Task Scheduling Optimisation for Cloud Computing using a New PSO-initialisation

Amine Chraibi, Said Ben Alla, Abdellah Ezzati

2021

Abstract

Task scheduling optimisation in cloud computing was regarded as one of the most notable challenges with possible alternatives. As most optimisation algorithms in task scheduling required substantial performance period and assessment value calculation, this study emphasised task scheduling optimisation (specifically makespan optimisation). Specifically, a new artificial intelligence method and meta-heuristic Particle Swarm Optimisation (PSO) initialisation (HI-PSO) were suggested. Following a cloud simulator (CloudSim), the experimental outcomes indicated that the recommended HI-PSO could attain improved outcomes involving Makespan compared to implementation.

Paper Citation

in Harvard Style

Chraibi A., Ben Alla S. and Ezzati A. (2021). Task Scheduling Optimisation for Cloud Computing using a New PSO-initialisation. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 5-9

in Bibtex Style

@conference{bml21,
author={Amine Chraibi and Said Ben Alla and Abdellah Ezzati},
title={Task Scheduling Optimisation for Cloud Computing using a New PSO-initialisation},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={5-9},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Task Scheduling Optimisation for Cloud Computing using a New PSO-initialisation
SN - 978-989-758-559-3
AU - Chraibi A.
AU - Ben Alla S.
AU - Ezzati A.
PY - 2021
SP - 5
EP - 9
DO -