# CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters

arXiv: https://arxiv.org/abs/2308.00852 · HTML: https://mlsanity.com/paper/2308.00852/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 75 references that are in the map.
- First posted on arXiv 1 Aug 2023.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://mlsanity.com/paper/1906.00091/) (2019)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [Measuring the Effects of Data Parallelism on Neural Network Training](https://mlsanity.com/paper/1811.03600/) (2018)
- [Mesh-TensorFlow: Deep Learning for Supercomputers](https://mlsanity.com/paper/1811.02084/) (2018)
- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/1802.09941/) (2018)
- [PyTorch-BigGraph: A Large-scale Graph Embedding System](https://mlsanity.com/paper/1903.12287/) (2019)
- [Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models](https://mlsanity.com/paper/2104.05158/) (2021)
- [Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning](https://mlsanity.com/paper/2008.12260/) (2020)
- [Scaling Distributed Machine Learning with In-Network Aggregation](https://mlsanity.com/paper/1903.06701/) (2019)
- [Themis: Fair and Efficient GPU Cluster Scheduling](https://mlsanity.com/paper/1907.01484/) (2019)
- [TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches](https://mlsanity.com/paper/2111.04867/) (2021)
- [Blink: Fast and Generic Collectives for Distributed ML](https://mlsanity.com/paper/1910.04940/) (2019)
- [TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs](https://mlsanity.com/paper/2202.00433/) (2022)
- [Tesseract: Parallelize the Tensor Parallelism Efficiently](https://mlsanity.com/paper/2105.14500/) (2021)
- [Automatic Curriculum Generation for Learning Adaptation in Networking](https://mlsanity.com/paper/2202.05940/) (2022)
- [Optimizing Network Performance for Distributed DNN Training on GPU Clusters: ImageNet/AlexNet Training in 1.5 Minutes](https://mlsanity.com/paper/1902.06855/) (2019)
- [Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning](https://mlsanity.com/paper/2210.00093/) (2022)
- [Amazon SageMaker Model Parallelism: A General and Flexible Framework for Large Model Training](https://mlsanity.com/paper/2111.05972/) (2021)

## Built on by (most cited first, 26 of 26)

- [JITServe: SLO-aware LLM Serving with Imprecise Request Information](https://mlsanity.com/paper/2504.20068/) (2025)
- [RLBoost: Harvesting Preemptible Resources for Cost-Efficient Reinforcement Learning on LLMs](https://mlsanity.com/paper/2510.19225/) (2025)
- [Communication Optimization for Distributed Training: Architecture, Advances, and Opportunities](https://mlsanity.com/paper/2403.07585/) (2024)
- [Improving training time and GPU utilization in geo-distributed language model training](https://mlsanity.com/paper/2411.14458/) (2024)
- [Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning](https://mlsanity.com/paper/2504.20854/) (2025)
- [Robust Heuristic Algorithm Design with LLMs](https://mlsanity.com/paper/2510.08755/) (2025)
- [GPU Cluster Scheduling for Network-Sensitive Deep Learning](https://mlsanity.com/paper/2401.16492/) (2024)
- [ForestColl: Throughput-Optimal Collective Communications on Heterogeneous Network Fabrics](https://mlsanity.com/paper/2402.06787/) (2024)
- [Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey](https://mlsanity.com/paper/2406.08115/) (2024)
- [Sandwich: Joint Configuration Search and Hot-Switching for Efficient CPU LLM Serving](https://mlsanity.com/paper/2507.18454/) (2025)
- [UCCL-Zip: Lossless Compression Supercharged GPU Communication](https://mlsanity.com/paper/2604.17172/) (2026)
- [m4: A Learned Flow-level Network Simulator](https://mlsanity.com/paper/2503.01770/) (2025)
- [Stage-Aware Communication Scheduling for Disaggregated LLM Serving](https://mlsanity.com/paper/2603.17456/) (2026)
- [NetKV: Network-Aware Decode Instance Selection for Disaggregated LLM Inference](https://mlsanity.com/paper/2606.03910/) (2026)
- [ML-for-ML](https://mlsanity.com/paper/2608.06046/) (2026)
- [MLCC: A Congestion Control Technique to Accelerate ML Training](https://mlsanity.com/paper/2402.09589/) (2024)
- [Routing for Large ML Models](https://mlsanity.com/paper/2503.05324/) (2025)
- [Collective Communication Profiling of Modern-day Machine Learning Workloads](https://mlsanity.com/paper/2507.07117/) (2025)
- [Morphlux: Transforming Torus Fabrics for Efficient Multi-tenant ML](https://mlsanity.com/paper/2508.03674/) (2025)
- [Detecting Anomalies in Machine Learning Infrastructure via Hardware Telemetry](https://mlsanity.com/paper/2510.26008/) (2025)
- [EPARA: Parallelizing Categorized AI Inference in Edge Clouds](https://mlsanity.com/paper/2511.00603/) (2025)
- [Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters](https://mlsanity.com/paper/2512.10271/) (2025)
- [From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs](https://mlsanity.com/paper/2605.09370/) (2026)
- [Gleam: Adaptive Network-Efficient CUDA API Remoting for Cross-Device GPU Sharing over LANs](https://mlsanity.com/paper/2607.23115/) (2026)
- [Tuning Collective Patterns to Alleviate Congestion in Shared AI Clusters](https://mlsanity.com/paper/2609.04417/) (2026)
- [COMPASS-ABS: Reducing Fragmentation in Shared GPU Clusters for Deep Learning Training Workloads](https://mlsanity.com/paper/2609.18519/) (2026)

## Abstract (arXiv)

We present CASSINI, a network-aware job scheduler for machine learning (ML) clusters. CASSINI introduces a novel geometric abstraction to consider the communication pattern of different jobs while placing them on network links. To do so, CASSINI uses an affinity graph that finds a series of time-shift values to adjust the communication phases of a subset of jobs, such that the communication patterns of jobs sharing the same network link are interleaved with each other. Experiments with 13 common ML models on a 24-server testbed demonstrate that compared to the state-of-the-art ML schedulers, CASSINI improves the average and tail completion time of jobs by up to 1.6x and 2.5x, respectively. Moreover, we show that CASSINI reduces the number of ECN marked packets in the cluster by up to 33x.