Image de couverture de livre

Placement of Microservices-based IoT Applications in Fog Computing: A Taxonomy and Future Directions

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Pallewatta, Samodha, Kostakos, Vassilis, Buyya, Rajkumar
Format:	Preprint
Publié:	2022
Sujets:	Distributed, Parallel, and Cluster Computing
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Towards Secure Management of Edge-Cloud IoT Microservices using Policy as Code
par: Pallewatta, Samodha, et autres
Publié: (2024)

Adaptive Management of Microservices in Dynamic Computing Environments: A Taxonomy and Future Directions
par: Chen, Ming, et autres
Publié: (2026)

Performance and Security Aware Distributed Service Placement in Fog Computing
par: Goudarzi, Mohammad, et autres
Publié: (2026)

Reinforcement Learning based Workflow Scheduling in Cloud and Edge Computing Environments: A Taxonomy, Review and Future Directions
par: Jayanetti, Amanda, et autres
Publié: (2024)

TrustMesh: A Blockchain-Enabled Trusted Distributed Computing Framework for Open Heterogeneous IoT Environments
par: Rangwala, Murtaza, et autres
Publié: (2024)

TF-DDRL: A Transformer-enhanced Distributed DRL Technique for Scheduling IoT Applications in Edge and Cloud Computing Environments
par: Wang, Zhiyu, et autres
Publié: (2024)

Auto-scaling Approaches for Microservice Applications: A Survey and Taxonomy
par: Xu, Minxian, et autres
Publié: (2025)

HGraphScale: Hierarchical Graph Learning for Autoscaling Microservice Applications in Container-based Cloud Computing
par: Fang, Zhengxin, et autres
Publié: (2025)

Observability in Fog Computing
par: Araujo, Aleteia, et autres
Publié: (2024)

ReinFog: A Deep Reinforcement Learning Empowered Framework for Resource Management in Edge and Cloud Computing Environments
par: Wang, Zhiyu, et autres
Publié: (2024)

A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
par: Wang, Zhiyu, et autres
Publié: (2025)

Microservices-based Software Systems Reengineering: State-of-the-Art and Future Directions
par: Mohottige, Thakshila Imiya, et autres
Publié: (2024)

A Joint Time and Energy-Efficient Federated Learning-based Computation Offloading Method for Mobile Edge Computing
par: Mukherjee, Anwesha, et autres
Publié: (2024)

Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions
par: Zhou, Guangyao, et autres
Publié: (2021)

DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum Cloud Computing
par: Nguyen, Hoa T., et autres
Publié: (2024)

Sustainable Edge Computing: Challenges and Future Directions
par: Arroba, Patricia, et autres
Publié: (2023)

DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning
par: Wang, Zhiyu, et autres
Publié: (2026)

ORACL: Optimized Reasoning for Autoscaling via Chain of Thought with LLMs for Microservices
par: Bai, Haoyu, et autres
Publié: (2026)

Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT
par: Rangwala, Murtaza, et autres
Publié: (2025)

Generative Federated Learning for Smart Prediction and Recommendation Applications
par: Mukherjee, Anwesha, et autres
Publié: (2025)

Energy-Efficient Resource Management in Microservices-based Fog and Edge Computing: State-of-the-Art and Future Directions
par: Vali, Ali Akbar, et autres
Publié: (2025)

Optimizing Resource Allocation and Energy Efficiency in Federated Fog Computing for IoT
par: Shah, Syed Sarmad, et autres
Publié: (2025)

REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud-Edge Continuum
par: Bai, Xu, et autres
Publié: (2025)

Deep Reinforcement Learning (DRL)-based Methods for Serverless Stream Processing Engines: A Vision, Architectural Elements, and Future Directions
par: Read, Maria R., et autres
Publié: (2024)

Efficient Fog Node Placement using Nature-Inspired Metaheuristic for IoT Applications
par: Naouri, Abdenacer, et autres
Publié: (2023)

Incentive-Based Federated Learning: Architectural Elements and Future Directions
par: Kaluannakkage, Chanuka A. S. Hewa, et autres
Publié: (2025)

Quantum Cloud Computing: A Review, Open Problems, and Future Directions
par: Nguyen, Hoa T., et autres
Publié: (2024)

iDynamics: A Configurable Emulation Framework for Evaluating Microservice Scheduling Policies under Controllable Cloud-Edge Dynamics
par: Chen, Ming, et autres
Publié: (2025)

A Deep Reinforcement Learning Approach for Cost Optimized Workflow Scheduling in Cloud Computing Environments
par: Jayanetti, Amanda, et autres
Publié: (2024)

A Decentralized Root Cause Localization Approach for Edge Computing Environments
par: Fernando, Duneesha, et autres
Publié: (2025)

Proactive and Reactive Autoscaling Techniques for Edge Computing
par: Gupta, Suhrid, et autres
Publié: (2025)

A Hybrid Reactive-Proactive Auto-scaling Algorithm for SLA-Constrained Edge Computing
par: Gupta, Suhrid, et autres
Publié: (2025)

EnFed: An Energy-aware Federated Learning in Resource Constrained Environments for Human Activity Recognition
par: Mukherjee, Anwesha, et autres
Publié: (2024)

iAnomaly: A Toolkit for Generating Performance Anomaly Datasets in Edge-Cloud Integrated Computing Environments
par: Fernando, Duneesha, et autres
Publié: (2024)

A Cascaded Graph Neural Network for Joint Root Cause Localization and Analysis in Edge Computing Environments
par: Fernando, Duneesha, et autres
Publié: (2026)

A Risk-Aware UAV-Edge Service Framework for Wildfire Monitoring and Emergency Response
par: Huang, Yulun, et autres
Publié: (2026)

Differential Privacy for Secure Machine Learning in Healthcare IoT-Cloud Systems
par: Mangala, N, et autres
Publié: (2025)

A System Aware Resource Allocation for Distributed Workflows in Quantum Computing Environments
par: Sawaika, Abhishek, et autres
Publié: (2026)

Saarthi: An End-to-End Intelligent Platform for Optimising Distributed Serverless Workloads
par: Agarwal, Siddharth, et autres
Publié: (2025)

AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computing
par: Wang, Zhiyu, et autres
Publié: (2025)