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To accommodate the needs of large-scale distributed P2P systems, scalable data management strategies are required, allowing applications to efficiently cope with continuously growing, highly dis tributed data. This paper addresses the problem of efficiently stor ing and accessing very large binary data objects (blobs). It proposesan efficient versioning scheme allowing a large number of clients to concurrently read, write and append data to huge blobs that are fragmented and distributed at a very large scale. Scalability under heavy concurrency is achieved thanks to an original metadata scheme, based on a distributed segment tree built on top of a Distributed Hash Table (DHT). Our approach has been implemented and experimented within our BlobSeer prototype on the Grid5000 testbed, using up to 175 nodes.
Erasure codes are an integral part of many distributed storage systems aimed at Big Data, since they provide high fault-tolerance for low overheads. However, traditional erasure codes are inefficient on reading stored data in degraded environments (w
In this paper we study the problem of storing reliably an archive of versioned data. Specifically, we focus on systems where the differences (deltas) between subseque
Access libraries such as ROOT and HDF5 allow users to interact with datasets using high level abstractions, like coordinate systems and associated slicing operations. Unfortunately, the implementations of access libraries are based on outdated assump
One of the major performance and scalability bottlenecks in large scientific applications is parallel reading and writing to supercomputer I/O systems. The usage of parallel file systems and consistency requirements of POSIX, that all the traditional
In this paper we prove lower and matching upper bounds for the number of servers required to implement a regular shared register that tolerates unsynchronized Mobile Byzantine failures. We consider the strongest model of Mobile Byzantine failures to