Speaker
Description
Complex, large-scale distributed systems are more frequently used to solve
extraordinary computing, storage and other problems. However, the development
of these systems usually requires working with several software components,
maintaining and improving large codebases, and also a relatively large number
of developers working together. Therefore, it is inevitable to introduce faults
to the system. On the other hand, these systems often perform important if not
crucial tasks so critical bugs, performance-hindering algorithms are not
acceptable to reach the production state of the software and the system. Also,
the larger number of developers can work more liberated and productively when
they receive constant feedback that their changes are still in harmony with the
system requirements and other people’s work which also greatly helps scaling
out manpower, meaning that adding more developers to a project can actually
result in more work done.
In this paper we will go through the case study of EOS, the CERN disk storage
system and introduce the methods and possibilities of how to achieve
all-automatic regression, performance, robustness testing and continuous
integration for such a large-scale, complex and critical system using
container-based environments. We will also pay special attention to the details
and challenges of testing distributed storage and file systems.
