Energy efficient task scheduling based on deep reinforcement learning in cloud environment: A specialized review DOI
Huanhuan Hou, Siti Nuraishah Agos Jawaddi,

Azlan Ismail

et al.

Future Generation Computer Systems, Journal Year: 2023, Volume and Issue: 151, P. 214 - 231

Published: Oct. 14, 2023

Language: Английский

Edge Offloading in Smart Grid DOI Creative Commons
Gabriel Ioan Arcas, Tudor Cioara, Ionuț Anghel

et al.

Smart Cities, Journal Year: 2024, Volume and Issue: 7(1), P. 680 - 711

Published: Feb. 19, 2024

The management of decentralized energy resources and smart grids needs novel data-driven low-latency applications services to improve resilience responsiveness ensure closer real-time control. However, the large-scale integration Internet Things (IoT) devices has led generation significant amounts data at edge grid, posing challenges for traditional cloud-based smart-grid architectures meet stringent latency response time requirements emerging applications. In this paper, we delve into grid computational distribution architectures, including edge–fog–cloud models, orchestration, frameworks support design offloading across continuum. Key factors influencing process, such as network performance, Artificial Intelligence (AI) processes, requirements, application-specific factors, efficiency, are analyzed considering operational requirements. We conduct a comprehensive overview current research landscape decision-making regarding strategies from cloud fog or edge. focus is on metaheuristics identifying near-optimal solutions reinforcement learning adaptively optimizing process. A macro perspective determining when what offload in provided next-generation AI applications, offering an features trade-offs selecting between federated solutions. Finally, work contributes understanding grids, providing Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis cost–benefit strategies.

Language: Английский

Citations

8
A new approach to reduce energy consumption in priority live migration of services based on green cloud computing DOI
Gang Wang, Bin Wen, Jianhua He

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 22, 2025

Language: Английский

Citations

1
Advancing food security through drone-based hyperspectral imaging: applications in precision agriculture and post-harvest management DOI

Debashish Kar,

Sambandh Bhusan Dhal

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(3)

Published: Feb. 13, 2025

Language: Английский

Citations

1
Economic and Environmental Costs of Cloud Technologies for Medical Imaging and Radiology Artificial Intelligence DOI
Florence X. Doo, Pranav Kulkarni, Eliot L. Siegel

et al.

Journal of the American College of Radiology, Journal Year: 2023, Volume and Issue: 21(2), P. 248 - 256

Published: Dec. 9, 2023

Language: Английский

Citations

22
Energy efficient task scheduling based on deep reinforcement learning in cloud environment: A specialized review DOI
Huanhuan Hou, Siti Nuraishah Agos Jawaddi,

Azlan Ismail

et al.

Future Generation Computer Systems, Journal Year: 2023, Volume and Issue: 151, P. 214 - 231

Published: Oct. 14, 2023

Language: Английский

Citations

17