Abstract: One or more aspects of the present disclosure relate to service level input/output scheduling to control central processing unit (CPU) utilization. Input/output (I/O) operations are processed with one or more of a first CPU pool and a second CPU pool of two or more CPU pools. The second CPU pool processes I/O operations that are determined to stall any of the CPU cores.

Abstract: Embodiments of the present disclosure relate to traffic class management of NVMe (non-volatile memory express) traffic. One or more input/output (I/O) operations are received at a device interface coupled to one or more storage devices of a storage array. A service level (SL) corresponding to each of the one or more I/O operations is determined. Each of the one or more I/O operations is transmitted to the one or storage devices over a virtual channel of a set of virtual channels based on the determined SL corresponding to each of the one or more I/O operations.

Abstract: Service level objectives (SLOs) defined for a storage system are associated with virtual machines running on hosts. For an I/O operation for a virtual machine (VM) running on a host system, storage processing is performed on the I/O operation in accordance with the SLO defined for the VM. The same storage system-defined SLOs may be applied to VMs running on multiple host systems. SLOs may be maintained by the storage array and shared with one or more host systems in I/O communication with the storage system. SLOs may be defined using a user interface of a storage system, host system, virtualizer control station or other component of a storage network, and communicated to the storage system, which may maintain the SLOs, and may disseminate them to one or more hosts. The SLOs then may be associated with VMs running on the hosts and tagged to I/O communications corresponding to the VMs.

Abstract: Service level objectives (SLOs) defined for a storage system are associated with VMs running on hosts. For an I/O operation for a virtual machine (VM) running on a host system, storage processing is performed on the I/O operation in accordance with the SLO defined for the VM. The same storage system-defined SLOs may be applied to VMs running on multiple host systems. SLOs may be maintained by the storage array and shared with one or more host systems in I/O communication with the storage system. SLOs may be defined using a user interface of a storage system, host system, virtualizer control station or other component of a storage network, and communicated to the storage system, which may maintain the SLOs, and may disseminate them to one or more hosts. The SLOs then may be associated with VMs running on the hosts and tagged to I/O communications corresponding to the VMs.

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: Techniques are provided for service-level rebuild of one or more storage drives. Storage drives can store data of varying priority levels. In an example, when a storage drive is rebuilt, higher-priority data is written to the rebuilt storage drive before lower-priority data. In some examples where multiple storage drives are being rebuilt, a system can prioritize a use of rebuild bandwidth in rebuilding storage drives.

Abstract: Tiers of compression algorithms may be determined using compression information collected regarding compression ratios achieved for data sets using compression algorithms. Each tier may meet specified criteria regarding expected compression ratios achieved for a specified portion or number of data sets. Compression algorithms of each tier may be implemented by a different hardware device that may include hardware accelerators for the algorithms of the tier. Different tiers, and thus different hardware devices, achieve different levels of compression. A recommendation may be provided using compression information collected, such as from one of the hosts, regarding which hardware device to use for compression. The recommendation may be to purchase a license to use or whether to purchase a particular hardware device for compression. Compression information may be collected by a host that issues tagged I/Os providing a hint regarding what compression algorithm to use for the particular I/O operation data.

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: Techniques are provided for adaptive look-ahead configuration for data prefetching based on request size and frequency. One method comprises performing the following steps: estimating an earning value for a particular portion based on an average size and frequency of past input/output requests for the particular portion; calculating a quota for the particular portion by normalizing the earning value for the particular portion of the storage system based on earning values of one or more additional portions of the storage system; obtaining a size of a look-ahead window for a new request based on the quota for the particular portion over a prefetch budget assigned to the storage system; and moving a requested data item and one or more additional data items within the look-ahead window from the storage system to the cache memory responsive to the requested data item and/or the additional data items within the look-ahead window not being in the cache memory.

Abstract: Techniques for processing data may include: determining a first amount denoting an amount of write pending data stored in cache to be redirected through storage class memory (SCM) when destaging cached write pending data from the cache; performing first processing that destages write pending data from the cache, the first processing including: selecting, in accordance with the first amount, a first portion of write pending data that is destaged from the cache and stored in the SCM and a second portion of write pending data that is destaged directly from the cache and stored on one or more physical storage devices providing back-end non-volatile physical storage; and subsequent to storing the first portion of write pending data to the SCM, transferring the first portion of write pending data from the SCM to the one or more physical storage devices providing back-end non-volatile physical storage.

Abstract: Determining an expected compression rate for a prospective process in a federated system includes obtaining compression rate data for existing processes in the federated system, compiling the compression rate data into a plurality of entries in a process name table according to process identifier, client, and industry, determining a specific entry in the process name table for an existing process that most closely matches the prospective process, and determining an expected compression rate of the prospective process based on the compression rate data for the specific entry. Compression rate data may be provided by a driver at host systems that sends compression rate information to a central repository. The central repository may be provided by a host system at a data center of the federated system. The compression rate data may use a sliding average that weighs the data more heavily to favor more recent data.

