Imagen de Portada

Enhancing Cluster Scheduling in HPC: A Continuous Transfer Learning for Real-Time Optimization

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Sliwko, Leszek, Mizera-Pietraszko, Jolanta
Formato:	Preprint
Publicado:	2025
Materias:	Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Cluster Workload Allocation: Semantic Soft Affinity Using Natural Language Processing
por: Sliwko, Leszek, et al.
Publicado: (2026)

Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiency
por: Sliwko, Leszek
Publicado: (2025)

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

A Taxonomy of Schedulers -- Operating Systems, Clusters and Big Data Frameworks
por: Sliwko, Leszek
Publicado: (2025)

A Meta-Heuristic Load Balancer for Cloud Computing Systems
por: Sliwko, Leszek, et al.
Publicado: (2025)

Intelligent Load Balancing in Cloud Computer Systems
por: Sliwko, Leszek
Publicado: (2025)

A Reinforced Evolution-Based Approach to Multi-Resource Load Balancing
por: Sliwko, Leszek
Publicado: (2025)

AGOCS -- Accurate Google Cloud Simulator Framework
por: Sliwko, Leszek, et al.
Publicado: (2025)

A novel strategy for multi-resource load balancing in agent-based systems
por: Sliwko, Leszek, et al.
Publicado: (2025)

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters
por: Dongare, Shruti, et al.
Publicado: (2025)

Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization
por: Xu, Zheng, et al.
Publicado: (2024)

Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management
por: Zhang, Yifan, et al.
Publicado: (2024)

FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
por: Chen, Tan, et al.
Publicado: (2025)

Enhancing Large-Scale AI Training Efficiency: The C4 Solution for Real-Time Anomaly Detection and Communication Optimization
por: Dong, Jianbo, et al.
Publicado: (2024)

Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
por: Wang, Yuqing, et al.
Publicado: (2025)

Adaptive Approach to Enhance Machine Learning Scheduling Algorithms During Runtime Using Reinforcement Learning in Metascheduling Applications
por: Alshaer, Samer, et al.
Publicado: (2025)

Systematic Optimization of Real-Time Diffusion Model Inference on Apple M3 Ultra
por: Ochiai, Yoichi
Publicado: (2026)

Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
por: Li, Boyang, et al.
Publicado: (2024)

Fairness-Aware Job Scheduling for Multi-Job Federated Learning
por: Shi, Yuxin, et al.
Publicado: (2024)

TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
por: Hu, Gangqiang, et al.
Publicado: (2024)

Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
por: Gao, Zhidong, et al.
Publicado: (2024)

An Advanced Reinforcement Learning Framework for Online Scheduling of Deferrable Workloads in Cloud Computing
por: Dong, Hang, et al.
Publicado: (2024)

CoLLM: Continuous Adaptation for SLO-Aware LLM Serving on Shared GPU Clusters
por: Huang, Shaoyuan, et al.
Publicado: (2026)

Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
por: Jadhav, Prachi, et al.
Publicado: (2025)

Clustered Federated Learning with Hierarchical Knowledge Distillation
por: Ahmad, Sabtain, et al.
Publicado: (2025)

COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training
por: Kadiyala, Divya Kiran, et al.
Publicado: (2022)

FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
por: Kahenga, Ferdinand, et al.
Publicado: (2025)

Variational Bayes for Federated Continual Learning
por: Yao, Dezhong, et al.
Publicado: (2024)

LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data
por: Zhang, Yuxin, et al.
Publicado: (2025)

Tackling the Dynamicity in a Production LLM Serving System with SOTA Optimizations via Hybrid Prefill/Decode/Verify Scheduling on Efficient Meta-kernels
por: Song, Mingcong, et al.
Publicado: (2024)

Scaling Deep Learning Research with Kubernetes on the NRP Nautilus HyperCluster
por: Hurt, J. Alex, et al.
Publicado: (2024)

FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
por: Zhang, Yuxin, et al.
Publicado: (2024)

Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning
por: Yan, Jintao, et al.
Publicado: (2024)

Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities
por: Chu, Junjie, et al.
Publicado: (2024)

Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments
por: Sun, Pengcheng, et al.
Publicado: (2024)

Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers
por: Zhang, Yue, et al.
Publicado: (2026)

OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
por: Tyagi, Sahil, et al.
Publicado: (2025)

ELIS: Efficient LLM Iterative Scheduling System with Response Length Predictor
por: Choi, Seungbeom, et al.
Publicado: (2025)

FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments
por: Pramanik, Anik, et al.
Publicado: (2026)

FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
por: Islam, Md Sirajul, et al.
Publicado: (2024)