A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach

Cloud computing slowly gained an important role in scientific application, on-demand facility of virtualized resources is provided as a service with the help of virtualization without any additional waiting time. Energy consumption is reduced for job scheduling problems based on makespan constraint...

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Main Authors: N. Moganarangan, R.G. Babukarthik, S. Bhuvaneswari, M.S. Saleem Basha, P. Dhavachelvan
Format: Article
Language:English
Published: Elsevier 2016-01-01
Series:Journal of King Saud University: Computer and Information Sciences
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1319157815000816
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author N. Moganarangan
R.G. Babukarthik
S. Bhuvaneswari
M.S. Saleem Basha
P. Dhavachelvan
author_facet N. Moganarangan
R.G. Babukarthik
S. Bhuvaneswari
M.S. Saleem Basha
P. Dhavachelvan
author_sort N. Moganarangan
collection DOAJ
description Cloud computing slowly gained an important role in scientific application, on-demand facility of virtualized resources is provided as a service with the help of virtualization without any additional waiting time. Energy consumption is reduced for job scheduling problems based on makespan constraint which in turn leads to significant decrease in the energy cost. Additionally, there is an increase in complexity for scheduling problems mainly because the application is not based on makespan constraint. In this paper we propose a new Hybrid algorithm combining the benefits of ACO and cuckoo search algorithm. It is focused on the voltage scaling factor for reduction of energy consumption. Performance of the Hybrid algorithm is considerably increased from 45 tasks onward when compared to ACO. Energy consumed by Hybrid algorithm is measured and energy improvement is evaluated up to 35 tasks. Energy consumption is the same as ACO algorithm because as the number of tasks increases (45 to 70) there is a considerable decrease in the energy consumption rate. Makespan of Hybrid algorithm based on number of tasks is compared with ACO algorithm. Further we have analyzed the energy consumption for a number of processors and its improvement rate – up to 6 processors, energy consumption is considerably reduced and the energy consumption tends to be in steady state with further increase in the number of processors.
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spelling doaj.art-e4fc1ff2b6d140b89a261e35f530baec2022-12-22T00:54:26ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782016-01-01281556710.1016/j.jksuci.2014.04.007A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approachN. Moganarangan0R.G. Babukarthik1S. Bhuvaneswari2M.S. Saleem Basha3P. Dhavachelvan4Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamil Nadu, IndiaDepartment of Computer Science, Pondicherry University, Puducherry, IndiaDepartment of Computer Science, Pondicherry University, Puducherry, IndiaDepartment of Computer Science, Pondicherry University, Puducherry, IndiaDepartment of Computer Science, Pondicherry University, Puducherry, IndiaCloud computing slowly gained an important role in scientific application, on-demand facility of virtualized resources is provided as a service with the help of virtualization without any additional waiting time. Energy consumption is reduced for job scheduling problems based on makespan constraint which in turn leads to significant decrease in the energy cost. Additionally, there is an increase in complexity for scheduling problems mainly because the application is not based on makespan constraint. In this paper we propose a new Hybrid algorithm combining the benefits of ACO and cuckoo search algorithm. It is focused on the voltage scaling factor for reduction of energy consumption. Performance of the Hybrid algorithm is considerably increased from 45 tasks onward when compared to ACO. Energy consumed by Hybrid algorithm is measured and energy improvement is evaluated up to 35 tasks. Energy consumption is the same as ACO algorithm because as the number of tasks increases (45 to 70) there is a considerable decrease in the energy consumption rate. Makespan of Hybrid algorithm based on number of tasks is compared with ACO algorithm. Further we have analyzed the energy consumption for a number of processors and its improvement rate – up to 6 processors, energy consumption is considerably reduced and the energy consumption tends to be in steady state with further increase in the number of processors.http://www.sciencedirect.com/science/article/pii/S1319157815000816ACO ant colony optimizationCS cuckoo searchVSF voltage scaling factorEcPSO extended compact particle swarm optimization
spellingShingle N. Moganarangan
R.G. Babukarthik
S. Bhuvaneswari
M.S. Saleem Basha
P. Dhavachelvan
A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach
Journal of King Saud University: Computer and Information Sciences
ACO ant colony optimization
CS cuckoo search
VSF voltage scaling factor
EcPSO extended compact particle swarm optimization
title A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach
title_full A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach
title_fullStr A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach
title_full_unstemmed A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach
title_short A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach
title_sort novel algorithm for reducing energy consumption in cloud computing environment web service computing approach
topic ACO ant colony optimization
CS cuckoo search
VSF voltage scaling factor
EcPSO extended compact particle swarm optimization
url http://www.sciencedirect.com/science/article/pii/S1319157815000816
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