Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely exe...

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Bibliographic Details
Published in:PeerJ. Computer science Vol. 10; p. e2128
Main Author: Alsadie, Deafallah
Format: Journal Article
Language:English
Published: United States PeerJ. Ltd 17.06.2024
PeerJ Inc
Subjects:
Algorithms
Algorithms and Analysis of Algorithms
Cloud computing
Computer Networks and Communications
Distributed and Parallel Computing
Energy efficiency
Fog computing
Heuristic methods
Internet Of Things
IoT applications
Machine learning
Optimization
Task scheduling
Technology application
Fog computing
Task scheduling
Cloud computing
IoT applications
Heuristic methods
Optimization
ISSN:2376-5992, 2376-5992
Online Access:Get full text
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Summary:Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.2128