Imagen de Portada

Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Liang, Feng, Zhang, Zhen, Lu, Haifeng, Li, Chengming, Leung, Victor C. M., Guo, Yanyi, Hu, Xiping
Formato:	Preprint
Publicado:	2024
Materias:	Distributed, Parallel, and Cluster Computing Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey
por: Liang, Feng, et al.
Publicado: (2024)

Scheduling Data-Intensive Workloads in Large-Scale Distributed Systems: Trends and Challenges
por: Stavrinides, Georgios L., et al.
Publicado: (2025)

Prediction-Assisted Online Distributed Deep Learning Workload Scheduling in GPU Clusters
por: Luo, Ziyue, et al.
Publicado: (2025)

Learning to Schedule: A Supervised Learning Framework for Network-Aware Scheduling of Data-Intensive Workloads
por: Timilsina, Sankalpa, et al.
Publicado: (2025)

Collaborative Resource Management and Workloads Scheduling in Cloud-Assisted Mobile Edge Computing across Timescales
por: Tang, Lujie, et al.
Publicado: (2024)

MSARS: A Meta-Learning and Reinforcement Learning Framework for SLO Resource Allocation and Adaptive Scaling for Microservices
por: Hu, Kan, et al.
Publicado: (2024)

iScheduler: Reinforcement Learning-Driven Continual Optimization for Large-Scale Resource Investment Problems
por: Hu, Yi-Xiang, et al.
Publicado: (2026)

FalconFS: Distributed File System for Large-Scale Deep Learning Pipeline
por: Xu, Jingwei, et al.
Publicado: (2025)

Eventually-Consistent Federated Scheduling for Data Center Workloads
por: Thiyyakat, Meghana, et al.
Publicado: (2023)

Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions
por: Zhou, Guangyao, et al.
Publicado: (2021)

Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-synchronous Federated Learning
por: Yu, Liangkun, et al.
Publicado: (2024)

Sustainable Graph Analytics Workload Scheduling with Evolutionary Reinforcement Learning in Edge-Cloud Systems
por: Ramicetty, P., et al.
Publicado: (2026)

LLMSched: Uncertainty-Aware Workload Scheduling for Compound LLM Applications
por: Zhu, Botao, et al.
Publicado: (2025)

Decouple and Decompose: Scaling Resource Allocation with DeDe
por: Xu, Zhiying, et al.
Publicado: (2024)

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
por: Svedas, Jonas, et al.
Publicado: (2025)

Scale: Deep Reinforcement Learning for Container Scheduling in Serverless Edge Computing
por: Chen, Chen, et al.
Publicado: (2026)

Task Scheduling in Geo-Distributed Computing: A Survey
por: Wu, Yujian, et al.
Publicado: (2025)

Scheduling of Distributed Applications on the Computing Continuum: A Survey
por: Mehran, Narges, et al.
Publicado: (2024)

AI Surrogate Model for Distributed Computing Workloads
por: Park, David K., et al.
Publicado: (2024)

PRISM: Dynamic Primitive-Based Forecasting for Large-Scale GPU Cluster Workloads
por: Wu, Xin, et al.
Publicado: (2026)

Distributed Hierarchical Machine Learning for Joint Resource Allocation and Slice Selection in In-Network Edge Systems
por: Rashid, Sulaiman Muhammad, et al.
Publicado: (2025)

Duration-Informed Workload Scheduler
por: Loreti, Daniela, et al.
Publicado: (2026)

Quantifying the Carbon Reduction of DAG Workloads: A Job Shop Scheduling Perspective
por: Bostandoost, Roozbeh, et al.
Publicado: (2025)

Evaluating Malleable Job Scheduling in HPC Clusters using Real-World Workloads
por: Zojer, Patrick, et al.
Publicado: (2026)

PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters
por: Jain, Rutwik, et al.
Publicado: (2024)

Tally: Non-Intrusive Performance Isolation for Concurrent Deep Learning Workloads
por: Zhao, Wei, et al.
Publicado: (2024)

Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing
por: Luo, Yizhou, et al.
Publicado: (2024)

Data Management System Analysis for Distributed Computing Workloads
por: Hsu, Kuan-Chieh, et al.
Publicado: (2025)

Adaptive Resource Allocation for Workflow Containerization on Kubernetes
por: Shan, Chenggang, et al.
Publicado: (2023)

Workflow-Driven Modeling for the Compute Continuum: An Optimization Approach to Automated System and Workload Scheduling
por: Sharma, Aasish Kumar, et al.
Publicado: (2025)

An Online Fragmentation-Aware Scheduler for Managing GPU-Sharing Workloads on Multi-Instance GPUs
por: Ting, Hsu-Tzu, et al.
Publicado: (2025)

A Review of Tools and Techniques for Optimization of Workload Mapping and Scheduling in Heterogeneous HPC System
por: Sharma, Aasish Kumar, et al.
Publicado: (2025)

Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training
por: Adnan, Muhammad, et al.
Publicado: (2024)

Workload Schedulers -- Genesis, Algorithms and Differences
por: Sliwko, Leszek, et al.
Publicado: (2025)

Distributed Load Balancing with Workload-Dependent Service Rates
por: Zhang, Wenxin, et al.
Publicado: (2024)

TD3-Sched: Learning to Orchestrate Container-based Cloud-Edge Resources via Distributed Reinforcement Learning
por: Song, Shengye, et al.
Publicado: (2025)

Resource Optimization with MPI Process Malleability for Dynamic Workloads in HPC Clusters
por: Iserte, Sergio, et al.
Publicado: (2025)

ARC-V: Vertical Resource Adaptivity for HPC Workloads in Containerized Environments
por: Medeiros, Daniel, et al.
Publicado: (2025)

Workload Buoyancy: Keeping Apps Afloat by Identifying Shared Resource Bottlenecks
por: Larsson, Oliver, et al.
Publicado: (2026)

Adaptive, Efficient and Fair Resource Allocation in Cloud Datacenters leveraging Weighted A3C Deep Reinforcement Learning
por: Kumari, Suchi, et al.
Publicado: (2025)