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<p>The proliferation of IoT devices has exerted significant demand on computing systems to process data rapidly, efficiently, and in proximity to its source. Conventional cloud-based methods frequently fail because of elevated latency and centralized constraints. Fog computing has emerged as a viable option by decentralizing computation to the edge; yet, successfully scheduling work in these dynamic and heterogeneous contexts continues to pose a significant difficulty. This research presents A Neuro-Fuzzy Multi-Objective Reinforcement Learning (NF-MORL), an innovative framework that integrates neuro-fuzzy systems with multi-objective reinforcement learning to tackle task scheduling in fog networks. The concept is straightforward yet impactful: a Takagi–Sugeno fuzzy layer addresses uncertainty and offers interpretable priorities, while a multi-objective actor–critic agent acquires the capacity to reconcile conflicting objectives makespan, energy consumption, cost, and reliability through practical experience. We assessed NF-MORL using empirical data from Google Cluster and EdgeBench. The findings were promising: relative to cutting-edge techniques, our methodology decreased makespan by up to 35%, enhanced energy efficiency by about 30%, reduced operational expenses by up to 40%, and augmented fault tolerance by as much as 37%. These enhancements persisted across various workload sizes, demonstrating that NF-MORL can effectively adjust to fluctuating situations. Our research indicates that integrating human-like reasoning through fuzzy logic with autonomous learning via reinforcement learning can yield more effective and resilient schedulers for actual fog deployments.</p>

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NF-MORL: a neuro-fuzzy multi-objective reinforcement learning framework for task scheduling in fog computing environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月26日

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  • Xiaomo Yu,
  • Ling Tang,
  • Jie Mi,
  • Long Long,
  • Xiao Qin,
  • Xiuming Li,
  • Qinglian Mo

摘要

The proliferation of IoT devices has exerted significant demand on computing systems to process data rapidly, efficiently, and in proximity to its source. Conventional cloud-based methods frequently fail because of elevated latency and centralized constraints. Fog computing has emerged as a viable option by decentralizing computation to the edge; yet, successfully scheduling work in these dynamic and heterogeneous contexts continues to pose a significant difficulty. This research presents A Neuro-Fuzzy Multi-Objective Reinforcement Learning (NF-MORL), an innovative framework that integrates neuro-fuzzy systems with multi-objective reinforcement learning to tackle task scheduling in fog networks. The concept is straightforward yet impactful: a Takagi–Sugeno fuzzy layer addresses uncertainty and offers interpretable priorities, while a multi-objective actor–critic agent acquires the capacity to reconcile conflicting objectives makespan, energy consumption, cost, and reliability through practical experience. We assessed NF-MORL using empirical data from Google Cluster and EdgeBench. The findings were promising: relative to cutting-edge techniques, our methodology decreased makespan by up to 35%, enhanced energy efficiency by about 30%, reduced operational expenses by up to 40%, and augmented fault tolerance by as much as 37%. These enhancements persisted across various workload sizes, demonstrating that NF-MORL can effectively adjust to fluctuating situations. Our research indicates that integrating human-like reasoning through fuzzy logic with autonomous learning via reinforcement learning can yield more effective and resilient schedulers for actual fog deployments.