SLA-aware and deadline constrained profit optimization for cloud resource management in big data analytics-as-a-service platforms

Zhao, Y; Calheiros, RN; Vasilakos, AV; Bailey, J; Sinnott, RO

SLA-aware and deadline constrained profit optimization for cloud resource management in big data analytics-as-a-service platforms

Zhao, Y Calheiros, RN Vasilakos, AV Bailey, J Sinnott, RO

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Cloud Computing, CLOUD, 2019, 2019-July, pp. 146-156
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Zhao, Y
dc.contributor.author	Calheiros, RN
dc.contributor.author	Vasilakos, AV
dc.contributor.author	Bailey, J
dc.contributor.author	Sinnott, RO
dc.date	2019-07-08
dc.date.accessioned	2020-06-21T02:52:03Z
dc.date.available	2020-06-21T02:52:03Z
dc.date.issued	2019-07-01
dc.identifier.citation	IEEE International Conference on Cloud Computing, CLOUD, 2019, 2019-July, pp. 146-156
dc.identifier.isbn	9781728127057
dc.identifier.issn	2159-6182
dc.identifier.issn	2159-6190
dc.identifier.uri	http://hdl.handle.net/10453/141599
dc.description.abstract	© 2019 IEEE. Discovering optimal data analytics solutions to extract value from data for better and faster decision making is essential for many application domains, especially in the big data era. Big data analytics typically requires a tremendous amount of computational resources to process large data volumes that can be very expensive and time consuming. Our research focuses on providing optimization solutions for Analytics-as-a-Service (AaaS) platforms that automatically and elastically provision cloud resources to execute queries guaranteeing Service Level Agreements (SLAs) across a range of Quality of Service (QoS) requirements. We propose admission control and resource scheduling algorithms for AaaS platforms to maximize profits while providing time-minimized query execution plans to meet user demands and expectations. To enable timely responses as required for many domains, the algorithms utilize data splitting-based query admission and resource scheduling offering parallel processing on the split datasets. Extensive experiments are conducted to evaluate the algorithm performance compared to state-of-the-art optimization algorithms. Experimental results show that our algorithms perform significantly better from a range of perspectives, including increasing query admission rates and creating higher profits, whilst supporting efficient resource configurations that are able to support big data processing demands under tight deadlines.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE International Conference on Cloud Computing, CLOUD
dc.relation.ispartof	International Conference on Cloud Computing
dc.relation.isbasedon	10.1109/CLOUD.2019.00034
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	SLA-aware and deadline constrained profit optimization for cloud resource management in big data analytics-as-a-service platforms
dc.type	Conference Proceeding
utslib.citation.volume	2019-July
utslib.location.activity	Milan, Italy
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-21T02:51:58Z
pubs.finish-date	2019-07-13
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-07-08
pubs.volume	2019-July
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Discovering optimal data analytics solutions to extract value from data for better and faster decision making is essential for many application domains, especially in the big data era. Big data analytics typically requires a tremendous amount of computational resources to process large data volumes that can be very expensive and time consuming. Our research focuses on providing optimization solutions for Analytics-as-a-Service (AaaS) platforms that automatically and elastically provision cloud resources to execute queries guaranteeing Service Level Agreements (SLAs) across a range of Quality of Service (QoS) requirements. We propose admission control and resource scheduling algorithms for AaaS platforms to maximize profits while providing time-minimized query execution plans to meet user demands and expectations. To enable timely responses as required for many domains, the algorithms utilize data splitting-based query admission and resource scheduling offering parallel processing on the split datasets. Extensive experiments are conducted to evaluate the algorithm performance compared to state-of-the-art optimization algorithms. Experimental results show that our algorithms perform significantly better from a range of perspectives, including increasing query admission rates and creating higher profits, whilst supporting efficient resource configurations that are able to support big data processing demands under tight deadlines.

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http://hdl.handle.net/10453/141599