Efficient Task Scheduling in Cloud Computing using an Improved Particle Swarm Optimization Algorithm

Guang Peng, Katinka Wolter

Abstract

An improved multi-objective discrete particle swarm optimization (IMODPSO) algorithm is proposed to solve the task scheduling and resource allocation problem for scientific workflows in cloud computing. First, we use a strategy to limit the velocity of particles and adopt a discrete position updating equation to solve the multi-objective time and cost optimization model. Second, we adopt a Gaussian mutation operation to update the personal best position and the external archive, which can retain the diversity and convergence accuracy of Pareto optimal solutions. Finally, the computational complexity of IMODPSO is compared with three other state-of-the-art algorithms. We validate the computational speed, the number of solutions found and the generational distance of IMODPSO and find that the new algorithm outperforms the three other algorithms with respect to all three metrics.

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Paper Citation


in Harvard Style

Peng G. and Wolter K. (2019). Efficient Task Scheduling in Cloud Computing using an Improved Particle Swarm Optimization Algorithm.In Proceedings of the 9th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-365-0, pages 58-67. DOI: 10.5220/0007674400580067


in Bibtex Style

@conference{closer19,
author={Guang Peng and Katinka Wolter},
title={Efficient Task Scheduling in Cloud Computing using an Improved Particle Swarm Optimization Algorithm},
booktitle={Proceedings of the 9th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2019},
pages={58-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007674400580067},
isbn={978-989-758-365-0},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Efficient Task Scheduling in Cloud Computing using an Improved Particle Swarm Optimization Algorithm
SN - 978-989-758-365-0
AU - Peng G.
AU - Wolter K.
PY - 2019
SP - 58
EP - 67
DO - 10.5220/0007674400580067