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<p>Fog computing is an emerging paradigm that brings computation closer to data sources, reducing latency, enhancing data privacy, and conserving network bandwidth. However, it also introduces challenges related to energy efficiency due to the increased frequency of local processing. This study conducts a comprehensive survey of task-scheduling algorithms focused on optimizing energy usage within this paradigm. We review 51 research articles published over the past six years, categorizing the algorithms into heuristic, meta-heuristic, AI-driven, and hybrid approaches. Each algorithm is critically evaluated based on its objectives, strategies, and energy management capabilities, with a focus on key performance metrics such as energy consumption, computational latency, task completion time, and quality of service. Furthermore, this study highlights potential research gaps and suggests future directions for improving energy efficiency in fog computing. This survey offers valuable guidance to researchers designing and implementing energy-efficient task scheduling algorithms within fog computing environments by synthesizing these insights and presenting practical recommendations.</p>

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Energizing the fog: a systematic survey on task scheduling strategies for energy optimization

  • 期刊论文

  • 发表日期：

    2025年6月16日

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  • Ganesan Nagabushnam,
  • Yundo Choi,
  • Kyong Hoon Kim

摘要

Fog computing is an emerging paradigm that brings computation closer to data sources, reducing latency, enhancing data privacy, and conserving network bandwidth. However, it also introduces challenges related to energy efficiency due to the increased frequency of local processing. This study conducts a comprehensive survey of task-scheduling algorithms focused on optimizing energy usage within this paradigm. We review 51 research articles published over the past six years, categorizing the algorithms into heuristic, meta-heuristic, AI-driven, and hybrid approaches. Each algorithm is critically evaluated based on its objectives, strategies, and energy management capabilities, with a focus on key performance metrics such as energy consumption, computational latency, task completion time, and quality of service. Furthermore, this study highlights potential research gaps and suggests future directions for improving energy efficiency in fog computing. This survey offers valuable guidance to researchers designing and implementing energy-efficient task scheduling algorithms within fog computing environments by synthesizing these insights and presenting practical recommendations.