mlsanityData to 7 Oct 2026

CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters

arXiv 2308.00852 ↗First posted 1 Aug 2023cs.NI cs.DC cs.LGcited by 26

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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.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 29 of its 75 references that are in the map. First posted on arXiv 1 Aug 2023.

Builds on

29 of 75 references.

References · 29

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 12; 2026: 8.

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  2. 2025RLBoost: Harvesting Preemptible Resources for Cost-Efficient Reinforcement Learning on LLMscited by 10
  3. 2024Communication Optimization for Distributed Training: Architecture, Advances, and Opportunitiescited by 3
  4. 2024Improving training time and GPU utilization in geo-distributed language model trainingcited by 3
  5. 2025Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learningcited by 3
  6. 2025Robust Heuristic Algorithm Design with LLMscited by 3
  7. 2024GPU Cluster Scheduling for Network-Sensitive Deep Learningcited by 2
  8. 2024ForestColl: Throughput-Optimal Collective Communications on Heterogeneous Network Fabricscited by 2
  9. 2024Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Surveycited by 2
  10. 2025Sandwich: Joint Configuration Search and Hot-Switching for Efficient CPU LLM Servingcited by 2
  11. 2026UCCL-Zip: Lossless Compression Supercharged GPU Communicationcited by 2
  12. 2025m4: A Learned Flow-level Network Simulatorcited by 1
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  3. 2026ML-for-MLcited by 1
  4. 2024MLCC: A Congestion Control Technique to Accelerate ML Trainingcited by 0
  5. 2025Routing for Large ML Modelscited by 0
  6. 2025Collective Communication Profiling of Modern-day Machine Learning Workloadscited by 0
  7. 2025Morphlux: Transforming Torus Fabrics for Efficient Multi-tenant MLcited by 0
  8. 2025Detecting Anomalies in Machine Learning Infrastructure via Hardware Telemetrycited by 0
  9. 2025EPARA: Parallelizing Categorized AI Inference in Edge Cloudscited by 0
  10. 2025Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusterscited by 0
  11. 2026From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUscited by 0
  12. 2026Gleam: Adaptive Network-Efficient CUDA API Remoting for Cross-Device GPU Sharing over LANscited by 0
  13. 2026Tuning Collective Patterns to Alleviate Congestion in Shared AI Clusterscited by 0
  14. 2026COMPASS-ABS: Reducing Fragmentation in Shared GPU Clusters for Deep Learning Training Workloadscited by 0