Imagen de Portada

Collaborative Resource Management and Workloads Scheduling in Cloud-Assisted Mobile Edge Computing across Timescales

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Tang, Lujie, Xu, Minxian, Xu, Chengzhong, Ye, Kejiang
Formato:	Preprint
Publicado:	2024
Materias:	Distributed, Parallel, and Cluster Computing
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

BrownoutServe: SLO-Aware Inference Serving under Bursty Workloads for MoE-based LLMs
por: Hu, Jianmin, et al.
Publicado: (2025)

LSRAM: A Lightweight Autoscaling and SLO Resource Allocation Framework for Microservices Based on Gradient Descent
por: Hu, Kan, et al.
Publicado: (2024)

CloudNativeSim: a toolkit for modeling and simulation of cloud-native applications
por: Wu, Jingfeng, et al.
Publicado: (2024)

DRPC: Distributed Reinforcement Learning Approach for Scalable Resource Provisioning in Container-based Clusters
por: Bai, Haoyu, et al.
Publicado: (2024)

TempoScale: A Cloud Workloads Prediction Approach Integrating Short-Term and Long-Term Information
por: Wen, Linfeng, et al.
Publicado: (2024)

Cloud Native System for LLM Inference Serving
por: Xu, Minxian, et al.
Publicado: (2025)

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

SealOS+: A Sealos-based Approach for Adaptive Resource Optimization Under Dynamic Workloads for Securities Trading System
por: Jia, Haojie, et al.
Publicado: (2025)

UELLM: A Unified and Efficient Approach for LLM Inference Serving
por: He, Yiyuan, et al.
Publicado: (2024)

Unlock the Potential of Fine-grained LLM Serving via Dynamic Module Scaling
por: Wu, Jingfeng, et al.
Publicado: (2025)

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

C-Koordinator: Interference-aware Management for Large-scale and Co-located Microservice Clusters
por: Song, Shengye, et al.
Publicado: (2025)

DOPD: A Dynamic PD-Disaggregation Architecture for Maximizing Goodput in LLM Inference Serving
por: Liao, Junhan, et al.
Publicado: (2025)

Auto-scaling Approaches for Microservice Applications: A Survey and Taxonomy
por: Xu, Minxian, et al.
Publicado: (2025)

An Interference-aware Approach for Co-located Container Orchestration with Novel Metric
por: Li, Xiang, et al.
Publicado: (2024)

StatuScale: Status-aware and Elastic Scaling Strategy for Microservice Applications
por: Wen, Linfeng, et al.
Publicado: (2024)

BanaServe: Unified KV Cache and Dynamic Module Migration for Balancing Disaggregated LLM Serving in AI Infrastructure
por: He, Yiyuan, et al.
Publicado: (2025)

BucketServe: Bucket-Based Dynamic Batching for Smart and Efficient LLM Inference Serving
por: Zheng, Wanyi, et al.
Publicado: (2025)

FlexPipe: Adapting Dynamic LLM Serving Through Inflight Pipeline Refactoring in Fragmented Serverless Clusters
por: Lin, Yanying, et al.
Publicado: (2025)

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

CoEdge-RAG: Optimizing Hierarchical Scheduling for Retrieval-Augmented LLMs in Collaborative Edge Computing
por: Hong, Guihang, et al.
Publicado: (2025)

EdgeTimer: Adaptive Multi-Timescale Scheduling in Mobile Edge Computing with Deep Reinforcement Learning
por: Hao, Yijun, et al.
Publicado: (2024)

Dynamic Service Scheduling and Resource Management in Energy-Harvesting Multi-access Edge Computing
por: Chen, Shuyi, et al.
Publicado: (2025)

Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda
por: Xu, Minxian, et al.
Publicado: (2026)

LRScheduler: A Layer-aware and Resource-adaptive Container Scheduler in Edge Computing
por: Tang, Zhiqing, et al.
Publicado: (2025)

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
por: Gao, Chuanchao, et al.
Publicado: (2025)

Propius: A Platform for Collaborative Machine Learning across the Edge and the Cloud
por: Ding, Eric
Publicado: (2025)

Multi-Layer Scheduling for MoE-Based LLM Reasoning
por: Sun, Yifan, et al.
Publicado: (2026)

KCES: A Workflow Containerization Scheduling Scheme Under Cloud-Edge Collaboration Framework
por: Shan, Chenggang, et al.
Publicado: (2024)

Barycentric Coded Distributed Computing with Flexible Recovery Threshold for Collaborative Mobile Edge Computing
por: Qiu, Houming, et al.
Publicado: (2025)

ReinFog: A Deep Reinforcement Learning Empowered Framework for Resource Management in Edge and Cloud Computing Environments
por: Wang, Zhiyu, et al.
Publicado: (2024)

KubeDSM: A Kubernetes-based Dynamic Scheduling and Migration Framework for Cloud-Assisted Edge Clusters
por: Pashaeehir, Amirhossein, et al.
Publicado: (2025)

Efficient Routing of Inference Requests across LLM Instances in Cloud-Edge Computing
por: Yu, Shibo, et al.
Publicado: (2025)

Mobile Edge Computing
por: Ahmed, Sohaib, et al.
Publicado: (2024)

Edge AI: A Taxonomy, Systematic Review and Future Directions
por: Gill, Sukhpal Singh, et al.
Publicado: (2024)

EAT: QoS-Aware Edge-Collaborative AIGC Task Scheduling via Attention-Guided Diffusion Reinforcement Learning
por: Xu, Zhifei, et al.
Publicado: (2025)

DECICE: AI-Driven Scheduling and Digital Twin Integration for the Cloud-HPC-Edge Compute Continuum
por: Sharma, Aasish Kumar, et al.
Publicado: (2026)

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

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

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