Energy cost minimisation of geographically distributed data centres

Ignacio Castineiras; Deepak Mehta; Barry O'Sullivan

doi:10.1109/CloudNet.2015.7335322

Energy cost minimisation of geographically distributed data centres

Ignacio Castineiras
, Deepak Mehta
, Barry O'Sullivan

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

Abstract

In this paper we present a Mixed Integer Programming-based (MIP) approach to optimise the workload allocation of geographically distributed Data Centres (DCs) in the face of dynamic DC performances and electricity prices. We reduce the overall electricity cost for running a DC set over an operating horizon by finding a good compromise between: The number of migrations subject to the sovereignty of data, the loads of the servers in DCs and the energy cost reduction possible by following the DCs with best performance and energy efficiencies over time. To model the DC performance we use Power Usage Effectiveness (PUE), with a devoted function per DC dependent on the current outside temperature. We discuss the multiple dimensions of the problem, present a mathematical formulation for it and provide empirical evaluation to claim the improvement on the electricity cost achieved.

Original language	English
Title of host publication	2015 IEEE 4th International Conference on Cloud Networking, CloudNet 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	279-284
Number of pages	6
ISBN (Electronic)	9781467395014
DOIs	https://doi.org/10.1109/CloudNet.2015.7335322
Publication status	Published - 20 Nov 2015
Event	4th IEEE International Conference on Cloud Networking, CloudNet 2015 - Falls, Canada Duration: 5 Oct 2015 → 7 Oct 2015

Publication series

Name	2015 IEEE 4th International Conference on Cloud Networking, CloudNet 2015

Conference

Conference	4th IEEE International Conference on Cloud Networking, CloudNet 2015
Country/Territory	Canada
City	Falls
Period	5/10/15 → 7/10/15

UN SDGs

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

SDG 7 Affordable and Clean Energy

UCC Futures

Artificial Intelligence and Data Analytics

Access to Document

10.1109/CloudNet.2015.7335322

Cite this

Castineiras, I., Mehta, D., & O'Sullivan, B. (2015). Energy cost minimisation of geographically distributed data centres. In 2015 IEEE 4th International Conference on Cloud Networking, CloudNet 2015 (pp. 279-284). Article 7335322 (2015 IEEE 4th International Conference on Cloud Networking, CloudNet 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CloudNet.2015.7335322

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title = "Energy cost minimisation of geographically distributed data centres",

abstract = "In this paper we present a Mixed Integer Programming-based (MIP) approach to optimise the workload allocation of geographically distributed Data Centres (DCs) in the face of dynamic DC performances and electricity prices. We reduce the overall electricity cost for running a DC set over an operating horizon by finding a good compromise between: The number of migrations subject to the sovereignty of data, the loads of the servers in DCs and the energy cost reduction possible by following the DCs with best performance and energy efficiencies over time. To model the DC performance we use Power Usage Effectiveness (PUE), with a devoted function per DC dependent on the current outside temperature. We discuss the multiple dimensions of the problem, present a mathematical formulation for it and provide empirical evaluation to claim the improvement on the electricity cost achieved.",

author = "Ignacio Castineiras and Deepak Mehta and Barry O'Sullivan",

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doi = "10.1109/CloudNet.2015.7335322",

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series = "2015 IEEE 4th International Conference on Cloud Networking, CloudNet 2015",

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Castineiras, I, Mehta, D & O'Sullivan, B 2015, Energy cost minimisation of geographically distributed data centres. in 2015 IEEE 4th International Conference on Cloud Networking, CloudNet 2015., 7335322, 2015 IEEE 4th International Conference on Cloud Networking, CloudNet 2015, Institute of Electrical and Electronics Engineers Inc., pp. 279-284, 4th IEEE International Conference on Cloud Networking, CloudNet 2015, Falls, Canada, 5/10/15. https://doi.org/10.1109/CloudNet.2015.7335322

Energy cost minimisation of geographically distributed data centres. / Castineiras, Ignacio; Mehta, Deepak; O'Sullivan, Barry.
2015 IEEE 4th International Conference on Cloud Networking, CloudNet 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 279-284 7335322 (2015 IEEE 4th International Conference on Cloud Networking, CloudNet 2015).

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

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AB - In this paper we present a Mixed Integer Programming-based (MIP) approach to optimise the workload allocation of geographically distributed Data Centres (DCs) in the face of dynamic DC performances and electricity prices. We reduce the overall electricity cost for running a DC set over an operating horizon by finding a good compromise between: The number of migrations subject to the sovereignty of data, the loads of the servers in DCs and the energy cost reduction possible by following the DCs with best performance and energy efficiencies over time. To model the DC performance we use Power Usage Effectiveness (PUE), with a devoted function per DC dependent on the current outside temperature. We discuss the multiple dimensions of the problem, present a mathematical formulation for it and provide empirical evaluation to claim the improvement on the electricity cost achieved.

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Castineiras I, Mehta D, O'Sullivan B. Energy cost minimisation of geographically distributed data centres. In 2015 IEEE 4th International Conference on Cloud Networking, CloudNet 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 279-284. 7335322. (2015 IEEE 4th International Conference on Cloud Networking, CloudNet 2015). doi: 10.1109/CloudNet.2015.7335322

Energy cost minimisation of geographically distributed data centres

Abstract

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UN SDGs

UCC Futures

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