HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets

Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Reso...

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Main Authors:	Hasan, Raed Abdulkareem, Mostafa, A. Mohammed, Salih, Zeyad Hussein, M. A., Ameedeen, Tapus, Nicolae, Mohammed, Muamer N.
Format:	Article
Language:	English
Published:	Universitas Ahmad Dahlan 2018
Subjects:	T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/21940/ http://umpir.ump.edu.my/id/eprint/21940/ http://umpir.ump.edu.my/id/eprint/21940/ http://umpir.ump.edu.my/id/eprint/21940/1/9415-28194-1-PB.pdf

id	ump-21940
recordtype	eprints
spelling	ump-219402018-10-18T07:37:35Z http://umpir.ump.edu.my/id/eprint/21940/ HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets Hasan, Raed Abdulkareem Mostafa, A. Mohammed Salih, Zeyad Hussein M. A., Ameedeen Tapus, Nicolae Mohammed, Muamer N. T Technology (General) Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made. Universitas Ahmad Dahlan 2018-10 Article PeerReviewed pdf en cc_by_nc_nd http://umpir.ump.edu.my/id/eprint/21940/1/9415-28194-1-PB.pdf Hasan, Raed Abdulkareem and Mostafa, A. Mohammed and Salih, Zeyad Hussein and M. A., Ameedeen and Tapus, Nicolae and Mohammed, Muamer N. (2018) HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets. Telkomnika, 16 (5). pp. 2144-2154. ISSN 1693-6930 http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/9415 DOI: 10.12928/TELKOMNIKA.v16i5.9415
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Hasan, Raed Abdulkareem Mostafa, A. Mohammed Salih, Zeyad Hussein M. A., Ameedeen Tapus, Nicolae Mohammed, Muamer N. HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets
description	Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
format	Article
author	Hasan, Raed Abdulkareem Mostafa, A. Mohammed Salih, Zeyad Hussein M. A., Ameedeen Tapus, Nicolae Mohammed, Muamer N.
author_facet	Hasan, Raed Abdulkareem Mostafa, A. Mohammed Salih, Zeyad Hussein M. A., Ameedeen Tapus, Nicolae Mohammed, Muamer N.
author_sort	Hasan, Raed Abdulkareem
title	HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets
title_short	HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets
title_full	HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets
title_fullStr	HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets
title_full_unstemmed	HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets
title_sort	hso: a hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets
publisher	Universitas Ahmad Dahlan
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/21940/ http://umpir.ump.edu.my/id/eprint/21940/ http://umpir.ump.edu.my/id/eprint/21940/ http://umpir.ump.edu.my/id/eprint/21940/1/9415-28194-1-PB.pdf
first_indexed	2023-09-18T22:32:25Z
last_indexed	2023-09-18T22:32:25Z
_version_	1777416373977219072

HSO: A hybrid swarm optimization algorithm for reducing energy consumption in the cloudlets

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