AI augmented Edge and Fog computing: trends and challenges
File(s)1-s2.0-S108480452300067X-main.pdf (2.7 MB)
Published version
Author(s)
Type
Journal Article
Abstract
In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The frontiers of these computing technologies have been boosted by shift from manually encoded algorithms to Artificial Intelligence (AI)-driven autonomous systems for optimum and reliable management of distributed computing resources. Prior work focuses on improving existing systems using AI across a wide range of domains, such as efficient resource provisioning, application deployment, task placement, and service management. This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality of Service (QoS) in the presence of a continuum of resources. We present the latest trends and impact areas such as optimizing AI models that are deployed on or for computing systems. We layout a roadmap for future research directions in areas such as resource management for QoS optimization and service reliability. Finally, we discuss blue-sky ideas and envision this work as an anchor point for future research on AI-driven computing systems.
Date Issued
2023-07
Date Acceptance
2023-04-13
Citation
Journal of Network and Computer Applications, 2023, 216, pp.1-28
ISSN
1084-8045
Publisher
Elsevier BV
Start Page
1
End Page
28
Journal / Book Title
Journal of Network and Computer Applications
Volume
216
Copyright Statement
© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.sciencedirect.com/science/article/pii/S108480452300067X
Publication Status
Published
Article Number
103648
Date Publish Online
2023-05-03