# ANDREAS: Artificial intelligence traiNing scheDuler foR accElerAted resource clusterS

arXiv: https://arxiv.org/abs/2105.05080 · HTML: https://mlsanity.com/paper/2105.05080/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 5 of 5)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads](https://mlsanity.com/paper/1901.05758/) (2019)
- [DL2: A Deep Learning-driven Scheduler for Deep Learning Clusters](https://mlsanity.com/paper/1909.06040/) (2019)
- [Effective Elastic Scaling of Deep Learning Workloads](https://mlsanity.com/paper/2006.13878/) (2020)

## Built on by (most cited first, 2 of 2)

- [Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision](https://mlsanity.com/paper/2205.11913/) (2022)
- [Power- and Fragmentation-aware Online Scheduling for GPU Datacenters](https://mlsanity.com/paper/2412.17484/) (2024)

## Abstract (arXiv)

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.