Glowworm swarm optimisation based task scheduling for cloud computing

Dabiah Ahmed Alboaneen; Huaglory Tianfield; Yan Zhang

doi:10.1145/3018896.3036395

Glowworm swarm optimisation based task scheduling for cloud computing

Dabiah Ahmed Alboaneen, Huaglory Tianfield, Yan Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Task scheduling is a non-deterministic polynomial-time hard (NP-hard) optimisation problem, thus applying metaheuristics is important. In this paper, we employ glowworm swarm optimisation (GSO) to solve the task scheduling problem in cloud computing to minimise the total execution cost of tasks while keeping the total completion time within the deadline. Simulation results show that GSO based task scheduling (GSOTS) algorithm outperforms shortest task first (STF), largest task first (LTF) and particle swarm optimisation (PSO) algorithms in reducing the total completion time and the cost of executing tasks.

Original language	English
Title of host publication	Proceedings of the 2nd International Conference on Internet of things and Cloud Computing
Publisher	ACM
Number of pages	7
ISBN (Print)	9781450347747
DOIs	https://doi.org/10.1145/3018896.3036395
Publication status	Published - 22 Mar 2017

Keywords

cloud computing
resource management
glowworm swarm optimisation (GSO)
task scheduling

Documents and Links

10.1145/3018896.3036395

Alboaneen, D.A. et al (2017) Glowworm swarm optimisation based task scheduling for cloud computing
© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ICC '17 Proceedings of the Second International Conference on Internet of things and Cloud Computing Article No. 152. Available at: http://doi.acm.org/10.1145/3018896.3036395
Author accepted manuscript, 304 KB

Cite this

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title = "Glowworm swarm optimisation based task scheduling for cloud computing",

abstract = "Task scheduling is a non-deterministic polynomial-time hard (NP-hard) optimisation problem, thus applying metaheuristics is important. In this paper, we employ glowworm swarm optimisation (GSO) to solve the task scheduling problem in cloud computing to minimise the total execution cost of tasks while keeping the total completion time within the deadline. Simulation results show that GSO based task scheduling (GSOTS) algorithm outperforms shortest task first (STF), largest task first (LTF) and particle swarm optimisation (PSO) algorithms in reducing the total completion time and the cost of executing tasks. ",

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AU - Alboaneen, Dabiah Ahmed

AU - Tianfield, Huaglory

AU - Zhang, Yan

N1 - Acceptance in SAN AAM: no embargo Pub date from proceedings webpage

PY - 2017/3/22

Y1 - 2017/3/22

N2 - Task scheduling is a non-deterministic polynomial-time hard (NP-hard) optimisation problem, thus applying metaheuristics is important. In this paper, we employ glowworm swarm optimisation (GSO) to solve the task scheduling problem in cloud computing to minimise the total execution cost of tasks while keeping the total completion time within the deadline. Simulation results show that GSO based task scheduling (GSOTS) algorithm outperforms shortest task first (STF), largest task first (LTF) and particle swarm optimisation (PSO) algorithms in reducing the total completion time and the cost of executing tasks.

AB - Task scheduling is a non-deterministic polynomial-time hard (NP-hard) optimisation problem, thus applying metaheuristics is important. In this paper, we employ glowworm swarm optimisation (GSO) to solve the task scheduling problem in cloud computing to minimise the total execution cost of tasks while keeping the total completion time within the deadline. Simulation results show that GSO based task scheduling (GSOTS) algorithm outperforms shortest task first (STF), largest task first (LTF) and particle swarm optimisation (PSO) algorithms in reducing the total completion time and the cost of executing tasks.

KW - cloud computing

KW - resource management

KW - glowworm swarm optimisation (GSO)

KW - task scheduling

U2 - 10.1145/3018896.3036395

DO - 10.1145/3018896.3036395

M3 - Conference contribution

SN - 9781450347747

BT - Proceedings of the 2nd International Conference on Internet of things and Cloud Computing

PB - ACM

ER -

Glowworm swarm optimisation based task scheduling for cloud computing

Abstract

Keywords

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