Real-Time Optimization of Dynamic Speed Scaling for Distributed Data Centers

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

Real-Time Optimization of Dynamic Speed Scaling for Distributed Data Centers

Hou, S Ni, W

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

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Network Science and Engineering, 2020, 7, (3), pp. 2090-2103
Issue Date:: 2020-07-01

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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	Chen, S
dc.contributor.author	Zhao, S
dc.contributor.author	Cheng, B
dc.contributor.author	Chen, J
dc.date.accessioned	2021-03-23T01:28:30Z
dc.date.available	2021-03-23T01:28:30Z
dc.date.issued	2020-07-01
dc.identifier.citation	IEEE Transactions on Network Science and Engineering, 2020, 7, (3), pp. 2090-2103
dc.identifier.issn	2327-4697
dc.identifier.issn	2327-4697
dc.identifier.uri	http://hdl.handle.net/10453/147464
dc.description.abstract	© 2013 IEEE. This paper proposes a new distributed real-time optimization for MapReduce-style framework in emerging cloud platforms supporting dynamic speed scaling functions. Distinctively different from the existing MapReduce parallelism strategy with fixed specific data chuck sizes, the new approach is able to dynamically dispatch input data of adequate sizes and synthesize interim processing results according to applications and the state of data centers (DCs). The key idea is to decouple the optimizations of data dispatching, processing, and result aggregation without loss of optimality, by employing stochastic optimization techniques. Another important aspect is that we optimize the subproblem of data processing to leverage the energy- and speed-configurability of DCs, by optimally deciding the number of servers to be activated at every DC and the CPU speeds of the activated servers. Evident from extensive simulations, the proposed approach is able to increase the throughput-cost ratio by up to 94.3%, as compared to existing initiatives, and substantially improve the throughput in the case of high-rate data streams.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	IEEE Transactions on Network Science and Engineering
dc.relation.isbasedon	10.1109/TNSE.2020.2974250
dc.rights	info:eu-repo/semantics/closedAccess
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.title	Real-Time Optimization of Dynamic Speed Scaling for Distributed Data Centers
dc.type	Journal Article
utslib.citation.volume	7
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
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-03-23T01:28:29Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	3

Abstract:

© 2013 IEEE. This paper proposes a new distributed real-time optimization for MapReduce-style framework in emerging cloud platforms supporting dynamic speed scaling functions. Distinctively different from the existing MapReduce parallelism strategy with fixed specific data chuck sizes, the new approach is able to dynamically dispatch input data of adequate sizes and synthesize interim processing results according to applications and the state of data centers (DCs). The key idea is to decouple the optimizations of data dispatching, processing, and result aggregation without loss of optimality, by employing stochastic optimization techniques. Another important aspect is that we optimize the subproblem of data processing to leverage the energy- and speed-configurability of DCs, by optimally deciding the number of servers to be activated at every DC and the CPU speeds of the activated servers. Evident from extensive simulations, the proposed approach is able to increase the throughput-cost ratio by up to 94.3%, as compared to existing initiatives, and substantially improve the throughput in the case of high-rate data streams.

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http://hdl.handle.net/10453/147464