# Learning to Optimize DAG Scheduling in Heterogeneous Environment

arXiv: https://arxiv.org/abs/2103.06980 · HTML: https://mlsanity.com/paper/2103.06980/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2021.

## Built on by (most cited first, 6 of 6)

- [Machine Learning Insides OptVerse AI Solver: Design Principles and Applications](https://mlsanity.com/paper/2401.05960/) (2024)
- [GCNScheduler: Scheduling Distributed Computing Applications using Graph Convolutional Networks](https://mlsanity.com/paper/2110.11552/) (2021)
- [Learning the Markov Decision Process in the Sparse Gaussian Elimination](https://mlsanity.com/paper/2109.14929/) (2021)
- [A Learning Method with Gap-Aware Generation for Heterogeneous DAG Scheduling](https://mlsanity.com/paper/2603.23249/) (2026)
- [GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing](https://mlsanity.com/paper/2305.14562/) (2023)
- [Partition-Aware Scheduling for Mobile Heterogeneous Inference Co-Execution](https://mlsanity.com/paper/2609.14213/) (2026)

## Abstract (arXiv)

Scheduling job flows efficiently and rapidly on distributed computing clusters is one of huge challenges for daily operation of data centers. In a practical scenario, a single job consists of numerous stages with complex dependency relation represented as a Directed Acyclic Graph (DAG) structure. Nowadays a data center usually equips with a cluster of heterogeneous computing servers which are different in the hardware/software configuration. From both the cost saving and environmental friendliness, the data centers could benefit a lot from optimizing the job scheduling problems in the heterogeneous environment. Thus the problem has attracted more and more attention from both the industry and academy. In this paper, we propose a task-duplication based learning algorithm, namely lachesis footnoteThe second of the Three Fates in ancient Greek mythology, who determines destiny., aiming to optimize the problem. In the proposed approach, it first perceives the topological dependencies between jobs using a reinforcement learning framework and a specially designed graph neural network (GNN) to select the most promising task to be executed. Then the task is assigned to a specific executor with the consideration of duplicating all its precedent tasks according to an expert-designed rules. We have conducted extensive experiments over standard workloads to evaluate the proposed solution. The experimental results suggest that lachesisquad can achieve at most 26.7% reduction of makespan and 35.2% improvement of speedup ratio over seven strong baseline algorithms, including the state-of-the-art heuristics methods and a variety of deep reinforcement learning based algorithms.