Green scheduling for cloud data centers using renewable resources

Chonglin Gu; Chunyan Liu; Jiangtao Zhang; Hejiao Huang; Xiaohua Jia

doi:10.1109/INFCOMW.2015.7179410

Green scheduling for cloud data centers using renewable resources

Chonglin Gu
, Chunyan Liu
, Jiangtao Zhang
, Hejiao Huang^*
, Xiaohua Jia

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

39 Scopus citations

Abstract

Cloud data centers provide all kinds of service using hundreds of thousands of servers. This naturally leads to concerns about the effect on environment such as carbon emissions and global warming. Huge amounts of effort have been devoted to power-aware scheduling using renewable energy. However, the intermittent availability of the renewable energy brings us a new challenge: how to dynamically distribute the requests to the data centers that are powered by renewable energy, while minimizing carbon emissions under a fixed electricity budget. In this paper, we model our problem as a constraint optimization problem. The goal is to minimize the carbon emissions of the data centers by using renewable energy while satisfying: (1) the request processing time constraint; (2) the total electricity budget in each time slot; (3) the intermittent supply of the renewable resources; (4) the maximal number of servers in each data center. We solve the problem by ingeniously transforming it into an integer linear programming model, and calculate the decision variables using existed method. Experiments show that our scheduler can minimize carbon emissions using renewable resources, while satisfying the constraints mentioned above.

Original language	English
Title of host publication	2015 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	354-359
Number of pages	6
ISBN (Electronic)	9781467371315
DOIs	https://doi.org/10.1109/INFCOMW.2015.7179410
State	Published - 4 Aug 2015
Externally published	Yes
Event	IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015 - Hong Kong, Hong Kong Duration: 26 Apr 2015 → 1 May 2015

Publication series

Name	Proceedings - IEEE INFOCOM
Volume	2015-August
ISSN (Print)	0743-166X

Conference

Conference	IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015
Country/Territory	Hong Kong
City	Hong Kong
Period	26/04/15 → 1/05/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

carbon emission reduction
cloud computing
data center
green
renewable energy
schedule

Access to Document

10.1109/INFCOMW.2015.7179410

Cite this

Gu, C., Liu, C., Zhang, J., Huang, H., & Jia, X. (2015). Green scheduling for cloud data centers using renewable resources. In 2015 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015 (pp. 354-359). Article 7179410 (Proceedings - IEEE INFOCOM; Vol. 2015-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFCOMW.2015.7179410

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title = "Green scheduling for cloud data centers using renewable resources",

abstract = "Cloud data centers provide all kinds of service using hundreds of thousands of servers. This naturally leads to concerns about the effect on environment such as carbon emissions and global warming. Huge amounts of effort have been devoted to power-aware scheduling using renewable energy. However, the intermittent availability of the renewable energy brings us a new challenge: how to dynamically distribute the requests to the data centers that are powered by renewable energy, while minimizing carbon emissions under a fixed electricity budget. In this paper, we model our problem as a constraint optimization problem. The goal is to minimize the carbon emissions of the data centers by using renewable energy while satisfying: (1) the request processing time constraint; (2) the total electricity budget in each time slot; (3) the intermittent supply of the renewable resources; (4) the maximal number of servers in each data center. We solve the problem by ingeniously transforming it into an integer linear programming model, and calculate the decision variables using existed method. Experiments show that our scheduler can minimize carbon emissions using renewable resources, while satisfying the constraints mentioned above.",

keywords = "carbon emission reduction, cloud computing, data center, green, renewable energy, schedule",

author = "Chonglin Gu and Chunyan Liu and Jiangtao Zhang and Hejiao Huang and Xiaohua Jia",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015 ; Conference date: 26-04-2015 Through 01-05-2015",

year = "2015",

month = aug,

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doi = "10.1109/INFCOMW.2015.7179410",

language = "英语",

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Gu, C, Liu, C, Zhang, J, Huang, H & Jia, X 2015, Green scheduling for cloud data centers using renewable resources. in 2015 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015., 7179410, Proceedings - IEEE INFOCOM, vol. 2015-August, Institute of Electrical and Electronics Engineers Inc., pp. 354-359, IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015, Hong Kong, Hong Kong, 26/04/15. https://doi.org/10.1109/INFCOMW.2015.7179410

Green scheduling for cloud data centers using renewable resources. / Gu, Chonglin; Liu, Chunyan; Zhang, Jiangtao et al.
2015 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 354-359 7179410 (Proceedings - IEEE INFOCOM; Vol. 2015-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Gu, Chonglin

AU - Liu, Chunyan

AU - Zhang, Jiangtao

AU - Huang, Hejiao

AU - Jia, Xiaohua

PY - 2015/8/4

Y1 - 2015/8/4

N2 - Cloud data centers provide all kinds of service using hundreds of thousands of servers. This naturally leads to concerns about the effect on environment such as carbon emissions and global warming. Huge amounts of effort have been devoted to power-aware scheduling using renewable energy. However, the intermittent availability of the renewable energy brings us a new challenge: how to dynamically distribute the requests to the data centers that are powered by renewable energy, while minimizing carbon emissions under a fixed electricity budget. In this paper, we model our problem as a constraint optimization problem. The goal is to minimize the carbon emissions of the data centers by using renewable energy while satisfying: (1) the request processing time constraint; (2) the total electricity budget in each time slot; (3) the intermittent supply of the renewable resources; (4) the maximal number of servers in each data center. We solve the problem by ingeniously transforming it into an integer linear programming model, and calculate the decision variables using existed method. Experiments show that our scheduler can minimize carbon emissions using renewable resources, while satisfying the constraints mentioned above.

AB - Cloud data centers provide all kinds of service using hundreds of thousands of servers. This naturally leads to concerns about the effect on environment such as carbon emissions and global warming. Huge amounts of effort have been devoted to power-aware scheduling using renewable energy. However, the intermittent availability of the renewable energy brings us a new challenge: how to dynamically distribute the requests to the data centers that are powered by renewable energy, while minimizing carbon emissions under a fixed electricity budget. In this paper, we model our problem as a constraint optimization problem. The goal is to minimize the carbon emissions of the data centers by using renewable energy while satisfying: (1) the request processing time constraint; (2) the total electricity budget in each time slot; (3) the intermittent supply of the renewable resources; (4) the maximal number of servers in each data center. We solve the problem by ingeniously transforming it into an integer linear programming model, and calculate the decision variables using existed method. Experiments show that our scheduler can minimize carbon emissions using renewable resources, while satisfying the constraints mentioned above.

KW - carbon emission reduction

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KW - renewable energy

KW - schedule

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ER -

Green scheduling for cloud data centers using renewable resources

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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