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: In one embodiment, a node coupled to one or more storage devices executes a storage input/output (I/O) stack having a volume layer, a persistence layer and an administration layer that interact to create a copy of a parent volume associated with a storage container on the one or more storage devices. A copy create start message is received at the persistence layer from the administration layer. The persistence layer ensures that dirty data for the parent volume is incorporated into the copy of the parent volume. New data for the parent volume received at the persistence layer during creation of the copy of the parent volume is prevented from incorporation into the copy of the parent volume. A reply to the copy create start message is sent from the persistence layer to the administration layer to initiate the creation of the copy of the parent volume at the volume layer.

Abstract: In one embodiment, a node of a cluster executing a storage input/output (I/O) stack having a volume layer, stores a multi-level dense tree metadata structure. Each level of the dense tree metadata structure includes volume metadata entries for storing volume metadata. One or more non-volatile logs (NVLogs) are updated. The one or more NVLogs including a volume layer log configured to record changes to the volume metadata, wherein volume metadata entries inserted into a top-level of the dense tree metadata structure are recorded in the volume layer log. The node writes volume metadata entries from the volume layer log to one or more storage devices to be stored as extents.

Abstract: In one embodiment, a node coupled to one or more storage devices executes a storage input/output (I/O) stack having a volume layer that manages volume metadata. The volume metadata is organized as one or more dense tree metadata structures having a top level residing in memory and lower levels residing on the one or more storage devices. The dense tree metadata structures include a first dense tree metadata structure associated with a parent volume and a second dense tree metadata structure associated with a copy of the parent volume. The top level of the first dense tree metadata structure may be copied to the second dense tree metadata structure. The lower levels of the first dense tree metadata structure are initially shared with the second dense tree metadata structure. The shared lower levels may eventually be split as the parent volume diverges from the copy of the parent volume.

Abstract: An optical analyte sensor and diabetes management system is provided. The sensor preferably includes a hydrogel matrix for receiving a sample containing an analyte at unknown concentration, a light emitter for emitting light at a stimulation frequency, a light receiver for receiving a fluorescence signal at a first isosbestic frequency, and at a second frequency, for measuring an intensity of the fluorescence signal and the first and second frequencies. A processor determines a concentration of the analyte based on the respective intensities.

Abstract: In one embodiment, a node of a cluster executing a storage input/output (I/O) stack having a volume layer, stores a multi-level dense tree metadata structure. Each level of the dense tree metadata structure includes volume metadata entries for storing volume metadata. One or more non-volatile logs (NVLogs) are updated. The one or more NVLogs including a volume layer log configured to record changes to the volume metadata, wherein volume metadata entries inserted into a top-level of the dense tree metadata structure are recorded in the volume layer log. The node writes volume metadata entries from the volume layer log to one or more storage devices to be stored as extents.

Abstract: In one embodiment, snapshots and/or clones of storage objects are created and managed by a volume layer of a storage input/output (I/O) stack executing on one or more nodes of a cluster. Illustratively, the snapshots and clones may be represented as independent volumes, and embodied as respective read-only copies (snapshots) and read-write copies (clones) of a parent volume. Volume metadata is illustratively organized as one or more multi-level dense tree metadata structures, wherein each level of the dense tree metadata structure (dense tree) includes volume metadata entries for storing the metadata. Each snapshot/clone may be derived from a dense tree of the parent volume (parent dense tree). Portions of the parent dense tree may be shared with the snapshot/clone.

Abstract: In one embodiment, snapshots and/or clones of storage objects are created and managed by a volume layer of a storage input/output (I/O) stack executing on one or more nodes of a cluster. Illustratively, the snapshots and clones may be represented as independent volumes, and embodied as respective read-only copies (snapshots) and read-write copies (clones) of a parent volume. Volume metadata is illustratively organized as one or more multi-level dense tree metadata structures, wherein each level of the dense tree metadata structure (dense tree) includes volume metadata entries for storing the metadata. Each snapshot/clone may be derived from a dense tree of the parent volume (parent dense tree). Portions of the parent dense tree may be shared with the snapshot/clone.

