Geographical Scheduling of Multi-application Tasks for Cost Minimization in Distributed Green Data Centers

Jing Bi; Haitao Yuan; Mengchu Zhou

doi:10.1109/SMC.2018.00537

Geographical Scheduling of Multi-application Tasks for Cost Minimization in Distributed Green Data Centers

Jing Bi
, Haitao Yuan
, Mengchu Zhou

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

3 Scopus citations

Abstract

The infrastructure resources in distributed green data centers (DGDCs) are shared by multiple heterogeneous applications to provide flexible services to global users in a high-performance and low-cost way. It is highly challenging to minimize the total cost of a DGDC provider in a market where bandwidth price of Internet service providers (ISPs), electricity price and the availability of renewable green energy all vary with geographic locations. Unlike existing studies, a Geographical Scheduling method of Multi-Application Tasks (GSMAT) that exploits spatial diversity in DGDCs is proposed to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks' delay bound constraints. In each time slot, the cost minimization problem for DGDCs is formulated as a constrained optimization one and solved by the proposed Simulated-annealing-based Bat Algorithm (SBA). Trace-driven experiments demonstrate that GSMAT achieves lower cost and higher throughput than two typical scheduling methods.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3171-3176
Number of pages	6
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00537
State	Published - 2 Jul 2018
Externally published	Yes
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: 7 Oct 2018 → 10 Oct 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/Territory	Japan
City	Miyazaki
Period	7/10/18 → 10/10/18

UN SDGs

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

SDG 3 Good Health and Well-being
SDG 7 Affordable and Clean Energy

Keywords

Green data centers
bat algorithm
cost minimization
distributed computing
hybrid meta-heuristic optimization
simulated annealing
task scheduling

Access to Document

10.1109/SMC.2018.00537

Cite this

Bi, J., Yuan, H., & Zhou, M. (2018). Geographical Scheduling of Multi-application Tasks for Cost Minimization in Distributed Green Data Centers. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 3171-3176). Article 8616534 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00537

Bi, Jing ; Yuan, Haitao ; Zhou, Mengchu. / Geographical Scheduling of Multi-application Tasks for Cost Minimization in Distributed Green Data Centers. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 3171-3176 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

@inproceedings{3ef8a850137948e69fd00653ad4c4e2c,

title = "Geographical Scheduling of Multi-application Tasks for Cost Minimization in Distributed Green Data Centers",

abstract = "The infrastructure resources in distributed green data centers (DGDCs) are shared by multiple heterogeneous applications to provide flexible services to global users in a high-performance and low-cost way. It is highly challenging to minimize the total cost of a DGDC provider in a market where bandwidth price of Internet service providers (ISPs), electricity price and the availability of renewable green energy all vary with geographic locations. Unlike existing studies, a Geographical Scheduling method of Multi-Application Tasks (GSMAT) that exploits spatial diversity in DGDCs is proposed to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks' delay bound constraints. In each time slot, the cost minimization problem for DGDCs is formulated as a constrained optimization one and solved by the proposed Simulated-annealing-based Bat Algorithm (SBA). Trace-driven experiments demonstrate that GSMAT achieves lower cost and higher throughput than two typical scheduling methods.",

keywords = "Green data centers, bat algorithm, cost minimization, distributed computing, hybrid meta-heuristic optimization, simulated annealing, task scheduling",

author = "Jing Bi and Haitao Yuan and Mengchu Zhou",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/SMC.2018.00537",

language = "英语",

series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3171--3176",

booktitle = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

address = "美国",

}

Bi, J, Yuan, H & Zhou, M 2018, Geographical Scheduling of Multi-application Tasks for Cost Minimization in Distributed Green Data Centers. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616534, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 3171-3176, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 7/10/18. https://doi.org/10.1109/SMC.2018.00537

Geographical Scheduling of Multi-application Tasks for Cost Minimization in Distributed Green Data Centers. / Bi, Jing; Yuan, Haitao; Zhou, Mengchu.
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3171-3176 8616534 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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

TY - GEN

T1 - Geographical Scheduling of Multi-application Tasks for Cost Minimization in Distributed Green Data Centers

AU - Bi, Jing

AU - Yuan, Haitao

AU - Zhou, Mengchu

PY - 2018/7/2

Y1 - 2018/7/2

N2 - The infrastructure resources in distributed green data centers (DGDCs) are shared by multiple heterogeneous applications to provide flexible services to global users in a high-performance and low-cost way. It is highly challenging to minimize the total cost of a DGDC provider in a market where bandwidth price of Internet service providers (ISPs), electricity price and the availability of renewable green energy all vary with geographic locations. Unlike existing studies, a Geographical Scheduling method of Multi-Application Tasks (GSMAT) that exploits spatial diversity in DGDCs is proposed to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks' delay bound constraints. In each time slot, the cost minimization problem for DGDCs is formulated as a constrained optimization one and solved by the proposed Simulated-annealing-based Bat Algorithm (SBA). Trace-driven experiments demonstrate that GSMAT achieves lower cost and higher throughput than two typical scheduling methods.

AB - The infrastructure resources in distributed green data centers (DGDCs) are shared by multiple heterogeneous applications to provide flexible services to global users in a high-performance and low-cost way. It is highly challenging to minimize the total cost of a DGDC provider in a market where bandwidth price of Internet service providers (ISPs), electricity price and the availability of renewable green energy all vary with geographic locations. Unlike existing studies, a Geographical Scheduling method of Multi-Application Tasks (GSMAT) that exploits spatial diversity in DGDCs is proposed to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks' delay bound constraints. In each time slot, the cost minimization problem for DGDCs is formulated as a constrained optimization one and solved by the proposed Simulated-annealing-based Bat Algorithm (SBA). Trace-driven experiments demonstrate that GSMAT achieves lower cost and higher throughput than two typical scheduling methods.

KW - Green data centers

KW - bat algorithm

KW - cost minimization

KW - distributed computing

KW - hybrid meta-heuristic optimization

KW - simulated annealing

KW - task scheduling

UR - https://www.scopus.com/pages/publications/85062239183

U2 - 10.1109/SMC.2018.00537

DO - 10.1109/SMC.2018.00537

M3 - 会议稿件

AN - SCOPUS:85062239183

T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

SP - 3171

EP - 3176

BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Y2 - 7 October 2018 through 10 October 2018

ER -

Bi J, Yuan H, Zhou M. Geographical Scheduling of Multi-application Tasks for Cost Minimization in Distributed Green Data Centers. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 3171-3176. 8616534. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). doi: 10.1109/SMC.2018.00537

Geographical Scheduling of Multi-application Tasks for Cost Minimization in Distributed Green Data Centers

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this