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Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments.

Deafallah Alsadie1, Musleh Alsulami2

  • 1Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, 21961, Saudi Arabia. dbsadie@uqu.edu.sa.

Scientific Reports
|October 21, 2024
PubMed
Summary
This summary is machine-generated.

The Modified Firefly Optimization Algorithm (ModFOA) efficiently schedules scientific workflows in hybrid cloud-edge environments, outperforming other methods in reducing completion times and optimizing resource use.

Keywords:
Cloud computingFirefly Optimization AlgorithmResource utilizationScheduling algorithmsScientific workflows

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Artificial Intelligence

Background:

  • Scientific workflows require efficient scheduling in hybrid cloud-edge environments for optimal resource utilization and minimal completion times.
  • Traditional scheduling algorithms struggle with the complexities of scientific workflows and the unique demands of hybrid cloud-edge architectures.
  • Existing methods like Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) show promise but may not fully address these challenges.

Purpose of the Study:

  • To evaluate and compare the performance of various scheduling algorithms for scientific workflows in hybrid cloud-edge environments.
  • To introduce and assess the Modified Firefly Optimization Algorithm (ModFOA) as a solution for optimizing workflow scheduling in these integrated systems.
  • To analyze key performance metrics including makespan, resource utilization, and energy consumption.

Main Methods:

  • Comparative analysis of scheduling algorithms: Modified Firefly Optimization Algorithm (ModFOA), Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO).
  • Evaluation of performance metrics: makespan, resource utilization, and energy consumption.
  • Testing across both cloud and edge computing configurations within a hybrid environment.

Main Results:

  • ModFOA demonstrated superior performance in reducing makespan and overall completion times compared to ACO, GA, and PSO.
  • The algorithm maintained competitive levels of resource utilization and energy efficiency.
  • ModFOA effectively integrates cloud and edge computing resources for enhanced workflow scheduling.

Conclusions:

  • The Modified Firefly Optimization Algorithm (ModFOA) offers an effective solution for scheduling scientific workflows in hybrid cloud-edge environments.
  • ModFOA enhances workflow efficiency and resource management by effectively integrating cloud and edge resources.
  • Future research should focus on parameter refinement and practical validation of ModFOA in real-world hybrid cloud-edge scenarios.