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BlobSeer is a large-scale distributed storage service that addresses advanced data management requirements resulting from ever-increasing data sizes. It is centered around the idea of leveraging versioning for concurrent manipulation of binary large objects in order to efficiently exploit data-level parallelism and sustain a high throughput desp…
bnicolae/blobseer
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BlobSeer ======== BlobSeer is a large-scale, distributed data storage system that addresses storage requirements in an innovating way, aiming to deal with the explosion of data sizes in a scalable fashion. It emerged from the idea of leveraging versioning for concurrent manipulation of binary large objects (BLOBs) in order to efficiently exploit data-level parallelism and sustain a high throughput despite massively parallel data accesses. Features of BlobSeer include: - a versioning based access model - distributed data and metadata - instant snapshots and clones - replication and erasure code based fault tolerance support - deduplication and compression - multiple subprojects: * BSFS: enables storage for MapReduce by acting as a replacement for HDFS * BlobVM: enables storage and management of VMs by exposing BLOBS as raw virtual machine images BlobSeer was written by Bogdan Nicolae and forms the core of his PhD thesis "BlobSeer: Towards efficient data storage management for large-scale, distributed systems", defended Nov 30, 2010 at the University of Rennes 1, France. It is currently used in numerous research projects of the KerData team, INRIA Rennes, France. Parts of this code is based on the JERASURE library, Copright (C) 2007 James S. Plank and released under LGPL 2.1. Bindings for Ruby and Fortran were contributed by Matthieu Dorier. Bindings for Python were contributed by Benjamin Giraud. BlobSeer is released as an open-source project under LGPL 3.0. Please see the accompanying lgpl-3.0.txt file for licensing details. For additional information please visit: http://blobseer.gforge.inria.fr
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BlobSeer is a large-scale distributed storage service that addresses advanced data management requirements resulting from ever-increasing data sizes. It is centered around the idea of leveraging versioning for concurrent manipulation of binary large objects in order to efficiently exploit data-level parallelism and sustain a high throughput desp…
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