A comparative study of energy-aware scheduling algorithms for computational grids

Recent advances in High Performance Computing (HPC) have required the attention of scientific community regarding aspects that do not concern only performance. In order to enhance computational capacity, modern parallel and distributed architectures are designed with more processing units, causing an increase in energy consumption. Currently, one of the most representative HPC platforms are computational grids, which are used in many scientific and academic projects. In this work, we propose four energy-aware scheduling algorithms to efficiently manage the energy consumption in computational grids, trying to mitigate performance loss. Our algorithms propose an efficient management of idle resources and a clever use of active ones. We have evaluated our algorithms using the SimGrid framework and an energy consumption estimation method we proposed for Bag-of-Tasks-type (BoT) applications. We compared our algorithms against five others developed to work with computational grids. In a set of experimental scenarios, our results show that by using our algorithms it is possible to achieve up to 75.90% of reduction in the energy consumption combined with 5.28% of performance loss compared with the best algorithm in performance.