Abstract: Techniques are provided for adaptive look-ahead configuration for data prefetching based on request size and frequency. One method comprises performing the following steps: estimating an earning value for a particular portion based on an average size and frequency of past input/output requests for the particular portion; calculating a quota for the particular portion by normalizing the earning value for the particular portion of the storage system based on earning values of one or more additional portions of the storage system; obtaining a size of a look-ahead window for a new request based on the quota for the particular portion over a prefetch budget assigned to the storage system; and moving a requested data item and one or more additional data items within the look-ahead window from the storage system to the cache memory responsive to one the requested data item and/or the additional data items within the look-ahead window not being in the cache memory.

Abstract: A data processing device includes persistent storage, a cache for the persistent storage, and a cache manager. The persistent storage is divided into logical units. The cache manager obtains persistent storage use data; selects model parameters for a cache prediction model based on the persistent storage use data; trains the cache prediction model based on the persistent storage use data using the selected model parameters to obtain a trained cache prediction model; and manages the cache based on logical units of the persistent storage using the trained cache prediction model.

Abstract: Described are data movements techniques may include: receiving a plurality of write quotas for a plurality of physical media types; determining a first physical storage device of a first physical media type that is over utilized with respect to a write quota; selecting a first data portion of the first physical storage device for data movement; determining a target device that is not over utilized with respect to a write quota; determining an expected total write I/O workload of the target device after hypothetically moving the first data portion thereto; determining whether the expected total write I/O workload exceeds a write quota of the target device; and responsive to determining the total write I/O workload does not exceed the write quota for the target device, moving the first data portion to the target storage device.

Abstract: Options for handling write operations may be selected based on a determined probability that a read operation for a portion of data will occur while the data portion is still in an I/O cache as a result of a write operation. As used herein, a “read-after-write event (“RAW”) is an occurrence of a read operation for a portion of data while the data portion is still in an I/O cache as a result of a write operation. The probability of a RAW may be determined by applying Bayesian inference, and may include applying exponential smoothing to calculations made on historical I/O information so that more recent I/O events have greater weight in determining RAW probability. Based on the determined RAW probability, write data may either be journaled in a write journal or written to a cache slot and de-staged to a physical storage device as part of write-in-place processing.

Abstract: Techniques are provided for service-level rebuild of one or more storage drives. Storage drives can store data of varying priority levels. In an example, when a storage drive is rebuilt, higher-priority data is written to the rebuilt storage drive before lower-priority data. In some examples where multiple storage drives are being rebuilt, a system can prioritize a use of rebuild bandwidth in rebuilding storage drives.

Abstract: Techniques for processing I/O operations may include: detecting, at a host, a sequence of I/O operations to be sent from the host to a data storage system, wherein each of the I/O operations of the sequence specifies a target address included in a first logical address subrange of a first logical device; sending, from the host, the sequence of I/O operations to a same target port of the data storage system, wherein each of the I/O operations of the sequence includes an indicator denoting whether resources used by the same target port in connection with processing said each I/O operation are to be released subsequent to completing processing of said each I/O operation; receiving the sequence of I/O operations at the same target port of the data storage system; and processing the sequence of I/O operations.

Abstract: Techniques for processing I/O operations may include: randomly assigning extent identifiers of extents to RAID groups; receiving write activity information for the extents for a sampling period; for each of the RAID groups, performing stream detection processing in accordance with the first write activity information and determining stream groups, wherein each stream group of one of the RAID groups includes extents of the RAID group belonging to a same write stream in the RAID group, wherein stream groups across all RAID groups are uniquely identified using stream group identifiers; tagging a write operation directed to one of the extents with a first stream group identifier denoting a stream group including the one extent; and issuing the write operation to a solid state drive of one of the RAID groups. Stream detection may use K-Means clustering algorithm. An aggregate address range of logical devices is mapped to the extent identifiers.

Abstract: Non-volatile cache in a host computer provides a storage tier in a system that includes at least one other tier of storage in a storage array. Selected data of a host device that represents a storage array production volume is stored in non-volatile cache. An MPIO driver in the host computer accesses the non-volatile cache or the storage array in response to an IO from a host application to the host device. A tiering engine in either the host computer or the storage array uses data access statistics to calculate which data should be promoted or demoted to non-volatile cache. Data access statistics indicative of access to non-volatile cache may be provided to the storage array by the host computer. Data changes may be aggregated by the host computer and asynchronously destaged to the storage array. Data changes or dirty data discard messages may be exchanged by peer host computer synchronously with a write operation.