  Artificial Intelligence (AI) and Deep Learning (DL) algorithms are currently
applied to a wide range of products and solutions. DL training jobs are highly
resource demanding and they experience great benefits when exploiting AI
accelerators (e.g., GPUs). However, the effective management of GPU-powered
clusters comes with great challenges. Among these, efficient scheduling and
resource allocation solutions are crucial to maximize performance and minimize
Data Centers operational costs. In this paper we propose ANDREAS, an advanced
scheduling solution that tackles these problems jointly, aiming at optimizing
DL training runtime workloads and their energy consumption in accelerated
clusters. Experiments based on simulation demostrate that we can achieve a cost
reduction between 30 and 62% on average with respect to first-principle methods
while the validation on a real cluster shows a worst case deviation below 13%
between actual and predicted costs, proving the effectiveness of ANDREAS
solution in practical scenarios.
