Abstract: A technique enables recovery of storage space trapped in an extent store from overlapping write requests associated with metadata describing volume logical storage addresses for data in the extent store. The metadata is organized as metadata entries in a multi-level dense tree metadata structure. When a level of the dense tree is full, the metadata entries of the level are merged with a next lower level of the dense tree in accordance with a dense tree merge operation. The technique may be invoked during the merge operation to process the metadata entries associated with the overlapping write requests involved in the merge operation. Processing of the overlapping write requests during the merge operation may partially overwrite extents which, in turn, may result in logical storage space being trapped in the extent store. The technique may perform read-modify-write (RMW) operations on the partially overwritten extents to recapture that trapped space.

Abstract: A consistency group is used as a basic unit of data management of storage containers served by a storage input/output (I/O) stack executing on one or more nodes of a cluster. The storage container may be a LUN embodied as parent volume (active volume), a snapshot (represented as an independent volume embodied as read-only copy of the active volume), and a clone (represented as another independent volume embodied as a read-write copy (clone) of the active volume). A consistency group (CG) is a set (i.e., collection) of objects, e.g., LUNs or other CGs (nested CG), which may be managed and operated upon collectively by an administrative command via a Storage Area Network administration layer (SAL) of the storage I/O stack.

Abstract: A technique efficiently manages a snapshot and/or clone by a volume layer of a storage input/output (I/O) stack executing on one or more nodes of the cluster. According to the technique, an ownership attribute is included in metadata entries of a dense tree data structure for extents that eliminates otherwise needed reference count operations for the snapshots and reduces reference count operations for the clones. Illustratively, a copy of a parent dense tree level created by a copy-on-write (COW) operation is referred to as a “derived level”, whereas the existing level of the parent dense tree is referred to as a “source level”. The source level may be persistently linked to the derived level by keeping “level identifying key information” in a respective dense tree source level header. Moreover, two different types of dense tree derivations are defined: a derive relationship and a reverse-derive relationship.

Abstract: One or more techniques and/or computing devices are provided for cross-platform replication. For example, a replication relationship may be established between a first storage endpoint and a second storage endpoint, where at least one of the storage endpoints, such as the first storage endpoint, lacks or has incompatible functionality to perform and manage replication because the storage endpoints have different storage platforms that store data differently, use different control operations and interfaces, etc. Accordingly, replication destination workflow, replication source workflow, and/or a proxy representing the first storage endpoint may be implemented at the second storage endpoint comprising the replication functionality.

Abstract: A technique efficiently determines accurate storage space savings reported to a host coupled to a reference-counted storage system that employs de-duplication and compression, wherein the storage space savings relate to snapshots and/or clones supported by the storage system. The snapshot/clone may be represented as an independent volume, and embodied as a respective read-only copy (snapshot) or read-write copy (clone) of a parent volume. Metadata is illustratively organized as one or more multi-level dense trees, wherein each level of each dense tree includes volume metadata entries for storing the metadata. The metadata is illustratively embodied as mappings from LBAs of a LUN to extent keys. Space adjustment counters, such as clone space adjustment (CSA) and diverged space adjustment (DSA) counters, may be employed when determining the storage space savings. The CSA counter is equal to the sum of mapped storage space across all levels of a dense tree.

Abstract: In one embodiment, as new blocks of data are written to storage devices of a storage system, fingerprints are generated for those new blocks and inserted as entries into a top level (L0) of a dense tree data structure. When L0 is filled, the contents from L0 may be merged with level 1 (L1). After the initial merge, new fingerprints are added to L0 until L0 fills up again, which triggers a new merge. Duplicate fingerprints in L0 and L1 are identified which, in turn, indicates duplicate data blocks. A post-processing deduplication operation is then performed to remove duplicate data blocks corresponding to the duplicate fingerprints. In a different embodiment, as new fingerprint entries are loaded into L0, those new fingerprints may be compared with existing fingerprints loaded into L0 and/or other levels to facilitate inline deduplication to identify duplicate fingerprints and subsequently perform the deduplication operation.

