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

Nasiru Muhammad Dankolo1, Nor Haizan Mohamed Radzi2, Noorfa Haszlinna Mustaffa2

  • 1Faculty of Computing, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia. muhammaddankolo@graduate.utm.my.

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This study introduces a hybrid Flower Pollination Algorithm and Tabu Search (FPA-TS) for optimizing Internet of Things (IoT) resource allocation. The FPA-TS algorithm effectively balances cost and task completion time in edge-cloud environments.

Keywords:
ContinuumEdge-cloudFlower pollinationInternet of ThingsOptimizationResource allocation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cloud Computing

Background:

  • Internet of Things (IoT) applications require efficient resource allocation across edge and cloud computing environments.
  • Optimizing resource allocation in dynamic edge-cloud systems is complex, balancing cost and task completion time (makespan).
  • Existing algorithms often provide suboptimal solutions for multi-objective optimization in IoT scenarios.

Purpose of the Study:

  • To introduce a novel hybrid Flower Pollination Algorithm and Tabu Search (FPA-TS) for multi-objective resource allocation in IoT.
  • To enhance the Flower Pollination Algorithm with adaptive probability and dynamic levy flight control for improved global search.
  • To leverage Tabu Search for memory-guided local refinement to minimize makespan and costs.

Main Methods:

  • Developed a hybrid FPA-TS algorithm integrating enhanced FPA features with Tabu Search.
  • Implemented adaptive probability based on solution diversity and dynamic levy flight control in FPA.
  • Utilized Tabu Search for local search and refinement to optimize makespan and cost.
  • Conducted extensive simulations using representative IoT scenarios.

Main Results:

  • The hybrid FPA-TS algorithm demonstrated superior performance compared to existing algorithms.
  • Achieved significant improvements in balancing cost and makespan for IoT resource allocation.
  • The enhanced FPA components contributed to effective global search capabilities.
  • Tabu Search effectively refined solutions for minimized makespan and costs.

Conclusions:

  • The hybrid FPA-TS offers a robust and efficient approach for resource allocation in IoT edge-cloud systems.
  • This method addresses the multi-objective optimization challenges inherent in dynamic IoT environments.
  • The proposed algorithm provides a promising solution for optimizing performance and cost in large-scale IoT deployments.