Article: An optimised AI-driven swarm-based enhanced task scheduling model for cloud computing environment Journal: International Journal of Cloud Computing (IJCC) 2025 Vol.14 No.1 pp.25 - 53 Abstract: Task scheduling in cloud computing environment becomes difficult when complexity level of dispute, including task count and computing resources, rises with user growth. Solving this, an enhanced task scheduling (ETS) model with optimised artificial intelligence driven by swarm is proposed in paper. In proposed method, supervised machine learning algorithm, artificial neural networks (ANN) with swarm-based moth flame optimisation (MFO) is used to balance scheduling. MFO optimises by separating out virtual machines (VMs) considering basic properties like CPU utilisation, memory and bandwidth. ETS model is optimised based on resource allocation and balancing issues using back-propagation algorithm (BPA) with ANN (ANN-BPA) to analyse scheduling and problem identification mechanism. Efficiency of ETS model is assessed, focusing on aspects such as task allocation, task completion, execution time and energy consumption. The ANN-BPA-based task scheduling model outperforms by present technique and ANN-based model, which enhances resource utilisation by 7.54% and decreases completion time by 0.6 s. Inderscience Publishers - linking academia, business and industry through research

Title: An optimised AI-driven swarm-based enhanced task scheduling model for cloud computing environment

Authors: Surinder Kaur; Jaspreet Singh; Vishal Bharti

Addresses: Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India ' Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India ' MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana, India

Abstract: Task scheduling in cloud computing environment becomes difficult when complexity level of dispute, including task count and computing resources, rises with user growth. Solving this, an enhanced task scheduling (ETS) model with optimised artificial intelligence driven by swarm is proposed in paper. In proposed method, supervised machine learning algorithm, artificial neural networks (ANN) with swarm-based moth flame optimisation (MFO) is used to balance scheduling. MFO optimises by separating out virtual machines (VMs) considering basic properties like CPU utilisation, memory and bandwidth. ETS model is optimised based on resource allocation and balancing issues using back-propagation algorithm (BPA) with ANN (ANN-BPA) to analyse scheduling and problem identification mechanism. Efficiency of ETS model is assessed, focusing on aspects such as task allocation, task completion, execution time and energy consumption. The ANN-BPA-based task scheduling model outperforms by present technique and ANN-based model, which enhances resource utilisation by 7.54% and decreases completion time by 0.6 s.

Keywords: cloud computing; resource allocation; task scheduling; ANN-BPA; particle swarm optimisation; PSO; artificial bee colony; ABC; CSA; moth flame optimisation; MFO.

DOI: 10.1504/IJCC.2025.145660

International Journal of Cloud Computing, 2025 Vol.14 No.1, pp.25 - 53

Received: 13 Jul 2024
Accepted: 20 Dec 2024
Published online: 11 Apr 2025 *

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Title: An optimised AI-driven swarm-based enhanced task scheduling model for cloud computing environment

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