Performance and Security Aware Distributed Service Placement in Fog Computing
Fuente:
arXiv
Saved in:
| Main Authors: | Goudarzi, Mohammad, Shaghaghi, Arash, Wang, Zhiyu, Buyya, Rajkumar |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ReinFog: A Deep Reinforcement Learning Empowered Framework for Resource Management in Edge and Cloud Computing Environments
by: Wang, Zhiyu, et al.
Published: (2024)
by: Wang, Zhiyu, et al.
Published: (2024)
TF-DDRL: A Transformer-enhanced Distributed DRL Technique for Scheduling IoT Applications in Edge and Cloud Computing Environments
by: Wang, Zhiyu, et al.
Published: (2024)
by: Wang, Zhiyu, et al.
Published: (2024)
Placement of Microservices-based IoT Applications in Fog Computing: A Taxonomy and Future Directions
by: Pallewatta, Samodha, et al.
Published: (2022)
by: Pallewatta, Samodha, et al.
Published: (2022)
A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
by: Wang, Zhiyu, et al.
Published: (2025)
by: Wang, Zhiyu, et al.
Published: (2025)
A Risk-Aware UAV-Edge Service Framework for Wildfire Monitoring and Emergency Response
by: Huang, Yulun, et al.
Published: (2026)
by: Huang, Yulun, et al.
Published: (2026)
DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning
by: Wang, Zhiyu, et al.
Published: (2026)
by: Wang, Zhiyu, et al.
Published: (2026)
Observability in Fog Computing
by: Araujo, Aleteia, et al.
Published: (2024)
by: Araujo, Aleteia, et al.
Published: (2024)
TrustMesh: A Blockchain-Enabled Trusted Distributed Computing Framework for Open Heterogeneous IoT Environments
by: Rangwala, Murtaza, et al.
Published: (2024)
by: Rangwala, Murtaza, et al.
Published: (2024)
A Joint Time and Energy-Efficient Federated Learning-based Computation Offloading Method for Mobile Edge Computing
by: Mukherjee, Anwesha, et al.
Published: (2024)
by: Mukherjee, Anwesha, et al.
Published: (2024)
AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computing
by: Wang, Zhiyu, et al.
Published: (2025)
by: Wang, Zhiyu, et al.
Published: (2025)
A System Aware Resource Allocation for Distributed Workflows in Quantum Computing Environments
by: Sawaika, Abhishek, et al.
Published: (2026)
by: Sawaika, Abhishek, et al.
Published: (2026)
DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum Cloud Computing
by: Nguyen, Hoa T., et al.
Published: (2024)
by: Nguyen, Hoa T., et al.
Published: (2024)
iAnomaly: A Toolkit for Generating Performance Anomaly Datasets in Edge-Cloud Integrated Computing Environments
by: Fernando, Duneesha, et al.
Published: (2024)
by: Fernando, Duneesha, et al.
Published: (2024)
A Deep Reinforcement Learning Approach for Cost Optimized Workflow Scheduling in Cloud Computing Environments
by: Jayanetti, Amanda, et al.
Published: (2024)
by: Jayanetti, Amanda, et al.
Published: (2024)
Reinforcement Learning based Workflow Scheduling in Cloud and Edge Computing Environments: A Taxonomy, Review and Future Directions
by: Jayanetti, Amanda, et al.
Published: (2024)
by: Jayanetti, Amanda, et al.
Published: (2024)
Proactive and Reactive Autoscaling Techniques for Edge Computing
by: Gupta, Suhrid, et al.
Published: (2025)
by: Gupta, Suhrid, et al.
Published: (2025)
EnFed: An Energy-aware Federated Learning in Resource Constrained Environments for Human Activity Recognition
by: Mukherjee, Anwesha, et al.
Published: (2024)
by: Mukherjee, Anwesha, et al.
Published: (2024)
Generative Federated Learning for Smart Prediction and Recommendation Applications
by: Mukherjee, Anwesha, et al.
Published: (2025)
by: Mukherjee, Anwesha, et al.
Published: (2025)
A Decentralized Root Cause Localization Approach for Edge Computing Environments
by: Fernando, Duneesha, et al.
Published: (2025)
by: Fernando, Duneesha, et al.
Published: (2025)
Saarthi: An End-to-End Intelligent Platform for Optimising Distributed Serverless Workloads
by: Agarwal, Siddharth, et al.
Published: (2025)
by: Agarwal, Siddharth, et al.
Published: (2025)
A Hybrid Reactive-Proactive Auto-scaling Algorithm for SLA-Constrained Edge Computing
by: Gupta, Suhrid, et al.
Published: (2025)
by: Gupta, Suhrid, et al.