Abstract: A technique restores a file system of a storage input/output (I/O) stack to a deterministic point-in-time state in the event of failure (loss) of non-volatile random access memory (NVRAM) of a node. The technique enables restoration of the file system to a safepoint stored on storage devices, such solid state drives (SSD), of the node with minimum data and metadata loss. The safepoint is a point-in-time during execution of I/O requests (e.g., write operations) at which data and related metadata of the write operations prior to the point-in-time are safely persisted on SSD such that the metadata relating to an image of the file system on SSD (on-disk) is consistent and complete. Upon reboot after NVRAM loss, the technique identifies (i) the most recent safepoint, as well as (ii) the inflight writes that were persistently stored on disk after the most recent safepoint. The data and metadata of those inflight writes are then deleted to place the on-disk file system to its state at the most recent safepoint.

Abstract: A technique efficiently creates a snapshot for a logical unit (LUN) served by a storage input/output (I/O) stack executing on a node of a cluster that organizes data as extents referenced by keys. In addition, the technique efficiently creates one or more snapshots for a group of LUNs organized as a consistency group (CG) and served by storage I/O stacks executing on a plurality of nodes of the cluster. To that end, the technique involves a plurality of indivisible operations (i.e., transactions) of a snapshot creation workflow administered by a Storage Area Network (SAN) administration layer (SAL) of the storage I/O stack in response to a snapshot create request issued by a host.

Abstract: A patient monitoring device and method that determines and monitors at least one patient parameter is provided. A configuration processor generates configuration information in response to a first input signal and an adaptive notch filter receives a second input signal. The second input signal includes a signal of interest and an interference signal in a predetermined frequency range. The adaptive notch filter automatically estimates the interference signal within the second input signal based on a filter parameter and removes the estimated interference signal from the second input signal to generate a target signal. A step processor is electrically coupled between the configuration processor and the adaptive notch filter and sets a value of the filter parameter based on the configuration information, wherein the adaptive notch filter uses the filter parameter to reduce a ringing artifact on the target signal below a threshold level.

Abstract: A bottom-up technique repairs a data structure, e.g., a multi-level dense tree, used to organize volume metadata as metadata entries managed by a volume layer of a storage input/output (I/O) stack executing on one or more nodes of a cluster. The bottom-up repair technique implements a progressive repair algorithm that initially involves traversing each level of the dense tree to determine consistency of metadata entries by ensuring that the entries, e.g., (i) monotonically increase, (ii) do not overlap and (iii), if appropriate, reference (point to) existing entries of a lower level. The technique detects and corrects inconsistencies by, e.g., deleting out-of-order and overlapping entries, and adjusting the range of an index entry to reference the corresponding lower level entry. The technique then examines whether metadata entries at a lower level of the tree are referenced (pointed to) by corresponding index entries in an upper (parent) level.

Abstract: A technique schedules processing of metadata managed by a volume layer of a storage input/output (I/O) stack executing on a node of cluster in a manner that reduces bursty activity associated with metadata processing and maintains smooth, i.e., bounded, processing latency on the node. Operations on the metadata managed by the volume layer manifest as modifications to metadata entries of data structures, i.e., dense trees, at offset ranges of the regions. The operations are dense tree merge operations that are processed by threads of execution, i.e., a uni-processor services, on one or more central processing units of the node. The scheduling technique distributes the bursty activity of the dense tree merge operations by (i) controlling concurrency of the merge operations, (ii) distributing initiation of the merge operations (i.e., staggering the merge operations), and (iii) pacing execution of merge messages to limit the continuous runtime of the merge operations.

Abstract: A technique provides memory efficient caching of metadata managed by a volume layer of a storage input/output stack executing on one or more nodes of a cluster. Efficient caching of the metadata in a memory of a node may be realized through the use of a caching data structure, i.e., a page cache, configured to store a key-value pair, wherein the key is an extent key and the value is a metadata page containing the index entries. The page cache illustratively includes two data structures configured to maintain the properties of Least Recently Used (LRU) and Least Frequently Used (LFU) for the cache. The first data structure is a hash table that stores a dense tree metadata page (value) indexed by the extent key. The second data structure is a recycle queue that controls the metadata page stored in the hash table based on spatial and temporal locality of the page.

