Karafan Journal

Karafan Journal

A task scheduling method based on differential evolutionary algorithm and particle community in order to increase efficiency in cloud computing environment

Document Type : Original Article

Authors
Golnaz Aghaee Ghazvini 1
zahra Heydaran Daroogheh Amnyieh 2
Babak Nikmard 1
1 Department of Computer Engineering, Dol.C., Islamic Azad University, Isfahan, Iran.
2 Department of Electrical Engineering, Dol.C., Islamic Azad University, Isfahan, Iran.
10.48301/kssa.2025.499484.3120
Abstract
Cloud computing is a new trend in Internet computing that provides various resources as a service. One of the basic challenges in the cloud environment is task scheduling in order to optimally use cloud-based services and increase efficiency, which makes the system in a stable condition. In recent years, extensive research has been done to assign tasks to virtual machines in cloud environments. Although these algorithms work effectively, they still do not accurately address some challenges such as communication delay, effective use of resources, reliability, and assignment of work to unusable machines. One of the significant challenges in the cloud computing environment is choosing the best virtual machine for each task with the aim of increasing efficiency and reducing costs. In this article, a solution called PSODE is presented to schedule tasks using evolutionary difference algorithms and particle community algorithm with the aim of increasing efficiency in the cloud computing environment. In order to evaluate, the proposed solution was compared with the difference evolutionary algorithm (DE) and the particle swarm intelligence algorithm (PSO). The evaluation results for 1000 tasks indicate a reduction in response time compared to the mentioned methods.
Keywords
loud computing
task scheduling
increasing efficiency
differential evolutionary algorithm
particle community algorithm
Subjects
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Karafan Journal
Volume 23, Issue 1
Technical and Engineering
Spring 2026
Pages 516-541

  • Receive Date 13 January 2025
  • Revise Date 12 March 2025
  • Accept Date 14 December 2025