Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Li, Pochun, Xiao, Yuyang, Yan, Jinghua, Li, Xuan, Wang, Xiaoye |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Adaptive Cache Management for Complex Storage Systems Using CNN-LSTM-Based Spatiotemporal Prediction
par: Wang, Xiaoye, et autres
Publié: (2024)
par: Wang, Xiaoye, et autres
Publié: (2024)
AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP
par: Sun, Xiaoxuan, et autres
Publié: (2024)
par: Sun, Xiaoxuan, et autres
Publié: (2024)
Dynamic Scheduling Strategies for Resource Optimization in Computing Environments
par: Wang, Xiaoye
Publié: (2024)
par: Wang, Xiaoye
Publié: (2024)
Is Intelligence the Right Direction in New OS Scheduling for Multiple Resources in Cloud Environments?
par: Dou, Xinglei, et autres
Publié: (2025)
par: Dou, Xinglei, et autres
Publié: (2025)
Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
par: Wang, Yuqing, et autres
Publié: (2025)
par: Wang, Yuqing, et autres
Publié: (2025)
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
par: Jia, Chentao, et autres
Publié: (2023)
par: Jia, Chentao, et autres
Publié: (2023)
Autonomous Resource Management in Microservice Systems via Reinforcement Learning
par: Zou, Yujun, et autres
Publié: (2025)
par: Zou, Yujun, et autres
Publié: (2025)
Arena: Efficiently Training Large Models via Dynamic Scheduling and Adaptive Parallelism Co-Design
par: Xue, Chunyu, et autres
Publié: (2024)
par: Xue, Chunyu, et autres
Publié: (2024)
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments
par: Zhang, Ruirui, et autres
Publié: (2024)
par: Zhang, Ruirui, et autres
Publié: (2024)
Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning
par: Wang, Yang, et autres
Publié: (2025)
par: Wang, Yang, et autres
Publié: (2025)
Aryl: An Elastic Cluster Scheduler for Deep Learning
par: Li, Jiamin, et autres
Publié: (2022)
par: Li, Jiamin, et autres
Publié: (2022)
Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
par: Xiong, Guojun, et autres
Publié: (2023)
par: Xiong, Guojun, et autres
Publié: (2023)
DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning
par: Wang, Zhiyu, et autres
Publié: (2026)
par: Wang, Zhiyu, et autres
Publié: (2026)
StraightLine: An End-to-End Resource-Aware Scheduler for Machine Learning Application Requests
par: Ching, Cheng-Wei, et autres
Publié: (2024)
par: Ching, Cheng-Wei, et autres
Publié: (2024)
DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
par: Hu, Tianhao, et autres
Publié: (2026)
par: Hu, Tianhao, et autres
Publié: (2026)
Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach
par: Chen, Xiaojing, et autres
Publié: (2024)
par: Chen, Xiaojing, et autres
Publié: (2024)
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
par: Li, Li, et autres
Publié: (2021)
par: Li, Li, et autres
Publié: (2021)
Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things
par: Zhang, Tinghao, et autres
Publié: (2024)
par: Zhang, Tinghao, et autres
Publié: (2024)
ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference
par: Oh, Hyungjun, et autres
Publié: (2024)
par: Oh, Hyungjun, et autres
Publié: (2024)
Adaptive Execution Scheduler for DataDios SmartDiff
par: Poduri, Aryan
Publié: (2025)
par: Poduri, Aryan
Publié: (2025)
Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
par: Li, Boyang, et autres
Publié: (2024)
par: Li, Boyang, et autres
Publié: (2024)
ReSpec: Towards Optimizing Speculative Decoding in Reinforcement Learning Systems
par: Chen, Qiaoling, et autres
Publié: (2025)
par: Chen, Qiaoling, et autres
Publié: (2025)
Timeliness-Oriented Scheduling and Resource Allocation in Multi-Region Collaborative Perception
par: Zhu, Mengmeng, et autres
Publié: (2026)
par: Zhu, Mengmeng, et autres
Publié: (2026)
RLBoost: Harvesting Preemptible Resources for Cost-Efficient Reinforcement Learning on LLMs
par: Wu, Yongji, et autres
Publié: (2025)
par: Wu, Yongji, et autres
Publié: (2025)
Learning to Schedule Online Tasks with Bandit Feedback
par: Xu, Yongxin, et autres
Publié: (2024)
par: Xu, Yongxin, et autres
Publié: (2024)
EARL: Efficient Agentic Reinforcement Learning Systems for Large Language Models
par: Tan, Zheyue, et autres
Publié: (2025)
par: Tan, Zheyue, et autres
Publié: (2025)
REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments
par: Desai, Humaid Ahmed, et autres
Publié: (2023)
par: Desai, Humaid Ahmed, et autres
Publié: (2023)
Reinforcement Learning-based Adaptive Mitigation of Uncorrected DRAM Errors in the Field
par: Boixaderas, Isaac, et autres
Publié: (2024)
par: Boixaderas, Isaac, et autres
Publié: (2024)
Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning
par: Bian, Jieming, et autres
Publié: (2024)
par: Bian, Jieming, et autres
Publié: (2024)
Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching
par: Li, Yichen, et autres
Publié: (2024)
par: Li, Yichen, et autres
Publié: (2024)
Resource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing Systems
par: Aach, Marcel, et autres
Publié: (2024)
par: Aach, Marcel, et autres
Publié: (2024)
Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
par: Zhao, Lingxiao, et autres
Publié: (2025)
par: Zhao, Lingxiao, et autres
Publié: (2025)
AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling
par: Hamzeh, Hamed
Publié: (2026)
par: Hamzeh, Hamed
Publié: (2026)
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization
par: Perazzone, Jake B., et autres
Publié: (2025)
par: Perazzone, Jake B., et autres
Publié: (2025)
Boosting Resource-Constrained Federated Learning Systems with Guessed Updates
par: Boukhari, Mohamed Yassine, et autres
Publié: (2021)
par: Boukhari, Mohamed Yassine, et autres
Publié: (2021)
Scheduling for On-Board Federated Learning with Satellite Clusters
par: Razmi, Nasrin, et autres
Publié: (2024)
par: Razmi, Nasrin, et autres
Publié: (2024)
Alchemist: Towards the Design of Efficient Online Continual Learning System
par: Huang, Yuyang, et autres
Publié: (2025)
par: Huang, Yuyang, et autres
Publié: (2025)
DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
par: Dai, Yuanjun, et autres
Publié: (2025)
par: Dai, Yuanjun, et autres
Publié: (2025)
Adaptive Approach to Enhance Machine Learning Scheduling Algorithms During Runtime Using Reinforcement Learning in Metascheduling Applications
par: Alshaer, Samer, et autres
Publié: (2025)
par: Alshaer, Samer, et autres
Publié: (2025)
Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clients
par: Li, Yan, et autres
Publié: (2024)
par: Li, Yan, et autres
Publié: (2024)
Documents similaires
-
Adaptive Cache Management for Complex Storage Systems Using CNN-LSTM-Based Spatiotemporal Prediction
par: Wang, Xiaoye, et autres
Publié: (2024) -
AI-Driven Health Monitoring of Distributed Computing Architecture: Insights from XGBoost and SHAP
par: Sun, Xiaoxuan, et autres
Publié: (2024) -
Dynamic Scheduling Strategies for Resource Optimization in Computing Environments
par: Wang, Xiaoye
Publié: (2024) -
Is Intelligence the Right Direction in New OS Scheduling for Multiple Resources in Cloud Environments?
par: Dou, Xinglei, et autres
Publié: (2025) -
Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
par: Wang, Yuqing, et autres
Publié: (2025)