Conveners
Reproducibility and Open Data: Lightning talks
- Serge Fdida (Sorbonne)
Computational science today depends on complex, data-intensive applications operating on datasets from
a variety of scientific instruments. A major challenge is the integration of data into the scientist's workflow.
Recent advances in dynamic, networked cloud resources provide the building blocks to construct
reconfigurable, end-to-end infrastructure that can increase scientific...
Cloud testbeds are critical for enabling research into new cloud technologies - research that
requires experiments which potentially change the operation of the cloud itself. Several such
testbeds have been created in recent past (e.g., Chameleon, CloudLab, etc.) with the goal to
support the CISE systems research community. It has been shown that these testbeds are very
popular and heavily...
Computer Science experimental testbeds allow investigators to explore a broad
range of different state-of-the-art hardware options, assess scalability of their
systems, and provide conditions that allow deep reconfigurability and isolation so
that one user does not impact the experiments of another. Although the primary
purpose of those testbeds is to provide resources to users who would...
Reproducibilification, i.e., making experiments reproducible, is the
ultimate goal for successful scientific experiments. In this work, we
identify key challenges for the design of reproducible network experiments.
We present our approach for reproducible network research
which enforces an experiment workflow leading to inherently replicable
network experiments. Our approach realized in...
