Methods, apparatus, and processor-readable storage media for tracking data mirror differences are provided herein. An example computer-implemented method includes obtaining a request to start tracking data differences between a plurality of data mirror volumes of a storage system, wherein the storage system is configured to apply at least a first data tracking technique that tracks the data differences using one or more bitmap records and a second data tracking technique that tracks the data differences using or more journal records; selecting at least one of the first data tracking technique and the second data tracking technique using one or more selection criteria; and tracking the data differences in accordance with the selected at least one data tracking technique.

A solid state drive (SSD) includes a first storage region classified as byte addressable NV storage region and a controller communicatively coupled to the first storage region by a bus. The controller detects a reduced storage capacity of the first storage region, and in response to the detection, reclassifies the first storage region as a block addressable NV storage region. The SSD dynamically changes byte addressable NV storage regions to block addressable NV storage regions as the byte addressable NV storage regions are degraded, thereby extending the longevity of the SSD.

A system and method are described to efficiently allocate memory space with low latency overhead by allocating blocks of non-volatile memory on a storage device according to a tree data structure comprising a plurality of counter sets, each counter set including one or a plurality of counters indicating numbers of unallocated blocks of memory space within the non-volatile memory.

Systems and methods for reducing delays between the time at which a need for a resynchronization of data replication between a volume of a local CG and its peer volume of a remote CG is detected and the time at which the resynchronization is triggered (Reseed Time Period) are provided. According to an example, information indicative of the direction of data replication between the volume and the peer volume is maintained within a cache of a node. Responsive to a disruptive operation (e.g., relocation of the volume from an original node to a new node), the Reseed Time Period is lessened by proactively adding a passive cache entry to a cache within the new node at the time the CG relationship is created when the new node represents an HA partner of the original node and prior to the volume coming online when the new node represents a non-HA partner.

Provided are a memory controller with improved data reliability, a memory system including the memory controller, and a method of operating the memory controller. The memory controller includes an error correction code (ECC) circuit configured to perform an error detection on a codeword read from a memory device; and a processor configured to set at least one memory chip from among a plurality of memory chips as an indicator chip, monitor an error occurrence in the indicator chip based on a result of the error detection, and output reliability deterioration information indicating that the reliability of the memory device is deteriorated based on a result of the monitoring.

A method includes memory fencing in memory components of a memory sub-system and receiving a first number of commands and a second number of command for execution on a memory sub-system, receiving a memory fencing command associated with the first number of commands and the second number of commands, and executing at least one of the first number of commands before executing at least one of the second number of commands in response to receiving the memory fencing command. The method further includes executing the at least one of the first number of commands by moving data from a first location in the memory subsystem to a second location in the memory sub-system and executing the at least one of the second number of commands by reading data from the second location in the memory sub-system and sending the data to a host system.

Embodiments are directed to a cost-aware object selection for cloud garbage collection that deletes completely dead objects and also selects low-live objects up to a carefully selected liveness threshold value. This threshold is dynamically chosen per cloud garbage collection cycle by balancing costs including egress, input/output operations (IOPs), storage cost of cleaning partial live objects, and the storage cost incurred by leaving behind dead data by not cleaning the object. The threshold value is dynamically calculated to accommodate different cost models for different cloud providers and also caters to different costs for different storage tiers.

The described technology is generally directed towards a streaming data storage system that can switch between a tiered mode of operation in which events are written to Tier-1 storage and later migrated to Tier-2 storage, and a direct mode of operation in which events are written to Tier-2 storage, bypassing the tiered mode. The switching from tiered mode to direct mode, and from direct mode to tiered mode, can be automatic and based on user configuration information. For example, an event size metric (e.g., average event size) can be evaluated against user defined thresholds to determine which mode to use. If the average event size goes below a low threshold value, the tiered mode is switched to and used for appending events to a segment of a data stream. If the average event size goes above a high threshold value, the direct mode is switched to and used.

Methods, apparatus, and processor-readable storage media for automatically remediating storage device issues using machine learning techniques are provided herein. An example computer-implemented method includes obtaining performance-related information pertaining to one or more storage devices; automatically determining one or more workflows to be executed in connection with remediating at least one issue attributed to at least a portion of the one or more storage devices, wherein determining the one or more workflows comprises applying one or more machine learning techniques to the obtained performance-related information; and performing one or more automated actions based at least in part on the one or more workflows.

A system and method comprising: receiving a request to write data stored at a first range of a first volume to a second range of a second volume, where first metadata for the first range of the first volume is associated with a range of physical addresses where the data is stored in the storage system; and responsive to receiving the request: creating second metadata for the second range of the second volume, wherein the second metadata is associated with the range of physical addresses where the data is stored in the storage system; and associating the second volume with the second metadata.