  Data availability is one of the most important features in distributed
storage systems, made possible by data replication. Nowadays data are generated
rapidly and the goal to develop efficient, scalable and reliable storage
systems has become one of the major challenges for high performance computing.
In this work, we develop a dynamic, robust and strongly consistent distributed
storage implementation suitable for handling large objects (such as files). We
do so by integrating an Adaptive, Reconfigurable, Atomic Storage framework,
called ARES, with a distributed file system, called COBFS, which relies on a
block fragmentation technique to handle large objects. With the addition of
ARES, we also enable the use of an erasure-coded algorithm to further split our
data and to potentially improve storage efficiency at the replica servers and
operation latency. To put the practicality of our outcomes at test, we conduct
an in-depth experimental evaluation on the Emulab and AWS EC2 testbeds,
illustrating the benefits of our approaches, as well as other interesting
tradeoffs.
