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An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization.

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  • 1School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, India.

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Summary
This summary is machine-generated.

This study introduces an efficient cloud task scheduling algorithm using firefly optimization. It improves task-to-virtual machine assignment, enhancing service quality and user trust in cloud providers.

Keywords:
ACO—ant colony optimizationGA—genetic algorithmPSO—particle swarm optimizationSLA—service level agreementavailabilitymakespansuccess ratetask schedulingtrustturnaround efficiency

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Artificial Intelligence

Background:

  • Dynamic and heterogeneous workloads in cloud computing present significant task scheduling challenges.
  • Inappropriate task-to-resource assignment degrades service quality, violates Service Level Agreements (SLAs), and erodes user trust.

Purpose of the Study:

  • To propose an efficient task scheduling algorithm for cloud computing environments.
  • To enhance the accuracy of task assignment to virtual machines (VMs) by considering task and VM priorities.

Main Methods:

  • Developed a novel task scheduling algorithm modeled using firefly optimization.
  • Utilized fabricated datasets and real-world worklogs (HPC2N, NASA) for workload simulation.
  • Implemented and evaluated the algorithm in the Cloudsim simulation environment.

Main Results:

  • The proposed firefly optimization-based algorithm significantly outperformed baseline approaches (ACO, PSO, GA).
  • Demonstrated improvements in key performance metrics including makespan, availability, success rate, and turnaround efficiency.

Conclusions:

  • The proposed efficient task scheduling algorithm effectively addresses challenges in dynamic cloud environments.
  • Prioritizing tasks and VMs leads to more accurate scheduling, improved service quality, and increased user trust in cloud providers.