American Megatrends, Inc.

Amzetta Technologies, LLC

Raghavan Sowrirajan

Paresh Chatterjee

Srikumar Subramanian

Vijayarankan Muthirisavenugopal

Narayanaswami Ganapathy

Jomy Jose Maliakal

Sankarji Gopalakrishnan

Sharon Samuel Enoch

Loganathan Ranganathan

Sharon Enoch

Sakthi Kumar B

Soumyadarshi Adhikari

Shakeeb Kooriyattu Puthanpurayil

Systems and methods for handling input/output operations during a space crunch are described herein. An example method includes striping a volume across a plurality of storage nodes, and maintaining a cluster volume table (CVT) storing information regarding distribution of the logical blocks. Additionally, the CVT includes a plurality of entries, where each of the entries includes information identifying a respective owner storage node of a respective logical block. The method also includes receiving a write I/O operation directed to an unallocated logical block owned by a landing storage node, where the landing storage node lacks free storage capacity, and locking the unallocated logical block. The method further includes updating the CVT to identify a storage node having free storage capacity as owner storage node of the unallocated logical block, and unlocking the unallocated logical block, wherein the write I/O operation proceeds at the storage node having free storage capacity.

Systems and methods for implementing space consolidation and space expansion in a horizontally federated cluster

Described herein are systems, methods, and computer-readable media for providing enhanced encryption in a data storage system. An example method can include receiving a data set, selecting a first portion of the data set as a unique encryption key, encrypting a second portion of the data set using the unique encryption key, and writing the encrypted second portion of the data set to a storage device.

Systems, methods and computer-readable media for providing enhanced encryption in a storage system

Technologies are described herein for accelerating the boot process of client computers by consolidating client-specific boot data in a data storage system. Boot statistics are collected for a number of client computers booting from virtual storage volumes provided by the data storage system. The boot statistics are analyzed to identify client-specific boot data stored on each of the virtual storage volumes, and the client-specific boot data is consolidated and copied into contiguous regions of a single, consolidated boot volume in the data storage system. Requests for read operations from the client computers for the client-specific boot data are then redirected to the consolidated boot volume, increasing boot performance for the client computers.

Methods, systems, and computer-readable media for boot acceleration in a data storage system by consolidating client-specific boot data in a consolidated boot volume

Systems, methods, and computer-readable media for intelligent distribution of data in a storage cluster are described herein. An example method includes maintaining a cluster volume table (CVT) that stores information regarding data distribution of a volume across a plurality of storage nodes, and defining data distribution groups within the CVT. The CVT includes a plurality of entries, where each entry identifies an owner storage node for a respective logical block of the volume. Each data distribution group includes consecutive CVT entries. The method also includes receiving an input/output (I/O) operation directed to a region of the volume, identifying an owner storage node associated with the region of the volume using the CVT, and serving the I/O operation with at least one storage node of a data distribution group associated with the owner storage node. The I/O operation is served at a chunk-level granularity that is finer than a logical-block-level granularity of the volume. The at least one storage node that serves the I/O operation may be identified by performing a division and modulo operation. A modulus may equal the number of storage nodes in the data distribution group associated with the owner storage node. Data may be striped at the chunk-level granularity across the storage nodes in the data distribution group associated with the owner storage node.

Systems and methods for intelligently distributing data in a network scalable cluster using a cluster volume table (CVT) identifying owner storage nodes for logical blocks

Systems and methods for consolidating space in a storage cluster are described herein. An example method can include striping a volume across a plurality of storage nodes, and maintaining a cluster volume table (CVT) storing information regarding distribution of a plurality of logical blocks across the storage nodes. The CVT can include a plurality of entries, where each of the entries can include information identifying a respective owner storage node of a respective logical block. The method can also include adding a new storage node to the storage cluster, and transferring data for one or more logical blocks from each of the storage nodes to the new storage node based on respective usage ratios of the storage nodes. The method can further include updating the CVT to identify the new storage node as owner storage node of the one or more logical blocks transferred to the new storage node.

Systems, methods, and computer-readable media for intelligent distribution of data in a storage cluster are described herein. An example method can include striping a volume across a plurality of storage nodes. For example, a plurality of logical blocks of the volume can be distributed to the storage nodes in relation to respective sizes of the storage nodes. Additionally, the method can include maintaining a cluster volume table (CVT) storing information regarding distribution of the logical blocks across the storage nodes. The CVT can include a plurality of entries, where each of the entries in the CVT can include information identifying a respective owner storage node of a respective logical block. Optionally, a factor can be used to determine a number of logical blocks distributed to a storage node for each stripe when striping the volume across the storage nodes.

Systems and methods for implementing a horizontally federated heterogeneous cluster

Disclosed herein is an enhanced volume manager (VM) for a storage system that accelerates input/output (I/O) performance for random write operations to a striped disk array using parity. More specifically, various implementations are directed to accelerating 'random writes' (writes comprising less than a complete stripe of data) by consolidating several random writes together to create a 'sequential write' (a full-stripe write) to eliminate one or more read operations and/or increase the volume of new/updated data stored for each write operation. Several such implementations comprise functionality in the VM (volume manager) for identifying random write I/O requests, queuing them locally in a journal, and then periodically flushing the journal to the disk array as a sequential write request.

I/O accelerator for striped disk arrays using parity

A RAID bad block module is added to a RAID controller. The bad block module intercepts bad block errors and marks them in a bad block table. When a bad block error is intercepted the bad block module logs the error and determines, based on the error and previously received errors logged in the table, whether the RAID controller can handle the error without failing the entire array. If so, the bad block module passes the error to the RAID controller. Else, the bad block module passes the error to an application or operating system where it is handled like any other disk error. Thus, instead of failing the entire array, the bad block errors are dealt with by the operating system.

Systems and methods for improved fault tolerance in RAID configurations

Technologies are provided for reclaiming a file system coupled to a block device. In these technologies, a dummy file is created. The dummy file is expanded to fill a volume in the block device. A mapping of the dummy file to locations in the volume is retrieved. When the mapping of the dummy file to the locations in the volume is retrieved, corresponding space in the volume is reclaimed. This space may include the locations in the volume corresponding to the mapping of the dummy file. When this space in the volume is reclaimed, the dummy file is deleted.

Reclaiming space from a file system

Technologies are described herein for caching variably sized access control lists (ACLs) in a data storage system utilizing page object caching. A request to access a file is received. An inode number of the requested file is identified. A hash key based on the inode number and a predefined hash value is determined. A determination is made as to whether the hash key is contained in a hash list. In response to determining that the hash key is contained in the hash list, access to a page object in the memory is provided. The page object contains an access control list (ACL) associated with the requested file. The ACL is retrieved from the page object.

Page object caching for variably sized access control lists in data storage systems

Boot acceleration by consolidating client-specific boot data in a data storage system

Technologies for eliminating duplicate data within a storage system can efficiently identify and eliminate duplication by remapping borrower regions to share physical storage space with lender regions. Block-level de-duplication can co-exist with storage architectures for thin provisioning and snapshot management. Lending maps can track redirected pointers from borrower regions to shared physical storage from lender regions. The lending maps can track the freed status of regions to support efficient write I/O operations without defaulting to unnecessary read-modify-write cycles to complete data writes. Redundancy of de-duplicated data can maintain one or more copies to support recovery from media errors. Candidate regions for de-duplication can be identified by monitoring the times and patterns of data access operations. A sampled mechanism for calculating and comparing signatures of data blocks can support the efficient identification of duplicated data within the storage system.

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Fremont, CA, United States of America

Raghavan Sowrirajan