Optimal virtual machine selection for anomaly detection using a swarm intelligence approach

Selvaraj, A; Patan, R; Gandomi, AH; Deverajan, GG; Pushparaj, M

Optimal virtual machine selection for anomaly detection using a swarm intelligence approach

Selvaraj, A Patan, R Gandomi, AH Deverajan, GG Pushparaj, M

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Applied Soft Computing Journal, 2019, 84
Issue Date:: 2019-11-01

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Field	Value	Language
dc.contributor.author	Selvaraj, A
dc.contributor.author	Patan, R
dc.contributor.author	Gandomi, AH
dc.contributor.author	Deverajan, GG
dc.contributor.author	Pushparaj, M
dc.date.accessioned	2020-05-07T02:12:12Z
dc.date.available	2020-05-07T02:12:12Z
dc.date.issued	2019-11-01
dc.identifier.citation	Applied Soft Computing Journal, 2019, 84
dc.identifier.issn	1568-4946
dc.identifier.issn	1872-9681
dc.identifier.uri	http://hdl.handle.net/10453/140532
dc.description.abstract	© 2019 Elsevier B.V. Cloud computing plays a significant role in Healthcare Service (HCS) applications and rapidly improves it. A significant challenge is the selection of Virtual Machine (VM) in order to process a medical request. The optimal selection of VM increases the performance of HCS by minimizing the running time of the medical request and also substantially utilizes cloud resources. This paper presents a new idea for optimizing VM selection using a swarm intelligence approach called Analogous Particle swarm optimization (APSO) which works a cloud computing environment. To compute the running time of a medical request, three parameters are considered: Turnaround Time (TAT), Waiting time (WT), and CPU utilization. In addition, a selected optimal VM is used for predicting kidney disease. Early detection of kidney disease facilitates successful treatment. Here, the neural network is used as an automated technique to diagnose kidney disease. A set of experiments and comparisons were performed to analyze the proposed system (APSO and neural network). The results showed that the APSO model performed well, with an execution time of running all particle is 1 s (50 to 80%). Also, the proposed model improved the system efficiency by 5.6%. The precision of recognizing kidney disease using the neural network was 95.7% which outperfomed five other well-known classifiers.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Applied Soft Computing Journal
dc.relation.isbasedon	10.1016/j.asoc.2019.105686
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Optimal virtual machine selection for anomaly detection using a swarm intelligence approach
dc.type	Journal Article
utslib.citation.volume	84
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-05-07T02:12:11Z
pubs.publication-status	Published
pubs.volume	84

Abstract:

© 2019 Elsevier B.V. Cloud computing plays a significant role in Healthcare Service (HCS) applications and rapidly improves it. A significant challenge is the selection of Virtual Machine (VM) in order to process a medical request. The optimal selection of VM increases the performance of HCS by minimizing the running time of the medical request and also substantially utilizes cloud resources. This paper presents a new idea for optimizing VM selection using a swarm intelligence approach called Analogous Particle swarm optimization (APSO) which works a cloud computing environment. To compute the running time of a medical request, three parameters are considered: Turnaround Time (TAT), Waiting time (WT), and CPU utilization. In addition, a selected optimal VM is used for predicting kidney disease. Early detection of kidney disease facilitates successful treatment. Here, the neural network is used as an automated technique to diagnose kidney disease. A set of experiments and comparisons were performed to analyze the proposed system (APSO and neural network). The results showed that the APSO model performed well, with an execution time of running all particle is 1 s (50 to 80%). Also, the proposed model improved the system efficiency by 5.6%. The precision of recognizing kidney disease using the neural network was 95.7% which outperfomed five other well-known classifiers.

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http://hdl.handle.net/10453/140532