# GCNScheduler: Scheduling Distributed Computing Applications using Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2110.11552 · HTML: https://mlsanity.com/paper/2110.11552/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 45 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [edGNN: a Simple and Powerful GNN for Directed Labeled Graphs](https://mlsanity.com/paper/1904.08745/) (2019)
- [WfCommons: A Framework for Enabling Scientific Workflow Research and Development](https://mlsanity.com/paper/2105.14352/) (2021)
- [Learning to Optimize DAG Scheduling in Heterogeneous Environment](https://mlsanity.com/paper/2103.06980/) (2021)

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

- [GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing](https://mlsanity.com/paper/2305.14562/) (2023)
- [Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks](https://mlsanity.com/paper/2503.24203/) (2025)

## Abstract (arXiv)

We consider the classical problem of scheduling task graphs corresponding to complex applications on distributed computing systems. A number of heuristics have been previously proposed to optimize task scheduling with respect to metrics such as makespan and throughput. However, they tend to be slow to run, particularly for larger problem instances, limiting their applicability in more dynamic systems. Motivated by the goal of solving these problems more rapidly, we propose, for the first time, a graph convolutional network-based scheduler (GCNScheduler). By carefully integrating an inter-task data dependency structure with network settings into an input graph and feeding it to an appropriate GCN, the GCNScheduler can efficiently schedule tasks of complex applications for a given objective. We evaluate our scheme with baselines through simulations. We show that not only can our scheme quickly and efficiently learn from existing scheduling schemes, but also it can easily be applied to large-scale settings where current scheduling schemes fail to handle. We show that it achieves better makespan than the classic HEFT algorithm, and almost the same throughput as throughput-oriented HEFT (TP-HEFT), while providing several orders of magnitude faster scheduling times in both cases. For example, for makespan minimization, GCNScheduler schedules 50-node task graphs in about 4 milliseconds while HEFT takes more than 1500 seconds; and for throughput maximization, GCNScheduler schedules 100-node task graphs in about 3.3 milliseconds, compared to about 6.9 seconds for TP-HEFT.