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<p>The rise of Internet of Things (IoT) applications has heightened the need for efficient resource allocation across the edge-cloud continuum, combining low-latency edge nodes with high powered cloud servers. Optimizing this resource allocation is challenging, especially when balancing cost and task completion time (makespan). Traditional algorithms often fall short in these dynamic environments, leading to suboptimal solutions. This paper introduces a hybrid Flower Pollination Algorithm and Tabu Search (FPA–TS) algorithm to address IoT-specific requirements for multi objective optimization. The enhanced FPA incorporates adaptive probability based on solution diversity and dynamic levy flight control for effective global search, while Tabu Search contributes memory guided local refinement to further minimize makespan and costs. Extensive simulations with IoT scenarios show that the hybrid FPA–TS outperforms existing algorithms, offering a robust approach to optimizing resource allocation for IoT-driven applications in the edge-cloud continuum.</p>

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Optimizing resource allocation for IoT applications in the edge cloud continuum using hybrid metaheuristic algorithms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月25日

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  • Nasiru Muhammad Dankolo,
  • Nor Haizan Mohamed Radzi,
  • Noorfa Haszlinna Mustaffa,
  • Noreen Izza Arshad,
  • Maged Nasser,
  • Danlami Gabi,
  • Muhammed Nura Yusuf

摘要

The rise of Internet of Things (IoT) applications has heightened the need for efficient resource allocation across the edge-cloud continuum, combining low-latency edge nodes with high powered cloud servers. Optimizing this resource allocation is challenging, especially when balancing cost and task completion time (makespan). Traditional algorithms often fall short in these dynamic environments, leading to suboptimal solutions. This paper introduces a hybrid Flower Pollination Algorithm and Tabu Search (FPA–TS) algorithm to address IoT-specific requirements for multi objective optimization. The enhanced FPA incorporates adaptive probability based on solution diversity and dynamic levy flight control for effective global search, while Tabu Search contributes memory guided local refinement to further minimize makespan and costs. Extensive simulations with IoT scenarios show that the hybrid FPA–TS outperforms existing algorithms, offering a robust approach to optimizing resource allocation for IoT-driven applications in the edge-cloud continuum.