A task scheduling method for energy-performance trade-off in clouds

Yang, J; Xu, X; Tang, W; Hu, C; Dou, W; Chen, J

A task scheduling method for energy-performance trade-off in clouds

Yang, J Xu, X Tang, W Hu, C Dou, W Chen, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016, 2017, pp. 1029 - 1036
Issue Date:: 2017-01-20

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Field	Value	Language
dc.contributor.author	Yang, J	en_US
dc.contributor.author	Xu, X	en_US
dc.contributor.author	Tang, W	en_US
dc.contributor.author	Hu, C	en_US
dc.contributor.author	Dou, W	en_US
dc.contributor.author	Chen, J	en_US
dc.date.issued	2017-01-20	en_US
dc.identifier.citation	Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016, 2017, pp. 1029 - 1036	en_US
dc.identifier.isbn	9781509042968	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126318
dc.description.abstract	© 2016 IEEE. Energy consumption is an increasingly important issue for profit-driven cloud. Meanwhile, meeting users' expected response time is crucial for the real-time tasks. However, minimizing energy consumption may delay response time during task scheduling. Unfortunately, existing scheduling algorithms remain limit in balancing energy-saving and performance in elastic cloud environment. In view of this challenge, a task scheduling method for trade-off between energy and performance is proposed in this paper. Firstly, the energy-aware task scheduling problem in clouds is modeled by incorporating the virtualization technology. Then, an energy-aware resource provisioning algorithm is developed for generating available allocation plans for real time task by exploiting the elasticity of virtual resources. Finally, an energy-performance trade-off scheduling algorithm is designed for selecting final solution based on multi-objective optimization. The experimental evaluations are presented for validating our method.	en_US
dc.relation.ispartof	Proceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016	en_US
dc.relation.isbasedon	10.1109/HPCC-SmartCity-DSS.2016.0146	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A task scheduling method for energy-performance trade-off in clouds	en_US
dc.type	Conference Proceeding
utslib.for	0805 Distributed Computing	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Energy consumption is an increasingly important issue for profit-driven cloud. Meanwhile, meeting users' expected response time is crucial for the real-time tasks. However, minimizing energy consumption may delay response time during task scheduling. Unfortunately, existing scheduling algorithms remain limit in balancing energy-saving and performance in elastic cloud environment. In view of this challenge, a task scheduling method for trade-off between energy and performance is proposed in this paper. Firstly, the energy-aware task scheduling problem in clouds is modeled by incorporating the virtualization technology. Then, an energy-aware resource provisioning algorithm is developed for generating available allocation plans for real time task by exploiting the elasticity of virtual resources. Finally, an energy-performance trade-off scheduling algorithm is designed for selecting final solution based on multi-objective optimization. The experimental evaluations are presented for validating our method.

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http://hdl.handle.net/10453/126318