Abstract: A technique enables recovery of storage space trapped in an extent store due to overlapping write requests associated with metadata managed by a volume layer of a storage input/output stack executing on one or more nodes of a cluster. The metadata is organized as a multi-level dense tree metadata structure, wherein each level of the dense tree includes volume metadata entries for storing the metadata. When a level of the dense tree is full, the volume metadata entries of the level are merged with a next lower level of the dense tree in accordance with a dense tree merge operation. The technique may be invoked during the merge operation to process the volume metadata entries associated with the overlapping write requests at each level of the dense tree involved in the merge operation. Processing of the overlapping write requests during the merge operation may manifest as partial overwrites of one or more existing extents which, in turn, may result in logical storage space being trapped in the extent store.

Abstract: A system can maintain multiple queues for deduplication requests of different priorities. The system can also designate priority of storage units. The scheduling priority of a deduplication request is based on the priority of the storage unit indicated in the deduplication request and a trigger for the deduplication request.

Abstract: A system can maintain multiple queues for deduplication requests of different priorities. The system can also designate priority of storage units. The scheduling priority of a deduplication request is based on the priority of the storage unit indicated in the deduplication request and a trigger for the deduplication request.

Abstract: The embodiments described herein are directed to an organization of metadata managed by a volume layer of a storage input/output (I/O) stack executing on one or more nodes of a cluster. The metadata managed by the volume layer, i.e., the volume metadata, is illustratively embodied as mappings from addresses, i.e., logical block addresses (LBAs), of a logical unit (LUN) accessible by a host to durable extent keys maintained by an extent store layer of the storage I/O stack. In an embodiment, the volume layer organizes the volume metadata as a mapping data structure, i.e., a dense tree metadata structure, which represents successive points in time to enable efficient access to the metadata.

Abstract: A technique improves efficiency of a copy-on-write (COW) operation used to create a snapshot and/or clone by a volume layer of a storage input/output (I/O) stack executing on one or more nodes of a cluster. The snapshot/clone may be represented as an independent volume, and embodied as a respective read-only copy (snapshot) or read-write copy (clone) of a parent volume. Volume metadata managed by the volume layer is organized as one or more multi-level dense tree metadata structures, wherein each level of the dense tree includes volume metadata entries for storing the metadata. The volume metadata entries may be organized as metadata pages having associated metadata page keys. Each metadata page is rendered distinct or “unique” from other metadata pages in an extent store layer of the storage I/O stack through the use of a multi-component uniqifier contained in a header of each metadata page.

Abstract: A snap restore technique efficiently restores snapshots of storage containers served by a storage input/output (I/O) stack executing on one or more nodes of a cluster. A Small Computer Systems Interface administration layer interacts with a volume layer of the storage I/O stack to manage and implement a snap restore procedure to restore one or more snapshots of a storage container. The storage container may be a logical unit (LUN) embodied as parent volume (active volume) and the snapshot may be represented as an independent volume embodied as read-only copy of the active volume. The snap restore procedure may be configured to allow restoration to a single snapshot of a LUN or restoration of a plurality of LUNs organized as a consistency group from a group of snapshots.

Abstract: A technique efficiently creates a snapshot for a logical unit (LUN) served by a storage input/output (I/O) stack executing on a node of a cluster that organizes data as extents referenced by keys. In addition, the technique efficiently creates one or more snapshots for a group of LUNs organized as a consistency group (CG) and served by storage I/O stacks executing on a plurality of nodes of the cluster. To that end, the technique involves a plurality of indivisible operations (i.e., transactions) of a snapshot creation workflow administered by a Storage Area Network (SAN) administration layer (SAL) of the storage I/O stack in response to a snapshot create request issued by a host.

Abstract: A technique preserves efficiency for replication of data between a source node of a source cluster (“source”) and a destination node of a destination cluster (“destination”) of a clustered network. Replication in the clustered network may be effected by leveraging global in-line deduplication at the source to identify and avoid copying duplicate data from the source to the destination. To ensure that the copy of the data on the destination is synchronized with the data received at the source, the source creates a snapshot of the data for use as a baseline copy at the destination. Thereafter, new data received at the source that differs from the baseline snapshot are transmitted and copied to the destination. In addition, the source and destination nodes negotiate to establish a mapping of name-to-data when transferring data (i.e., an extent) between the clusters.