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      • GPU Sharing
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    • Resource Fragmentation
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      • Sky Computing
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      • Spot Instances
    • Remote Direct Memory Access (RDMA)
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    • Conference
      • ICML 2025
      • ATC 2025
      • OSDI 2025
      • HotOS 2025
      • MLSys 2025
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      • EuroSys 2025
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      • ICML 2024
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      • ISCA 2024
      • CVPR 2024
      • MLSys 2024
      • ASPLOS 2024
        • SpotServe: Serving generative large language models on preemptible instances
      • EuroSys 2024
        • Orion: Interference-aware, fine-grained GPU sharing for ML applications
      • NSDI 2024
      • NeurIPS 2023
      • SC 2023
        • Interference-aware multiplexing for deep learning in GPU clusters: A middleware approach
      • SoCC 2023
      • SOSP 2023
        • UGache: A unified GPU cache for embedding-based deep learning
      • SIGCOMM 2023
      • HotChips 2023
      • ICML 2023
      • ATC 2023
        • Accelerating Distributed MoE Training and Inference with Lina
        • SmartMoE: Efficiently Training Sparsely-Activated Models ...
        • Beware of Fragmentation: Scheduling GPU-Sharing Workloads with Fragmentation Gradient Descent
      • OSDI 2023
      • HotOS 2023
      • SIGMOD 2023
      • ISCA 2023
      • MLSys 2023
      • EuroSys 2023
      • NSDI 2023
        • Shepherd: Serving DNNs in the wild
        • Understanding RDMA microarchitecture resources for performance isolation
        • Skyplane: Optimizing transfer cost and throughput using cloud-aware overlays
        • Shockwave: Fair and efficient cluster scheduling for dynamic adaptation in machine learning
      • ASPLOS 2023
        • TPP: Transparent page placement for CXL-enabled tiered-memory
        • EVStore: Storage and caching capabilities for scaling embedding tables in deep recommendation system
        • Lucid: A non-intrusive, scalable and interpretable scheduler for deep learning training jobs
      • SC 2022
      • SoCC 2022
        • ESCHER: Expressive scheduling with ephemeral resources
        • Serving unseen deep learning model with near-optimal configurations: A fast adaptive search approach
      • SIGCOMM 2022
        • Multi-resource interleaving for deep learning training
      • ATC 2022
        • PilotFish: Harvesting Free Cycles of Cloud Gaming with Deep Learning Training
        • Memory Harvesting in Multi-GPU Systems with Hierarchical Unified Virtual Memory
        • Whale: Efficient Giant Model Training over Heterogeneous GPUs
        • DVABatch: Diversity-aware Multi-Entry Multi-Exit Batching for Efficient Processing of DNN Service...
        • Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing
        • SOTER: Guarding Black-box Inference for General Neural Networks at the Edge
        • Direct access, high-performance memory disaggregation with DirectCXL
      • OSDI 2022
        • Orca: A distributed serving system for transformer-based generative models
        • Microsecond-scale preemption for concurrent GPU-accelerated DNN inferences
        • Looking beyond GPUs for DNN scheduling on multi-tenant clusters
      • IPDPS 2022
        • DGSF: Disaggregated GPUs for serverless functions
      • EuroSys 2022
        • Slashing the disaggregation tax in heterogeneous data centers with FractOS
      • NSDI 2022
      • SoCC 2021
      • ATC 2021
        • Zico: Efficient GPU memory sharing for concurrent DNN training
      • OSDI 2021
        • Pollux: Co-adaptive cluster scheduling for goodput-optimized deep learning
      • SOSP 2021
        • HeMem: Scalable Tiered Memory Management for Big Data Applications and Real NVM
      • EuroSys 2021
        • Take it to the limit: Peak prediction-driven resource overcommitment in datacenters
      • HotOS 2021
        • From cloud computing to sky computing
      • NSDI 2021
      • OSDI 2020
        • A unified architecture for accelerating distributed DNN training in heterogeneous GPU/CPU clusters
        • HiveD: Sharing a GPU cluster for deep learning with guarantees
      • ATC 2020
        • Serverless in the wild: Characterizing and optimizing the serverless workload
      • EuroSys 2020
      • ASPLOS 2020
      • MLSys 2020
      • SoCC 2020
        • Elastic Parameter Server Load Distribution in Deep Learning Clusters
      • HPDC 2020
        • KubeShare: A framework to manage GPUs as first-class and shared resources in container cloud
      • CLUSTER 2019
      • EuroSys 2019
      • NSDI 2019
      • IWQoS 2019
        • Who limits the resource efficiency of my datacenter: An analysis of Alibaba datacenter traces
      • SIGCOMM 2018
        • Revisiting network support for RDMA
      • OSDI 2018
        • Ray: A distributed framework for emerging AI applications
      • EuroSys 2018
        • Medea: Scheduling of long running applications in shared production clusters
      • ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018
        • GaiaGPU: Sharing GPUs in container clouds
      • SoCC 2017
        • SLAQ: Quality-driven scheduling for distributed machine learning
      • ASPLOS 2017
        • Neurosurgeon: Collaborative intelligence between the cloud and mobile edge
      • NSDI 2017
        • Clipper: A low-latency online prediction serving system
      • CLUSTER 2014
        • Evaluating job packing in warehouse-scale computing
    • Journal
      • IEEE Transactions on Cloud Computing
        • 2021
          • Gemini: Enabling multi-tenant GPU sharing based on kernel burst estimation
      • ACM Computing Surveys
        • 2017
          • GPU virtualization and scheduling methods: A comprehensive survey
      • ACM SIGCOMM Computer Communication Review (CCR)
        • 2021
          • Data-driven Networking Research: models for academic collaboration with industry
        • 2007
          • How to Read a Paper
      • Communications of the ACM
        • 2015
          • Why Google stores billions of lines of code in a single repository
    • Miscellaneous
      • arXiv
        • 2024
          • Efficiently programming large language models using SGLang
        • 2023
          • HexGen: Generative inference of foundation model over heterogeneous decentralized environment
          • High-throughput generative inference of large language models with a single GPU
        • 2022
          • DisaggRec: Architecting disaggregated systems for large-scale personalized recommendation
          • A case for disaggregation of ML data processing
          • Singularity: Planet-scale, preemptive and elastic scheduling of AI workloads
          • Aryl: An elastic cluster scheduler for deep learning
        • 2016
          • Wide & deep learning for recommender systems
          • Training deep nets with sublinear memory cost
      • MSR Technical Report
        • 2011
          • Heuristics for vector bin packing
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  • Large Language Models (LLMs)
  • Distributed Training
  • Model Serving
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  • Resource Management
  • Acronyms

