SCHeMa: Scheduling Scientific Containers on a Cluster of Heterogeneous Machines

TL;DR

SCHeMa is an open-source platform designed to improve the execution and reproducibility of scientific experiments on heterogeneous clusters by integrating containerization, workflow management, and machine learning.

Contribution

It introduces SCHeMa, a novel open-source platform that combines multiple technologies to facilitate reproducible scientific computations on diverse hardware.

Findings

Enables reproducible scientific experiments on heterogeneous clusters.

Integrates containerization, workflow management, and machine learning.

Demonstrates effective execution of complex data analyses.

Abstract

In the era of data-driven science, conducting computational experiments that involve analysing large datasets using heterogeneous computational clusters, is part of the everyday routine for many scientists. Moreover, to ensure the credibility of their results, it is very important for these analyses to be easily reproducible by other researchers. Although various technologies, that could facilitate the work of scientists in this direction, have been introduced in the recent years, there is still a lack of open source platforms that combine them to this end. In this work, we describe and demonstrate SCHeMa, an open-source platform that facilitates the execution and reproducibility of computational analysis on heterogeneous clusters, leveraging containerization, experiment packaging, workflow management, and machine learning technologies.