Energy-aware scheduling with computing and data consolidation balance in 3-tier data center

M. Combarro; A. Tchernykh; A. Drozdov; D. Kliazovich; G. Radchenko

doi:10.1109/EnT.2016.13

Energy-aware scheduling with computing and data consolidation balance in 3-tier data center

M. Combarro, A. Tchernykh, A. Drozdov, D. Kliazovich, G. Radchenko

Publikation: Konferenzbeitrag › Papier › Begutachtung

Abstract

Energy consumption represents a large percentage of the operational expenses in data centers. Most of the existing solutions for energy-aware scheduling are focusing on job distribution and consolidation between computing servers, while network characteristics are not considered. In this paper, we propose a model of power and network-aware scheduling that can be tuned to achieve energy-savings, through job consolidation and traffic load balancing. We describe a methodology to find the best tuning of the Adjustable Scheduler. © 2016 IEEE.

Originalsprache	Englisch
Seiten	29-33
Seitenumfang	5
DOIs	https://doi.org/10.1109/EnT.2016.13
Publikationsstatus	Veröffentlicht - 2017
Veranstaltung	9th International Conference on Engineering and Telecommunication, - Moscow, Russland Dauer: 29 Nov. 2016 → 30 Nov. 2016

Konferenz

Konferenz	9th International Conference on Engineering and Telecommunication,
Kurztitel	EnT 2016
Land/Gebiet	Russland
Ort	Moscow
Zeitraum	29/11/16 → 30/11/16

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10.1109/EnT.2016.13

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013098527&doi=10.1109%2fEnT.2016.13&partnerID=40&md5=0218b69a050c984c52717ede3645c7ad

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title = "Energy-aware scheduling with computing and data consolidation balance in 3-tier data center",

abstract = "Energy consumption represents a large percentage of the operational expenses in data centers. Most of the existing solutions for energy-aware scheduling are focusing on job distribution and consolidation between computing servers, while network characteristics are not considered. In this paper, we propose a model of power and network-aware scheduling that can be tuned to achieve energy-savings, through job consolidation and traffic load balancing. We describe a methodology to find the best tuning of the Adjustable Scheduler. {\textcopyright} 2016 IEEE.",

keywords = "Data center, Energy efficient, Parallel machines, Scheduling algorithms, Energy conservation, Energy efficiency, Energy utilization, Scheduling, Data centers, Energy-aware scheduling, Network characteristics, Network-Aware Scheduling, Operational expense, Parallel machine, Traffic loads, Power management",

author = "M. Combarro and A. Tchernykh and A. Drozdov and D. Kliazovich and G. Radchenko",

note = "Conference code: 125964 Cited By :5 Export Date: 27 August 2021 Funding details: Ministry of Education and Science of the Russian Federation, Minobrnauka, 218, G25.31.0061/12/02/2013, RFMEFI58214X0003 Funding text 1: The work is partially supported by Ministry of Education and Science of Russian Federation under contracts RFMEFI58214X0003 and 02.G25.31.0061/12/02/2013 (Government Regulation No 218 from 09/04/2010), CONACYT (Consejo Nacional de Ciencia y Tecnolog?a, M?xico), grant no. 178415.; 9th International Conference on Engineering and Telecommunication, , EnT 2016 ; Conference date: 29-11-2016 Through 30-11-2016",

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AU - Combarro, M.

AU - Tchernykh, A.

AU - Drozdov, A.

AU - Kliazovich, D.

AU - Radchenko, G.

N1 - Conference code: 125964 Cited By :5 Export Date: 27 August 2021 Funding details: Ministry of Education and Science of the Russian Federation, Minobrnauka, 218, G25.31.0061/12/02/2013, RFMEFI58214X0003 Funding text 1: The work is partially supported by Ministry of Education and Science of Russian Federation under contracts RFMEFI58214X0003 and 02.G25.31.0061/12/02/2013 (Government Regulation No 218 from 09/04/2010), CONACYT (Consejo Nacional de Ciencia y Tecnolog?a, M?xico), grant no. 178415.

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N2 - Energy consumption represents a large percentage of the operational expenses in data centers. Most of the existing solutions for energy-aware scheduling are focusing on job distribution and consolidation between computing servers, while network characteristics are not considered. In this paper, we propose a model of power and network-aware scheduling that can be tuned to achieve energy-savings, through job consolidation and traffic load balancing. We describe a methodology to find the best tuning of the Adjustable Scheduler. © 2016 IEEE.

AB - Energy consumption represents a large percentage of the operational expenses in data centers. Most of the existing solutions for energy-aware scheduling are focusing on job distribution and consolidation between computing servers, while network characteristics are not considered. In this paper, we propose a model of power and network-aware scheduling that can be tuned to achieve energy-savings, through job consolidation and traffic load balancing. We describe a methodology to find the best tuning of the Adjustable Scheduler. © 2016 IEEE.

KW - Data center

KW - Energy efficient

KW - Parallel machines

KW - Scheduling algorithms

KW - Energy conservation

KW - Energy efficiency

KW - Energy utilization

KW - Scheduling

KW - Data centers

KW - Energy-aware scheduling

KW - Network characteristics

KW - Network-Aware Scheduling

KW - Operational expense

KW - Parallel machine

KW - Traffic loads

KW - Power management

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DO - 10.1109/EnT.2016.13

M3 - Paper

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T2 - 9th International Conference on Engineering and Telecommunication,

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ER -

Energy-aware scheduling with computing and data consolidation balance in 3-tier data center

Abstract

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