Published: (2025)
A Cascaded Graph Neural Network for Joint Root Cause Localization and Analysis in Edge Computing Environments
by: Fernando, Duneesha, et al.
Published: (2026)
by: Fernando, Duneesha, et al.
Published: (2026)
Efficient Training Approaches for Performance Anomaly Detection Models in Edge Computing Environments
by: Fernando, Duneesha, et al.
Published: (2024)
by: Fernando, Duneesha, et al.
Published: (2024)
HGraphScale: Hierarchical Graph Learning for Autoscaling Microservice Applications in Container-based Cloud Computing
by: Fang, Zhengxin, et al.
Published: (2025)
by: Fang, Zhengxin, et al.
Published: (2025)
Adaptive Management of Microservices in Dynamic Computing Environments: A Taxonomy and Future Directions
by: Chen, Ming, et al.
Published: (2026)
by: Chen, Ming, et al.
Published: (2026)
DRPC: Distributed Reinforcement Learning Approach for Scalable Resource Provisioning in Container-based Clusters
by: Bai, Haoyu, et al.
Published: (2024)
by: Bai, Haoyu, et al.
Published: (2024)
QFaaS: A Serverless Function-as-a-Service Framework for Quantum Computing
by: Nguyen, Hoa T., et al.
Published: (2022)
by: Nguyen, Hoa T., et al.
Published: (2022)
Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions
by: Zhou, Guangyao, et al.
Published: (2021)
by: Zhou, Guangyao, et al.
Published: (2021)
Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT
by: Rangwala, Murtaza, et al.
Published: (2025)
by: Rangwala, Murtaza, et al.
Published: (2025)
Optimizing Task Scheduling in Fog Computing with Deadline Awareness
by: Sirjani, Mohammad Sadegh, et al.
Published: (2025)
by: Sirjani, Mohammad Sadegh, et al.
Published: (2025)
Sustainable Edge Computing: Challenges and Future Directions
by: Arroba, Patricia, et al.
Published: (2023)
by: Arroba, Patricia, et al.
Published: (2023)
Efficient Routing of Inference Requests across LLM Instances in Cloud-Edge Computing
by: Yu, Shibo, et al.
Published: (2025)
by: Yu, Shibo, et al.
Published: (2025)
Fog Device-as-a-Service (FDaaS): A Framework for Service Deployment in Public Fog Environments
by: Battula, Sudheer Kumar, et al.
Published: (2023)
by: Battula, Sudheer Kumar, et al.
Published: (2023)
ORACL: Optimized Reasoning for Autoscaling via Chain of Thought with LLMs for Microservices
by: Bai, Haoyu, et al.
Published: (2026)
by: Bai, Haoyu, et al.
Published: (2026)
FedFog: Resource-Aware Federated Learning in Edge and Fog Networks
by: Sobati-M, Somayeh
Published: (2025)
by: Sobati-M, Somayeh
Published: (2025)
REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud-Edge Continuum
by: Bai, Xu, et al.
Published: (2025)
by: Bai, Xu, et al.
Published: (2025)
Deep Reinforcement Learning (DRL)-based Methods for Serverless Stream Processing Engines: A Vision, Architectural Elements, and Future Directions
by: Read, Maria R., et al.
Published: (2024)
by: Read, Maria R., et al.
Published: (2024)
A Framework for Carbon-aware Real-Time Workload Management in Clouds using Renewables-driven Cores
by: Hewage, Tharindu B., et al.
Published: (2024)
by: Hewage, Tharindu B., et al.
Published: (2024)
iDynamics: A Configurable Emulation Framework for Evaluating Microservice Scheduling Policies under Controllable Cloud-Edge Dynamics
by: Chen, Ming, et al.
Published: (2025)
by: Chen, Ming, et al.
Published: (2025)
Aging-aware CPU Core Management for Embodied Carbon Amortization in Cloud LLM Inference
by: Hewage, Tharindu B., et al.
Published: (2025)
by: Hewage, Tharindu B., et al.
Published: (2025)
Similar Items
-
ReinFog: A Deep Reinforcement Learning Empowered Framework for Resource Management in Edge and Cloud Computing Environments
by: Wang, Zhiyu, et al.
Published: (2024) -
TF-DDRL: A Transformer-enhanced Distributed DRL Technique for Scheduling IoT Applications in Edge and Cloud Computing Environments
by: Wang, Zhiyu, et al.
Published: (2024) -
Placement of Microservices-based IoT Applications in Fog Computing: A Taxonomy and Future Directions
by: Pallewatta, Samodha, et al.
Published: (2022) -
A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
by: Wang, Zhiyu, et al.
Published: (2025) -
A Risk-Aware UAV-Edge Service Framework for Wildfire Monitoring and Emergency Response
by: Huang, Yulun, et al.
Published: (2026)