ANDREAS: Artificial intelligence traiNing scheDuler foR accElerAted resource clusterS
This addresses operational cost challenges for data centers running AI workloads, but it is incremental as it builds on existing scheduling methods.
The paper tackles the problem of inefficient scheduling and resource allocation for deep learning training jobs on GPU-powered clusters, proposing ANDREAS to optimize runtime workloads and energy consumption, achieving a cost reduction of 30-62% on average compared to baseline methods.
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.