters including P denoting a number of auto-regressive terms, D denoting a number of nonseasonal difference needed for stationarity, and Q denoting a number of lagged forecast errors of prediction. A data storage optimizer may determine one or more data portions for movement from a current storage tier to a target storage tier using the predicted set of I/O statistics.

Abstract: A method, computer program product, and computing system for determining a load value for each of a plurality of storage targets included within a RAID group, thus defining a plurality of load values. The plurality of load values are compared to determine if the RAID group is load balanced. If the RAID group is not load balanced, data is repositioned between the plurality of storage targets.

Abstract: An external storage resource pool associated with federated tiered storage is associated with at least one performance tier based on evaluated performance of the external storage resource pool. Performance of the external pool may be evaluated in terms of service level expectations or service level objectives. Workload pattern analysis or performance probability curves may be used to classify the external storage resource pool. Workload distribution may be determined by a margin analysis algorithm that matches workloads and storage resource pool performance characteristics.

Abstract: High performance storage resources are proactively deallocated when a storage group exceeds its service level objective. A load score is calculated for each unit of storage allocated from a high performance physical disk group. A minimum load score is determined that is the lowest load score calculated for the storage units allocated from the high performance disk group to the storage group. When the storage group service level objective is exceeded, a demotion threshold is calculated equal to a product of the minimum load score and a demotion range factor. A demotion set is determined made up of storage units allocated from the high performance disk group having load scores less than or equal to the demotion threshold. Host data stored on the demotion set is migrated to storage from a lower performance disk group, and the high performance units of storage in the demotion set are released for re-allocation.

Abstract: Described are techniques for processing service level objectives. A first service level objective specified for a storage group of devices may include a first value denoting a first target level of performance for I/O operations. A second service level objective specified for a first portion of the storage group may include a second value denoting a second target level of performance for I/O operations directed to the first portion. The second value may denote a higher level of performance than the first value. It may be determined whether there is a violation of any of the first service level objective and the second level objective. Responsive to determining there is the violation of any of the first service level objective and the second level objective, one or more data movements in accordance with the violation may be performed.

Abstract: Described are techniques user to determine expected service levels. A first set of one or more data portions are selected from one or more devices forming a first device set. The first set of data portions are stored on physical storage of a physical device set of one or more physical devices having an unknown service level expectation. I/O operations directed to the first set of data portions are received and serviced for a time period. First information is collected that characterizes performance of the physical device set during the time period. In accordance with the first information and using an adaptive learning technique, a first service level expectation for servicing I/Os directed to the physical device set is determined.

Abstract: Described are techniques for controlling performance. A forecast error Et for time period “t” denoting a difference between a predicted performance value Ft of a metric for time period “t” and an actual performance value At of the metric for time period “t” is determined. A tracking signal Tt for time period “t” is determined in accordance with “N” forecast errors. It is determined, in accordance with the tracking signal Tt for time period “t”, whether to modify a current value for alpha denoting a weight used in calculating predicted performance values for the metric. If it is determined to modify the current value for alpha, the current value for alpha is set to a revised value in accordance with the tracking signal Tt for time period “t”. A predicted performance value Ft+1 of the metric for time period “t+1” is determined in accordance with the current value for alpha.

Abstract: Promotion and demotion of extents for each storage pool is determined based on a storage pool state defined by demanded activity and demanded capacity. Promotion and demotion of extents for each storage group is determined based on whether the storage group is performing at SLO, above SLO or below SLO. Events are identified and stored in local records using hash tables. The locally stored events are used to update a database of historical extent activity. A moving average of extent activity is used to calculate the demanded activity.