Section: New Results

Reproducible experiments and papers

  • In the field of large-scale distributed systems, experimentation is particularly difficult. The studied systems are complex, often nondeterministic and unreliable, software is plagued with bugs, whereas the experiment workflows are unclear and hard to reproduce. In [5] , we provide an extensive list of features offered by general-purpose experiment management tools dedicated to distributed systems research on real platforms. We then use it to assess existing solutions and compare them, outlining possible future paths for improvements.

  • Experiment reproducibility is a milestone of the scientific method. Reproducibility of experiments in computer science would bring several advantages such as code re-usability and technology transfer. The reproducibility problem in computer science has been solved partially, addressing particular class of applications or single machine setups. In [26] , we present our approach oriented to setup complex environments for experimentation, environments that require a lot of configuration and the installation of several software packages. The main objective of our approach is to enable the exact and independent reconstruction of a given software environment and the reuse of code. We present a simple and small software appliance generator that helps an experimenter to construct a specific software stack that can be deployed on different available testbeds. [14] ,

  • In [28] , [45] , we address the question of developing a lightweight and effective workflow for conducting experimental research on modern parallel computer systems in a reproducible way. Our workflow simply builds on two well-known tools (Org-mode and Git) and enables us to address issues such as provenance tracking, experimental setup reconstruction, replicable analysis. Although this workflow is perfectible and cannot be seen as a final solution, we have been using git for two years now and we have recently published a fully reproducible article, which demonstrates the effectiveness of our proposal.