Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing

Cloud computing environment provides several on-demand services and resource sharing for clients. Business processes are managed using the workflow technology over the cloud, which represents one of the challenges in using the resources in an efficient manner due to the dependencies between the task...

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Main Authors: Ahmad M. Manasrah, Hanan Ba Ali
Format: Article
Language:English
Published: Hindawi-Wiley 2018-01-01
Series:Wireless Communications and Mobile Computing
Online Access:http://dx.doi.org/10.1155/2018/1934784
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spelling doaj-880ed471176e4a25812accffaa4468272020-11-25T01:55:59ZengHindawi-WileyWireless Communications and Mobile Computing1530-86691530-86772018-01-01201810.1155/2018/19347841934784Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud ComputingAhmad M. Manasrah0Hanan Ba Ali1Network and Information Security Department, Yarmouk University, Irbid 21163, JordanComputer Sciences Department, Yarmouk University, Irbid 21163, JordanCloud computing environment provides several on-demand services and resource sharing for clients. Business processes are managed using the workflow technology over the cloud, which represents one of the challenges in using the resources in an efficient manner due to the dependencies between the tasks. In this paper, a Hybrid GA-PSO algorithm is proposed to allocate tasks to the resources efficiently. The Hybrid GA-PSO algorithm aims to reduce the makespan and the cost and balance the load of the dependent tasks over the heterogonous resources in cloud computing environments. The experiment results show that the GA-PSO algorithm decreases the total execution time of the workflow tasks, in comparison with GA, PSO, HSGA, WSGA, and MTCT algorithms. Furthermore, it reduces the execution cost. In addition, it improves the load balancing of the workflow application over the available resources. Finally, the obtained results also proved that the proposed algorithm converges to optimal solutions faster and with higher quality compared to other algorithms.http://dx.doi.org/10.1155/2018/1934784
collection DOAJ
language English
format Article
sources DOAJ
author Ahmad M. Manasrah
Hanan Ba Ali
spellingShingle Ahmad M. Manasrah
Hanan Ba Ali
Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing
Wireless Communications and Mobile Computing
author_facet Ahmad M. Manasrah
Hanan Ba Ali
author_sort Ahmad M. Manasrah
title Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing
title_short Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing
title_full Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing
title_fullStr Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing
title_full_unstemmed Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing
title_sort workflow scheduling using hybrid ga-pso algorithm in cloud computing
publisher Hindawi-Wiley
series Wireless Communications and Mobile Computing
issn 1530-8669
1530-8677
publishDate 2018-01-01
description Cloud computing environment provides several on-demand services and resource sharing for clients. Business processes are managed using the workflow technology over the cloud, which represents one of the challenges in using the resources in an efficient manner due to the dependencies between the tasks. In this paper, a Hybrid GA-PSO algorithm is proposed to allocate tasks to the resources efficiently. The Hybrid GA-PSO algorithm aims to reduce the makespan and the cost and balance the load of the dependent tasks over the heterogonous resources in cloud computing environments. The experiment results show that the GA-PSO algorithm decreases the total execution time of the workflow tasks, in comparison with GA, PSO, HSGA, WSGA, and MTCT algorithms. Furthermore, it reduces the execution cost. In addition, it improves the load balancing of the workflow application over the available resources. Finally, the obtained results also proved that the proposed algorithm converges to optimal solutions faster and with higher quality compared to other algorithms.
url http://dx.doi.org/10.1155/2018/1934784
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