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Hyper-Angle Exploitative Searching for Enabling Multi-Objective Optimization of Fog Computing.

Taj-Aldeen Naser Abdali1, Rosilah Hassan1, Azana Hafizah Mohd Aman1

  • 1Centre for Cyber Security, Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.

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Summary
This summary is machine-generated.

Fog computing optimizes resource allocation using the Hyper Angle Exploitative Searching (HAES) algorithm. This evolutionary approach enhances multi-objective optimization for efficient, stable, and cost-effective computational services.

Keywords:
crowding distanceevolutionary geneticsfog computinghyper-anglemulti-objective optimizationtask allocation

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Area of Science:

  • Computer Science
  • Distributed Computing
  • Optimization Algorithms

Background:

  • Fog computing enables wireless networks to provide user-specific computational services.
  • Optimal allocation of fog nodes is crucial for efficient, stable, and cost-effective computation.
  • Existing methods require enhanced multi-objective optimization strategies.

Purpose of the Study:

  • To develop a multi-objective optimization algorithm for fog computing.
  • To enhance the efficiency, stability, and cost-effectiveness of fog node allocation.
  • To introduce the Fog Computing Closed Loop (FCCL) model.

Main Methods:

  • Developed a novel evolutionary genetic algorithm: Hyper Angle Exploitative Searching (HAES).
  • HAES utilizes hyper angle and crowding distance for solution prioritization.
  • Evaluated HAES on multi-objective mathematical problems and compared with benchmark approaches.

Main Results:

  • HAES demonstrated superiority over benchmark approaches in multi-objective optimization.
  • The Fog Computing Closed Loop (FCCL) model was proposed and validated.
  • HAES achieved over 70% confidence in rejecting non-superiority based on domination metric set coverage.

Conclusions:

  • The HAES algorithm effectively optimizes multiple objectives in fog computing.
  • HAES provides a robust framework for efficient and stable fog node allocation.
  • This research advances multi-criteria optimization within fog computing environments.