Green-aware online resource allocation for geo-distributed cloud data centers on multi-source energy

Huaiwen He; Hong Shen

doi:10.1109/PDCAT.2016.037

Green-aware online resource allocation for geo-distributed cloud data centers on multi-source energy

Huaiwen He
, Hong Shen

Sun Yat-Sen University
University of Electronic Science and Technology of China
University of Adelaide

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

7 Citations (Scopus)

Abstract

Huge energy consumption of large-scale cloud data centers damages the environment with excessive carbon emission. More and more data center operators are seeking to reduce carbon footprint via various types of renewable energy sources. However, the intermittent availability of renewable energy source makes it quite challenging to cooperate the dynamic workload arrivals. In this paper, we investigate how to coordinate multi-Type renewable energy (e.g. wind power and solar power) in order to reduce the long-Term energy cost with spatio-Temporal diversity of electricity price for geo-distributed cloud data centers under the constraints of service level agreement (SLA) and carbon footprints. To tackle the randomness of workload arrival, dynamic electricity price change and renewable energy generation, we first formulate the minimizing energy cost problem into a constrained stochastic optimization problem. Then, based on Lyapunov optimization technique, we design an online control algorithm which can work without long-Term future system information for solving the problem. Finally, we evaluate the effectiveness of the algorithm with extensive simulations based on real-world workload traces, electricity price and historic climate data.

Original language	English
Title of host publication	Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016
Editors	Hong Shen, Hong Shen, Yingpeng Sang, Hui Tian
Publisher	IEEE Computer Society
Pages	113-118
Number of pages	6
ISBN (Electronic)	9781509050819
DOIs	https://doi.org/10.1109/PDCAT.2016.037
Publication status	Published - 2 Jul 2016
Externally published	Yes
Event	17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016 - Guangzhou, China Duration: 16 Dec 2016 → 18 Dec 2016

Publication series

Name	Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings
Volume	0

Conference

Conference	17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016
Country/Territory	China
City	Guangzhou
Period	16/12/16 → 18/12/16

UN SDGs

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

SDG 7 Affordable and Clean Energy
SDG 13 Climate Action

Access to Document

10.1109/PDCAT.2016.037

Cite this

He, H., & Shen, H. (2016). Green-aware online resource allocation for geo-distributed cloud data centers on multi-source energy. In H. Shen, H. Shen, Y. Sang, & H. Tian (Eds.), Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016 (pp. 113-118). Article 7943342 (Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings; Vol. 0). IEEE Computer Society. https://doi.org/10.1109/PDCAT.2016.037

He, Huaiwen ; Shen, Hong. / Green-aware online resource allocation for geo-distributed cloud data centers on multi-source energy. Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016. editor / Hong Shen ; Hong Shen ; Yingpeng Sang ; Hui Tian. IEEE Computer Society, 2016. pp. 113-118 (Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings).

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title = "Green-aware online resource allocation for geo-distributed cloud data centers on multi-source energy",

abstract = "Huge energy consumption of large-scale cloud data centers damages the environment with excessive carbon emission. More and more data center operators are seeking to reduce carbon footprint via various types of renewable energy sources. However, the intermittent availability of renewable energy source makes it quite challenging to cooperate the dynamic workload arrivals. In this paper, we investigate how to coordinate multi-Type renewable energy (e.g. wind power and solar power) in order to reduce the long-Term energy cost with spatio-Temporal diversity of electricity price for geo-distributed cloud data centers under the constraints of service level agreement (SLA) and carbon footprints. To tackle the randomness of workload arrival, dynamic electricity price change and renewable energy generation, we first formulate the minimizing energy cost problem into a constrained stochastic optimization problem. Then, based on Lyapunov optimization technique, we design an online control algorithm which can work without long-Term future system information for solving the problem. Finally, we evaluate the effectiveness of the algorithm with extensive simulations based on real-world workload traces, electricity price and historic climate data.",

author = "Huaiwen He and Hong Shen",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016 ; Conference date: 16-12-2016 Through 18-12-2016",

year = "2016",

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language = "English",

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He, H & Shen, H 2016, Green-aware online resource allocation for geo-distributed cloud data centers on multi-source energy. in H Shen, H Shen, Y Sang & H Tian (eds), Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016., 7943342, Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, vol. 0, IEEE Computer Society, pp. 113-118, 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016, Guangzhou, China, 16/12/16. https://doi.org/10.1109/PDCAT.2016.037

Green-aware online resource allocation for geo-distributed cloud data centers on multi-source energy. / He, Huaiwen; Shen, Hong.
Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016. ed. / Hong Shen; Hong Shen; Yingpeng Sang; Hui Tian. IEEE Computer Society, 2016. p. 113-118 7943342 (Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings; Vol. 0).

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

TY - GEN

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AU - He, Huaiwen

AU - Shen, Hong

PY - 2016/7/2

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N2 - Huge energy consumption of large-scale cloud data centers damages the environment with excessive carbon emission. More and more data center operators are seeking to reduce carbon footprint via various types of renewable energy sources. However, the intermittent availability of renewable energy source makes it quite challenging to cooperate the dynamic workload arrivals. In this paper, we investigate how to coordinate multi-Type renewable energy (e.g. wind power and solar power) in order to reduce the long-Term energy cost with spatio-Temporal diversity of electricity price for geo-distributed cloud data centers under the constraints of service level agreement (SLA) and carbon footprints. To tackle the randomness of workload arrival, dynamic electricity price change and renewable energy generation, we first formulate the minimizing energy cost problem into a constrained stochastic optimization problem. Then, based on Lyapunov optimization technique, we design an online control algorithm which can work without long-Term future system information for solving the problem. Finally, we evaluate the effectiveness of the algorithm with extensive simulations based on real-world workload traces, electricity price and historic climate data.

AB - Huge energy consumption of large-scale cloud data centers damages the environment with excessive carbon emission. More and more data center operators are seeking to reduce carbon footprint via various types of renewable energy sources. However, the intermittent availability of renewable energy source makes it quite challenging to cooperate the dynamic workload arrivals. In this paper, we investigate how to coordinate multi-Type renewable energy (e.g. wind power and solar power) in order to reduce the long-Term energy cost with spatio-Temporal diversity of electricity price for geo-distributed cloud data centers under the constraints of service level agreement (SLA) and carbon footprints. To tackle the randomness of workload arrival, dynamic electricity price change and renewable energy generation, we first formulate the minimizing energy cost problem into a constrained stochastic optimization problem. Then, based on Lyapunov optimization technique, we design an online control algorithm which can work without long-Term future system information for solving the problem. Finally, we evaluate the effectiveness of the algorithm with extensive simulations based on real-world workload traces, electricity price and historic climate data.

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ER -

He H, Shen H. Green-aware online resource allocation for geo-distributed cloud data centers on multi-source energy. In Shen H, Shen H, Sang Y, Tian H, editors, Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016. IEEE Computer Society. 2016. p. 113-118. 7943342. (Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings). doi: 10.1109/PDCAT.2016.037

Green-aware online resource allocation for geo-distributed cloud data centers on multi-source energy

Abstract

Publication series

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UN SDGs

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