# Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning

arXiv: https://arxiv.org/abs/2008.12260 · HTML: https://mlsanity.com/paper/2008.12260/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 30 of 72)

- [DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving](https://mlsanity.com/paper/2401.09670/) (2024)
- [LoongServe: Efficiently Serving Long-Context Large Language Models with Elastic Sequence Parallelism](https://mlsanity.com/paper/2404.09526/) (2024)
- [Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training](https://mlsanity.com/paper/2208.06102/) (2022)
- [Fairness in Serving Large Language Models](https://mlsanity.com/paper/2401.00588/) (2023)
- [StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation](https://mlsanity.com/paper/2504.15930/) (2025)
- [On Scaling Up 3D Gaussian Splatting Training](https://mlsanity.com/paper/2406.18533/) (2024)
- [Characterization of Large Language Model Development in the Datacenter](https://mlsanity.com/paper/2403.07648/) (2024)
- [Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates](https://mlsanity.com/paper/2309.08125/) (2023)
- [Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters](https://mlsanity.com/paper/2109.01313/) (2021)
- [Aryl: An Elastic Cluster Scheduler for Deep Learning](https://mlsanity.com/paper/2202.07896/) (2022)
- [CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters](https://mlsanity.com/paper/2308.00852/) (2023)
- [vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training](https://mlsanity.com/paper/2312.12391/) (2023)
- [Tenplex: Dynamic Parallelism for Deep Learning using Parallelizable Tensor Collections](https://mlsanity.com/paper/2312.05181/) (2023)
- [TRANSOM: An Efficient Fault-Tolerant System for Training LLMs](https://mlsanity.com/paper/2310.10046/) (2023)
- [Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads](https://mlsanity.com/paper/2202.07848/) (2022)
- [GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training](https://mlsanity.com/paper/2305.12201/) (2023)
- [Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2404.06114/) (2024)
- [Serving DNN Models with Multi-Instance GPUs: A Case of the Reconfigurable Machine Scheduling Problem](https://mlsanity.com/paper/2109.11067/) (2021)
- [Accelerating Distributed ML Training via Selective Synchronization](https://mlsanity.com/paper/2307.07950/) (2023)
- [Spindle: Efficient Distributed Training of Multi-Task Large Models via Wavefront Scheduling](https://mlsanity.com/paper/2409.03365/) (2024)
- [InfiniteHBD: Building Datacenter-Scale High-Bandwidth Domain for LLM with Optical Circuit Switching Transceivers](https://mlsanity.com/paper/2502.03885/) (2025)
- [Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision](https://mlsanity.com/paper/2205.11913/) (2022)
- [End-to-end Adaptive Distributed Training on PaddlePaddle](https://mlsanity.com/paper/2112.02752/) (2021)
- [Scavenger: A Cloud Service for Optimizing Cost and Performance of ML Training](https://mlsanity.com/paper/2303.06659/) (2023)
- [PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices](https://mlsanity.com/paper/2310.19991/) (2023)
- [LAPS: A Length-Aware-Prefill LLM Serving System](https://mlsanity.com/paper/2601.11589/) (2026)
- [TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling](https://mlsanity.com/paper/2608.10402/) (2026)
- [BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes](https://mlsanity.com/paper/2106.12091/) (2021)
- [Efficient Strong Scaling Through Burst Parallel Training](https://mlsanity.com/paper/2112.10065/) (2021)
- [InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models](https://mlsanity.com/paper/2308.08500/) (2023)

## Abstract (arXiv)

Pollux improves scheduling performance in deep learning (DL) clusters by adaptively co-optimizing inter-dependent factors both at the per-job level and at the cluster-wide level. Most existing schedulers expect users to specify the number of resources for each job, often leading to inefficient resource use. Some recent schedulers choose job resources for users, but do so without awareness of how DL training can be re-optimized to better utilize the provided resources. Pollux simultaneously considers both aspects. By monitoring the status of each job during training, Pollux models how their goodput (a novel metric we introduce that combines system throughput with statistical efficiency) would change by adding or removing resources. Leveraging these information, Pollux dynamically (re-)assigns resources to improve cluster-wide goodput, while respecting fairness and continually optimizing each DL job to better utilize those resources. In experiments with real DL jobs and with trace-driven simulations, Pollux reduces average job completion times by 37-50% relative to state-of-the-art DL schedulers, even when they are provided with ideal resource and training configurations for every job. Pollux promotes fairness among DL jobs competing for resources based on a more meaningful measure of useful job progress, and reveals a new opportunity for reducing DL cost in cloud environments. Pollux is implemented and publicly available as part of an open-source project at https://github.com/petuum/adaptdl.