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  1. Reading Notes
  2. Conference

EuroSys 2025

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Meta Info

Homepage:

Paper list:

Proceedings:

Acceptance Rate

  • Overall: 12.2% (= 85 / 696)

    • Total: 85 (= 44 + 41)

    • 11 revised papers from EuroSys'25 Fall

  • Fall: 8.2% (= 30 / 367)

    • 14 revised papers from EuroSys'25 Spring

    • Total: 44 (= 30 + 14)

  • Spring: 9.7% (= 32 / 329)

    • 9 revised papers from EuroSys'24 Fall

    • Total: 41 (= 32 + 9)

Papers

Large Language Models (LLMs)

  • LLM Training

    • Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization

      • UofT

    • MEPipe: Democratizing LLM Training with Memory-Efficient Slice-Level Pipeline Scheduling on Cost-Effective Accelerators

      • THU & Zhipu AI

  • LLM Inference

    • Fast State Restoration in LLM Serving with HCache

      • THU

    • Stateful Large Language Model Serving with Pensieve

      • NYU

    • CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion

      • CUHK-Shenzhen & UChicago & Stanford

      • Best Paper Award (Spring)

    • T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge

      • USTC & MSRA

    • DeltaZip: Efficient Serving of Multiple Full-Model-Tuned LLMs

      • ETH & MIT

    • SpInfer: Leveraging Low-Level Sparsity for Efficient Large Language Model Inference on GPUs

      • HKUST-GZ

      • Best Paper Award (Fall)

  • LLM Fine-Tuning

    • HybridFlow: A Flexible and Efficient RLHF Framework

      • HKU & ByteDance

  • Mixture-of-Experts (MoEs)

    • Samoyeds: Accelerating MoE Models with Structured Sparsity Leveraging Sparse Tensor Cores

      • SJTU

Distributed Training

  • JABAS: Joint Adaptive Batching and Automatic Scaling for DNN Training on Heterogeneous GPUs

    • UNIST & Samsung

  • FlowCheck: Decoupling Checkpointing and Training of Large-Scale Models

    • SJTU & Alibaba Cloud

  • Comprehensive Deadlock Prevention for GPU Collective Communication

    • PKU & OneFlow

Model Serving

  • A House United Within Itself: SLO-Awareness for On-Premises Containerized ML Inference Clusters via Faro

    • UIUC & IBM Research

    • UC Berkeley

    • Manage a mixture of spot and on-demand replicas across regions and clouds.

    • Improve availability, reduce correlated preemptions, overprovision cheap spot replicas.

Deep Learning Compilation

  • SpaceFusion: Advanced Deep Learning Operator Fusion via Space-Mapping Graph

    • SJTU

Resource Management

  • Scheduling

    • Towards VM Rescheduling Optimization Through Deep Reinforcement Learning

      • UC Merced & UC Berkeley & ByteDance

    • Eva: Cost-Efficient Cloud-Based Cluster Scheduling

      • UW-Madison

  • Serverless Computing

    • Serverless Cold Starts and Where to Find Them

      • Huawei

    • SeBS-Flow: Benchmarking Serverless Cloud Function Workflows

      • Karlsruhe Institute of Technology & ETH

    • AlloyStack: A Library Operating System for Serverless Workflow Applications

      • TJU & THU

  • GPU Sharing

    • Improving GPU Sharing Performance through Adaptive Bubbleless Spatial-Temporal Sharing

      • SJTU & Microsoft & Alibaba

    • Multiplexing Dynamic Deep Learning Workloads with SLO-awareness in GPU Clusters

      • University of Macau & SIAT, CAS

Acronyms

  • RLHF: Reinforcement Learning from Human Feedback

  • ML: Machine Learning

SkyServe: Serving AI Models across Regions and Clouds with Spot Instances [] [] []

Baselines: AWS Auto-scaling Group (ASG), MArk [ATC'19], AWS spot node pool (AWSSpot),

https://2025.eurosys.org
https://2025.eurosys.org/accepted-papers.html
https://dl.acm.org/doi/proceedings/10.1145/3689031
Paper
Code
arXiv
SpotServe