Home // CLOUD COMPUTING 2024, The Fifteenth International Conference on Cloud Computing, GRIDs, and Virtualization // View article
Authors:
Amir Pakmehr
Keywords: fog computing ; deep reinforcement learning ; task offloading ; cybersecurity.
Abstract:
The surge in Internet of Things (IoT) devices and data generation highlights the limitations of traditional cloud computing in meeting demands for immediacy, Quality of Service, and location-aware services. Fog computing emerges as a solution, bringing computation, storage, and networking closer to data sources. This study explores the role of Deep Reinforcement Learning in enhancing fog computing’s task offloading, aiming for operational efficiency and robust security. By reviewing current strategies and proposing future research directions, the paper shows the potential of Deep Reinforcement Learning in optimizing resource use, speeding up responses, and securing against vulnerabilities. It suggests advancing Deep Reinforcement Learning for fog computing, exploring blockchain for better security, and seeking energy-efficient models to improve the Internet of Things ecosystem. Incorporating artificial intelligence, Our results indicate potential improvements in key metrics, such as task completion time, energy consumption, and security incident reduction. These findings provide a concrete foundation for future research and practical applications in optimizing fog computing architectures.
Pages: 25 to 33
Copyright: Copyright (c) IARIA, 2024
Publication date: April 14, 2024
Published in: conference
ISSN: 2308-4294
ISBN: 978-1-68558-156-5
Location: Venice, Italy
Dates: from April 14, 2024 to April 18, 2024