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<p>Fog computing extends cloud services to the network edge, enabling low-latency processing for time-sensitive applications. However, scheduling complexity significantly increases due to heterogeneous resources, dynamic workloads, and strict Quality-of-Service (QoS) constraints. Although numerous scheduling techniques have been proposed, existing studies often assess only a narrow subset of algorithms or rely on offline metaheuristics unsuitable for real-time environments. This paper presents a comprehensive comparative evaluation of twelve scheduling algorithms, including five classical, one heuristic, and six metaheuristic-inspired approaches, within a realistic 20-node multi-tier fog-cloud topology. Across 1,365 experiments spanning seven utilization levels, we analyze each algorithm’s deadline adherence, load distribution, and scheduling efficiency. We adapt RT-MOMFO, a real-time variant of Multi-Objective Moth-Flame Optimization (Salehnia et al., 2023), for online fog scheduling through parameter reduction (population=20, iterations=8), ready queue management, and periodic tier preference cycling. This engineering adaptation achieves an 8.1% deadline miss ratio at U=0.8 with near-perfect load balance (0.988), ranking second overall. We also develop GMO (Greedy Multi-Objective), a lightweight greedy scheduler that eliminates population-based mechanisms while retaining load-aware routing and periodic tier cycling. GMO achieves a 9.4% DMR at U=0.8 with 47 times faster decisions (2 ms vs. 94 ms), demonstrating that speed advantages can offset algorithmic simplicity. The results establish a clear performance hierarchy, highlight the importance of explicit fog-cloud tier management, and show that simple greedy approaches remain competitive when perfect load balance is prioritized. Statistical analysis confirms top-tier algorithms (GAMMR, PSG-M, RT-MOMFO) are statistically indistinguishable at critical utilizations, with comprehensive ablation studies validating each design choice. This study provides one of the most extensive evaluations to date with rigorous statistical validation.</p>

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Comparative analysis of task scheduling in multi-tier fog-cloud computing: from classical approaches to greedy multi-objective optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月24日

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  • Zafril Rizal M Azmi,
  • Najmul Haque,
  • Saydul Akbar Murad

摘要

Fog computing extends cloud services to the network edge, enabling low-latency processing for time-sensitive applications. However, scheduling complexity significantly increases due to heterogeneous resources, dynamic workloads, and strict Quality-of-Service (QoS) constraints. Although numerous scheduling techniques have been proposed, existing studies often assess only a narrow subset of algorithms or rely on offline metaheuristics unsuitable for real-time environments. This paper presents a comprehensive comparative evaluation of twelve scheduling algorithms, including five classical, one heuristic, and six metaheuristic-inspired approaches, within a realistic 20-node multi-tier fog-cloud topology. Across 1,365 experiments spanning seven utilization levels, we analyze each algorithm’s deadline adherence, load distribution, and scheduling efficiency. We adapt RT-MOMFO, a real-time variant of Multi-Objective Moth-Flame Optimization (Salehnia et al., 2023), for online fog scheduling through parameter reduction (population=20, iterations=8), ready queue management, and periodic tier preference cycling. This engineering adaptation achieves an 8.1% deadline miss ratio at U=0.8 with near-perfect load balance (0.988), ranking second overall. We also develop GMO (Greedy Multi-Objective), a lightweight greedy scheduler that eliminates population-based mechanisms while retaining load-aware routing and periodic tier cycling. GMO achieves a 9.4% DMR at U=0.8 with 47 times faster decisions (2 ms vs. 94 ms), demonstrating that speed advantages can offset algorithmic simplicity. The results establish a clear performance hierarchy, highlight the importance of explicit fog-cloud tier management, and show that simple greedy approaches remain competitive when perfect load balance is prioritized. Statistical analysis confirms top-tier algorithms (GAMMR, PSG-M, RT-MOMFO) are statistically indistinguishable at critical utilizations, with comprehensive ablation studies validating each design choice. This study provides one of the most extensive evaluations to date with rigorous statistical validation.