Alsadie Deafallah
Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Makkah Almukaramah, Saudi Arabia.
PeerJ Comput Sci. 2024 Jun 17;10:e2128. doi: 10.7717/peerj-cs.2128. eCollection 2024.
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.
雾计算已成为一种有前景的范式，用于满足物联网应用的计算需求，将云计算的能力扩展到网络边缘。任务调度对于提高雾计算环境中的能源效率、优化资源利用以及确保任务的及时执行至关重要。本文全面综述了雾计算系统任务调度方法的进展，涵盖基于优先级、贪婪启发式、元启发式、基于学习、混合启发式以及自然启发式方法。通过对相关文献的系统分析，我们突出了每种方法的优点和局限性，并确定了雾计算任务调度面临的关键挑战，包括动态环境、异构性、可扩展性、资源约束、安全问题以及算法透明度。此外，我们提出了应对这些挑战的未来研究方向，包括集成机器学习技术进行实时自适应、利用联邦学习进行协同调度、开发资源感知和节能算法、纳入安全感知技术以及推进可解释人工智能方法。通过应对这些挑战并遵循这些研究方向，我们旨在促进为雾计算环境开发更强大、适应性更强且高效的任务调度解决方案，最终在雾计算系统中促进信任、安全和可持续性，并推动其在各种应用和领域中的广泛采用。
