Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

Next generation communication networks are expected to accommodate a high number of new and resource-voracious applications that can be offered to a large range of end users. Even though end devices are becoming more powerful, the available local resources cannot cope with the requirements of these...

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Bibliographic Details
Published in:Computer networks (Amsterdam, Netherlands : 1999) Vol. 195; p. 108177
Main Authors: Saeik, Firdose, Avgeris, Marios, Spatharakis, Dimitrios, Santi, Nina, Dechouniotis, Dimitrios, Violos, John, Leivadeas, Aris, Athanasopoulos, Nikolaos, Mitton, Nathalie, Papavassiliou, Symeon
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 04.08.2021
Elsevier Sequoia S.A
Elsevier
Subjects:
Application
Artificial intelligence
Cloud computing
Communication
Communication networks
Computation
Computation offloading
Computer Science
Control theory
Distributed, Parallel, and Cluster Computing
Edge Computing
End users
Infrastructure
Mathematical optimization
Networking and Internet Architecture
Optimization
Polls & surveys
Resource allocation
Task offloading
Ubiquitous Computing
Edge Computing
Control theory
Mathematical optimization
Artificial intelligence
Task offloading
Resource allocation
Control Theory
Mathematical Optimization
Task Offloading
Resource Allocation
Artificial Intelligence
ISSN:1389-1286, 1872-7069
Online Access:Get full text
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Summary:Next generation communication networks are expected to accommodate a high number of new and resource-voracious applications that can be offered to a large range of end users. Even though end devices are becoming more powerful, the available local resources cannot cope with the requirements of these applications. This has created a new challenge called task offloading, where computation intensive tasks need to be offloaded to more resource powerful remote devices. Naturally, the Cloud Computing is a well-tested infrastructure that can facilitate the task offloading. However, Cloud Computing as a centralized and distant infrastructure creates significant communication delays that cannot satisfy the requirements of the emerging delay-sensitive applications. To this end, the concept of Edge Computing has been proposed, where the Cloud Computing capabilities are repositioned closer to the end devices at the edge of the network. This paper provides a detailed survey of how the Edge and/or Cloud can be combined together to facilitate the task offloading problem. Particular emphasis is given on the mathematical, artificial intelligence and control theory optimization approaches that can be used to satisfy the various objectives, constraints and dynamic conditions of this end-to-end application execution approach. The survey concludes with identifying open challenges and future directions of the problem at hand.
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ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2021.108177