Saved in:
| Main Authors: | Silano, Giuseppe, Rikos, Evangelos, Rajkumar, Vetrivel, Gehrke, Oliver, Zerihun, Tesfaye Amare, Rodio, Carmine, Lazzari, Riccardo |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.00093 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Performance and Security Aware Distributed Service Placement in Fog Computing
by: Goudarzi, Mohammad, et al.
Published: (2026)
by: Goudarzi, Mohammad, et al.
Published: (2026)
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)
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)
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)
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)
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)
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)
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)
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)
SoK: Consensus for Fair Message Ordering
by: Li, Zhuolun, et al.
Published: (2024)
by: Li, Zhuolun, et al.
Published: (2024)
Linear Search for Capturing an Oblivious Mobile Target in the Sender/Receiver Model
by: Jawhar, Khaled, et al.
Published: (2025)
by: Jawhar, Khaled, et al.
Published: (2025)
Bike Assisted Evacuation on a Line of Robots with Communication Faults
by: Jawhar, Khaled, et al.
Published: (2023)
by: Jawhar, Khaled, et al.
Published: (2023)
A Decentralized Root Cause Localization Approach for Edge Computing Environments
by: Fernando, Duneesha, et al.
Published: (2025)
by: Fernando, Duneesha, 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)
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)
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 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)
Proactive and Reactive Autoscaling Techniques for Edge Computing
by: Gupta, Suhrid, et al.
Published: (2025)
by: Gupta, Suhrid, et al.
Published: (2025)
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)
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)
A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communication
by: Rodio, Angelo, et al.
Published: (2025)
by: Rodio, Angelo, et al.
Published: (2025)
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)
Unleashing the Power of Tree-of-Thoughts for Edge-Enabled AIGC Service Provisioning
by: Liu, Zhang, et al.
Published: (2026)
by: Liu, Zhang, et al.
Published: (2026)
FunLess: Functions-as-a-Service for Private Edge Cloud Systems
by: De Palma, Giuseppe, et al.
Published: (2024)
by: De Palma, Giuseppe, et al.
Published: (2024)
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)
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)
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)
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)
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)
Capturing a Moving Target by Two Robots in the F2F Model
by: Jawhar, Khaled, et al.
Published: (2025)
by: Jawhar, Khaled, et al.
Published: (2025)
Online Distributed Learning with Quantized Finite-Time Coordination
by: Bastianello, Nicola, et al.
Published: (2023)
by: Bastianello, Nicola, et al.
Published: (2023)
Coordinating GPU Data Centers and Power Grid Regulation Service for Exogenous Carbon Benefits
by: Jahanshahi, Ali, et al.
Published: (2026)
by: Jahanshahi, Ali, 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)
Multi-Layer Scheduling for MoE-Based LLM Reasoning
by: Sun, Yifan, et al.
Published: (2026)
by: Sun, Yifan, et al.
Published: (2026)
Observability in Fog Computing
by: Araujo, Aleteia, et al.
Published: (2024)
by: Araujo, Aleteia, et al.
Published: (2024)
Similar Items
-
A Risk-Aware UAV-Edge Service Framework for Wildfire Monitoring and Emergency Response
by: Huang, Yulun, et al.
Published: (2026) -
Performance and Security Aware Distributed Service Placement in Fog Computing
by: Goudarzi, Mohammad, et al.
Published: (2026) -
A Joint Time and Energy-Efficient Federated Learning-based Computation Offloading Method for Mobile Edge Computing
by: Mukherjee, Anwesha, et al.
Published: (2024) -
EnFed: An Energy-aware Federated Learning in Resource Constrained Environments for Human Activity Recognition
by: Mukherjee, Anwesha, et al.
Published: (2024) -
Generative Federated Learning for Smart Prediction and Recommendation Applications
by: Mukherjee, Anwesha, et al.
Published: (2025)