Handling uncertainty in cloud resource management using fuzzy Bayesian networks

Ramezani, F; Naderpour, M; Lu, J

Handling uncertainty in cloud resource management using fuzzy Bayesian networks

Ramezani, F

Naderpour, M

Lu, J

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2015, 2015-November
Issue Date:: 2015-11-25

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Field	Value	Language
dc.contributor.author	Ramezani, F https://orcid.org/0000-0002-0368-321X	en_US
dc.contributor.author	Naderpour, M https://orcid.org/0000-0002-3549-3875	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2015-11-25	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2015, 2015-November	en_US
dc.identifier.isbn	9781467374286	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43335
dc.description.abstract	© 2015 IEEE. The success of cloud services depends critically on the effective management of virtualized resources. This paper aims to design and implement a decision support method to handle uncertainties in resource management from the cloud provider perspective that enables underlying complexity, automates resource provisioning and controls client-perceived quality of service. The paper includes a probabilistic decision making module that relies upon a fuzzy Bayesian network to determine the current situation status of a cloud infrastructure, including physical and virtual machines, and predicts the near future state, that will help the hypervisor to migrate or expand the VMs to reduce execution time and meet quality of service requirements. First, the framework of resource management is presented. Second, the decision making module is developed. Lastly, a series of experiments to investigate the performance of the proposed module is implemented. Experiments reveal the efficiency of the module prototype.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140101366
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2015.7337979	en_US
dc.title	Handling uncertainty in cloud resource management using fuzzy Bayesian networks	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2015-November	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2015-November	en_US

Abstract:

© 2015 IEEE. The success of cloud services depends critically on the effective management of virtualized resources. This paper aims to design and implement a decision support method to handle uncertainties in resource management from the cloud provider perspective that enables underlying complexity, automates resource provisioning and controls client-perceived quality of service. The paper includes a probabilistic decision making module that relies upon a fuzzy Bayesian network to determine the current situation status of a cloud infrastructure, including physical and virtual machines, and predicts the near future state, that will help the hypervisor to migrate or expand the VMs to reduce execution time and meet quality of service requirements. First, the framework of resource management is presented. Second, the decision making module is developed. Lastly, a series of experiments to investigate the performance of the proposed module is implemented. Experiments reveal the efficiency of the module prototype.

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http://hdl.handle.net/10453/43335