# Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning

arXiv: https://arxiv.org/abs/2210.00093 · HTML: https://mlsanity.com/paper/2210.00093/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2022.

## Built on by (most cited first, 12 of 12)

- [CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters](https://mlsanity.com/paper/2308.00852/) (2023)
- [Resource Heterogeneity-Aware and Utilization-Enhanced Scheduling for Deep Learning Clusters](https://mlsanity.com/paper/2503.10918/) (2025)
- [Efficient Serving of LLM Applications with Probabilistic Demand Modeling](https://mlsanity.com/paper/2506.14851/) (2025)
- [EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms](https://mlsanity.com/paper/2606.04145/) (2026)
- [The Price of Anarchy in Disaggregated Inference](https://mlsanity.com/paper/2606.17081/) (2026)
- [Towards providing reliable job completion time predictions using PCS](https://mlsanity.com/paper/2401.10354/) (2024)
- [MLCC: A Congestion Control Technique to Accelerate ML Training](https://mlsanity.com/paper/2402.09589/) (2024)
- [Tesserae: Scalable Placement Policies for Deep Learning Workloads](https://mlsanity.com/paper/2508.04953/) (2025)
- [AEGIS: Runtime-Guided GPU Collocation for Multi-Tenant Deep Learning Training](https://mlsanity.com/paper/2508.19073/) (2025)
- [From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs](https://mlsanity.com/paper/2605.09370/) (2026)
- [NEURON-Fabric: Architecture-Runtime Co-Design for Controlled Low-Bit Gradient Communication](https://mlsanity.com/paper/2606.25759/) (2026)
- [Global Simulation-Guided Dynamic Operator Scheduling for Efficient Multi-Tenant Model Serving](https://mlsanity.com/paper/2608.15762/) (2026)

## Abstract (arXiv)

Dynamic adaptation has become an essential technique in accelerating distributed machine learning (ML) training. Recent studies have shown that dynamically adjusting model structure (e.g., lottery ticket hypothesis) or hyperparameters (e.g., batch size) can significantly accelerate training without sacrificing accuracy. However, existing ML cluster schedulers are not designed to handle dynamic adaptation. We show that existing schemes fail to provide fairness and degrade system efficiency when the training throughput changes over time under dynamic adaptation. We design Shockwave, a scheduler with future planning that builds on two key ideas. First, Shockwave extends classic market theory from static settings to dynamic settings to co-optimize efficiency and fairness. Second, Shockwave utilizes stochastic dynamic programming to handle dynamic changes. We build a system for Shockwave and validate its performance with both trace-driven simulation and cluster experiments. Results show that for traces of ML jobs with dynamic adaptation, Shockwave improves makespan by 1.3X and fairness by 2X when compared with existing fair scheduling schemes.