Decentralized Real-Time Optimization of Voltage Reconfigurable Cloud Computing Data Center

Hou, S; Ni, W; Zhao, S; Cheng, B; Chen, S; Chen, J

Decentralized Real-Time Optimization of Voltage Reconfigurable Cloud Computing Data Center

Hou, S Ni, W

Zhao, S Cheng, B Chen, S Chen, J

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Green Communications and Networking, 2020, 4, (2), pp. 577-592
Issue Date:: 2020-06-01

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Field	Value	Language
dc.contributor.author	Hou, S
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Zhao, S
dc.contributor.author	Cheng, B
dc.contributor.author	Chen, S
dc.contributor.author	Chen, J
dc.date.accessioned	2020-11-27T20:31:46Z
dc.date.available	2020-11-27T20:31:46Z
dc.date.issued	2020-06-01
dc.identifier.citation	IEEE Transactions on Green Communications and Networking, 2020, 4, (2), pp. 577-592
dc.identifier.issn	2473-2400
dc.identifier.issn	2473-2400
dc.identifier.uri	http://hdl.handle.net/10453/144427
dc.description.abstract	© 2017 IEEE. Dynamic Voltage and Frequency Scaling, and Adaptive Body Biasing are increasingly adopted hardware techniques to improve energy efficiency of multi-core servers by adjusting reconfigurable supply and body bias voltages. Existing algorithms cannot fulfill the potential of the techniques because random variations of workload and background traffic can lead to coupling of voltage configurations over time and hinder effective real-time reconfigurations. This paper proposes a new approach which enables multi-core servers to optimize in real-time their configurations under random traffic variations. The approach asymptotically minimizes the time-averaged energy consumption of cloud computing while maintaining platform stability in a fully decentralized fashion. Lyapunov optimization is employed to decouple and separately optimize the voltage configuration, inter- and intra-server offloading schedules among servers and over time. The voltage configuration which is non-convex is proved to increasingly exhibit convexity with growing workloads. The optimality loss from the non-convexity asymptotically diminishes. Simulations show our approach dramatically reduces the power if the cloud is lightly loaded, or converts the power to processing capacity otherwise. Embraced by theoretical breakthroughs, the approach can potentially revolutionize cloud computing.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Green Communications and Networking
dc.relation.isbasedon	10.1109/TGCN.2020.2987063
dc.rights	© 2020 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	Decentralized Real-Time Optimization of Voltage Reconfigurable Cloud Computing Data Center
dc.type	Journal Article
utslib.citation.volume	4
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	*
dc.date.updated	2020-11-27T20:31:43Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	4
utslib.citation.issue	2

Abstract:

© 2017 IEEE. Dynamic Voltage and Frequency Scaling, and Adaptive Body Biasing are increasingly adopted hardware techniques to improve energy efficiency of multi-core servers by adjusting reconfigurable supply and body bias voltages. Existing algorithms cannot fulfill the potential of the techniques because random variations of workload and background traffic can lead to coupling of voltage configurations over time and hinder effective real-time reconfigurations. This paper proposes a new approach which enables multi-core servers to optimize in real-time their configurations under random traffic variations. The approach asymptotically minimizes the time-averaged energy consumption of cloud computing while maintaining platform stability in a fully decentralized fashion. Lyapunov optimization is employed to decouple and separately optimize the voltage configuration, inter- and intra-server offloading schedules among servers and over time. The voltage configuration which is non-convex is proved to increasingly exhibit convexity with growing workloads. The optimality loss from the non-convexity asymptotically diminishes. Simulations show our approach dramatically reduces the power if the cloud is lightly loaded, or converts the power to processing capacity otherwise. Embraced by theoretical breakthroughs, the approach can potentially revolutionize cloud computing.

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http://hdl.handle.net/10453/144427