Abstract: In one embodiment, a node coupled to one or more storage devices executes a storage input/output (I/O) stack having a volume layer that manages volume metadata. The volume metadata is organized as one or more dense tree metadata structures having a top level residing in memory and lower levels residing on the one or more storage devices. The dense tree metadata structures include a first dense tree metadata structure associated with a parent volume and a second dense tree metadata structure associated with a copy of the parent volume. The top level of the first dense tree metadata structure may be copied to the second dense tree metadata structure. The lower levels of the first dense tree metadata structure are initially shared with the second dense tree metadata structure. The shared lower levels may eventually be split as the parent volume diverges from the copy of the parent volume.

Abstract: The embodiments described herein are directed to efficient logging and checkpointing 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 organized as a multi-level dense tree metadata structure, wherein each level of the dense tree metadata structure (dense tree) includes volume metadata entries for storing the volume metadata. Each volume metadata entry may be a descriptor that embodies one of a plurality of types, including a data entry and an index entry, and a hole (i.e., absence of data) entry.

Abstract: In one embodiment, a node coupled to one or more storage devices executes a storage input/output (I/O) stack having a volume layer. The volume layer manages volume metadata embodied as mappings from offsets of a logical unit (LUN) to extent keys associated with storage locations for extents on the one or more storage devices. Volume metadata is maintained as a dense tree metadata structure representing successive points in time. The dense tree metadata structure has multiple levels, wherein a top level of the dense tree metadata structure represents newer volume metadata changes and descending levels of the dense tree metadata structure represent older volume metadata changes. The node accesses a latest version of changes to the volume metadata by searching from the top level to the descending levels in the dense tree metadata structure.

Abstract: Prediction of a channel capacity is accomplished based on a TDR echo without explicitly estimating the topology of the line. The prediction is based on obtaining a measured TDR echo, determining a theoretical TDR echo for a plurality of loop lengths, estimating the equivalent TDR length based on an optimization, updating the equivalent TDR length and utilizing the updated TDR length to predict one or more of the upstream and downstream data rates.

Abstract: A method performed in a system that has a plurality of volumes stored to storage hardware, the method including generating, for each of the volumes, a respective space saving potential iteratively over time and scheduling space saving operations among the plurality of volumes by analyzing each of the volumes for space saving potential and assigning priority of resources based at least in part on space saving potential.

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: Systems for deduplicating one or more storage units of a storage system provide a scheduler, which is operable to select at least one storage unit (e.g. a storage volume) for deduplication and perform a deduplication process, which removes duplicate data blocks from the selected storage volume. The systems are operable to determine the state of one or more storage units and manage deduplication requests in part based state information. The system is further operable to manage user generated requests and manage deduplication requests in part based on user input information. The system may include a rules engine which prioritizes system operations including determining an order in which to perform state-gathering information and determining an order in which to perform deduplication. The system is further operable to determine the order in which storage units are processed.

Abstract: A method for sharing data blocks in a hierarchical file system in a storage server includes allocating a plurality of data blocks in the file system, and sharing data blocks in the file system, without using a persistent point-in-time image, to avoid duplication of data blocks. A method for identifying data blocks that can be shared includes computing a fingerprint for each of multiple data blocks to be written to a storage facility and storing the fingerprint with information identifying the data block in an entry in a set of metadata. The set of metadata is used to identify data blocks which are duplicates.

Abstract: Compositions and methods related to assessing the risk of cancer, such as breast cancer, lung cancer and bladder cancer, through analyzing the length of telomeres, such as chromosome 9p, 15p, and/or Xp telomere, such as the short arm of the 9p, 15p, and/or Xp telomere.

Abstract: A method performed in a system that has a plurality of volumes stored to storage hardware, the method including generating, for each of the volumes, a respective space saving potential iteratively over time and scheduling space saving operations among the plurality of volumes by analyzing each of the volumes for space saving potential and assigning priority of resources based at least in part on space saving potential.

Abstract: A system includes a low pass-filter and a Savitzky-Golay filter. The low-pass filter receives and processes a first electrocardiogram signal. The filter removes at least the high frequency components of the first electrocardiogram signal. The Savitzky-Golay filter estimates a baseline variation of the first electrocardiogram signal from the filtered first electrocardiogram signal. Related apparatus, systems, techniques and articles are also described.

Abstract: Prediction of a channel capacity is accomplished based on a TDR echo without explicitly estimating the topology of the line. The prediction is based on obtaining a measured TDR echo, determining a theoretical TDR echo for a plurality of loop lengths, estimating the equivalent TDR length based on an optimization, updating the equivalent TDR length and utilizing the updated TDR length to predict one or more of the upstream and downstream data rates.