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<p>In the rapidly advancing edge-cloud continuum computing, efficient task scheduling is crucial for optimizing the performance of large-scale and latency-sensitive applications. However, existing metaheuristic techniques often struggle with slow convergence, high computational complexity, and an imbalance between local and global searches, which limit their scalability and real-time adaptability. To address these challenges, this study aims to develop an efficient task scheduling algorithm, the Adaptive Hybrid Enhanced Flower Pollination-Based Simulated Annealing (AHEFPA-SA) algorithm. The AHEFPA-SA algorithm integrates simulated annealing with adaptive mechanisms, employing a chaotic cycle map to improve initial solutions and adaptive inertia weight to control levy flight distance, thereby reducing computational complexity and accelerating convergence. Implemented within the EdgeCloudSim simulator, the algorithm is evaluated against benchmark algorithms across key performance metrics including makespan, execution cost, and resource utilization, the proposed algorithm demonstrates significant improvements. The results show up to 22% reduction in makespan on edge servers and 20% on cloud servers, along with a 16% reduction in execution cost and a 13% improvement in resource utilization. These findings highlight the adaptability and real-time efficiency of the AHEFPA-SA algorithm, making it well-suited for dynamic, resource-constrained edge-cloud environments.</p>

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Adaptive hybrid enhanced flower pollination-based simulated annealing scheme for task scheduling in edge-cloud continuum

  • 期刊论文

  • 发表日期：

    2025年3月8日

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  • Nasiru Muhammad Dankolo,
  • Nor Haizan Mohamed Radzi,
  • Noorfa Haszlinna Mustaffa,
  • Danlami Gabi

摘要

In the rapidly advancing edge-cloud continuum computing, efficient task scheduling is crucial for optimizing the performance of large-scale and latency-sensitive applications. However, existing metaheuristic techniques often struggle with slow convergence, high computational complexity, and an imbalance between local and global searches, which limit their scalability and real-time adaptability. To address these challenges, this study aims to develop an efficient task scheduling algorithm, the Adaptive Hybrid Enhanced Flower Pollination-Based Simulated Annealing (AHEFPA-SA) algorithm. The AHEFPA-SA algorithm integrates simulated annealing with adaptive mechanisms, employing a chaotic cycle map to improve initial solutions and adaptive inertia weight to control levy flight distance, thereby reducing computational complexity and accelerating convergence. Implemented within the EdgeCloudSim simulator, the algorithm is evaluated against benchmark algorithms across key performance metrics including makespan, execution cost, and resource utilization, the proposed algorithm demonstrates significant improvements. The results show up to 22% reduction in makespan on edge servers and 20% on cloud servers, along with a 16% reduction in execution cost and a 13% improvement in resource utilization. These findings highlight the adaptability and real-time efficiency of the AHEFPA-SA algorithm, making it well-suited for dynamic, resource-constrained edge-cloud environments.