# SLAQ: Quality-Driven Scheduling for Distributed Machine Learning

arXiv: https://arxiv.org/abs/1802.04819 · HTML: https://mlsanity.com/paper/1802.04819/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2018.

## Built on by (most cited first, 10 of 10)

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- [Spindle: Efficient Distributed Training of Multi-Task Large Models via Wavefront Scheduling](https://mlsanity.com/paper/2409.03365/) (2024)
- [Doing More by Doing Less: How Structured Partial Backpropagation Improves Deep Learning Clusters](https://mlsanity.com/paper/2111.10672/) (2021)
- [Saturn: An Optimized Data System for Large Model Deep Learning Workloads](https://mlsanity.com/paper/2309.01226/) (2023)
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- [NEPHELE: A Neural Platform for Highly Realistic Cloud Radiance Rendering](https://mlsanity.com/paper/2303.04086/) (2023)
- [LeJOT-AutoML: LLM-Driven Feature Engineering for Job Execution Time Prediction in Databricks Cost Optimization](https://mlsanity.com/paper/2603.07897/) (2026)
- [MARA: Flow-Matching-Guided Multi-Agent Resource Allocation for Computational Resource Efficient Learning](https://mlsanity.com/paper/2608.09130/) (2026)

## Abstract (arXiv)

Training machine learning (ML) models with large datasets can incur significant resource contention on shared clusters. This training typically involves many iterations that continually improve the quality of the model. Yet in exploratory settings, better models can be obtained faster by directing resources to jobs with the most potential for improvement. We describe SLAQ, a cluster scheduling system for approximate ML training jobs that aims to maximize the overall job quality. When allocating cluster resources, SLAQ explores the quality-runtime trade-offs across multiple jobs to maximize system-wide quality improvement. To do so, SLAQ leverages the iterative nature of ML training algorithms, by collecting quality and resource usage information from concurrent jobs, and then generating highly-tailored quality-improvement predictions for future iterations. Experiments show that SLAQ achieves an average quality improvement of up to 73% and an average delay reduction of up to 44% on a large set of ML training jobs, compared to resource fairness schedulers.