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Modified firefly algorithm for workflow scheduling in cloud-edge environment.

Nebojsa Bacanin1, Miodrag Zivkovic1, Timea Bezdan1

  • 1Singidunum University, Danijelova 32, Belgrade, 11000 Serbia.

Neural Computing & Applications
|February 7, 2022
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Summary
This summary is machine-generated.

This study introduces an enhanced firefly algorithm for efficient workflow scheduling in cloud-edge computing. The improved algorithm significantly reduces makespan and cost, outperforming existing methods.

Keywords:
Edge computingFirefly algorithmGenetic operatorQuasi-reflection-based learningSwarm intelligenceWorkflow scheduling

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Edge computing brings resources closer to end-users, reducing response times and network bandwidth usage.
  • Efficient workflow scheduling is crucial for optimizing cloud-edge environments.
  • Existing metaheuristics have limitations in addressing complex cloud-edge scheduling challenges.

Purpose of the Study:

  • To propose an enhanced firefly algorithm for workflow scheduling in cloud-edge environments.
  • To improve upon the deficiencies of the original firefly metaheuristic.
  • To optimize for dual objectives of cost and makespan.

Main Methods:

  • Developed an enhanced firefly algorithm incorporating genetic operators and quasi-reflection-based learning.
  • Validated the algorithm on 10 standard benchmark instances.
  • Performed simulations comparing the proposed approach with state-of-the-art metaheuristics.

Main Results:

  • The enhanced firefly algorithm demonstrated superior convergence speed and result quality compared to the original algorithm and other metaheuristics.
  • Simulations showed significant improvements in reducing both makespan and cost.
  • The proposed algorithm achieved prominent results in cloud-edge workflow scheduling.

Conclusions:

  • The enhanced firefly algorithm is effective for workflow scheduling in cloud-edge environments.
  • The approach offers significant advantages over existing methods in terms of efficiency and cost-effectiveness.
  • This work contributes to optimizing resource utilization and performance in distributed computing systems.