TY - JOUR
T1 - Spatial Task Scheduling for Cost Minimization in Distributed Green Cloud Data Centers
AU - Yuan, Haitao
AU - Bi, Jing
AU - Zhou, Mengchu
N1 - Funding Information:
Manuscript received June 6, 2018; accepted July 14, 2018. Date of publication August 6, 2018; date of current version April 5, 2019. This paper was recommended for publication by Associate Editor A. E. Smith and Editor L. Shi upon evaluation of the reviewers’ comments. This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2016RC030, in part by the China Postdoctoral Science Foundation under Grant 2017T100034 and Grant 2016M600912, and in part by the National Natural Science Foundation of China under Grant 61703011. The work of H. Yuan was supported by China Scholarship Council. (Corresponding author: Jing Bi.) H. Yuan is with the School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China (e-mail: htyuan@bjtu.edu.cn).
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - The infrastructure resources in distributed green cloud data centers (DGCDCs) 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 DGCDC provider in a market, where bandwidth prices of Internet service providers (ISPs), electricity prices, and the availability of renewable green energy all vary with geographical locations. Unlike existing studies, this paper proposes a spatial task scheduling and resource optimization (STSRO) method to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks' delay-bound constraints. STSRO well exploits spatial diversity in DGCDCs. In each time slot, the cost minimization problem for DGCDCs is formulated as a constrained optimization one and solved by the proposed simulated annealing-based bat algorithm (SBA). Trace-driven experiments demonstrate that STSRO achieves lower total cost and higher throughput than two typical scheduling methods. Note to Practitioners - This paper investigates the cost minimization problem for DGCDCs while meeting delay-bound constraints for all arriving tasks. Previous task scheduling methods do not jointly investigate the spatial diversity in bandwidth prices of ISPs, electricity prices, and the availability of renewable green energy. Therefore, they fail to cost-effectively schedule all arriving tasks of heterogeneous applications during their delay-bound constraints. In this paper, a new method that overcomes the shortcomings of the existing methods is proposed. It is obtained by using the proposed SBA that solves a constrained optimization problem. Simulation results demonstrate that compared with two typical scheduling methods, it increases the throughput and decreases the cost. It can be readily implemented and integrated into real-world industrial DGCDCs. The future work needs to investigate the indeterminacy of renewable energy and the uncertainty in arriving tasks with rough deep neural network approaches on STSRO.
AB - The infrastructure resources in distributed green cloud data centers (DGCDCs) 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 DGCDC provider in a market, where bandwidth prices of Internet service providers (ISPs), electricity prices, and the availability of renewable green energy all vary with geographical locations. Unlike existing studies, this paper proposes a spatial task scheduling and resource optimization (STSRO) method to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks' delay-bound constraints. STSRO well exploits spatial diversity in DGCDCs. In each time slot, the cost minimization problem for DGCDCs is formulated as a constrained optimization one and solved by the proposed simulated annealing-based bat algorithm (SBA). Trace-driven experiments demonstrate that STSRO achieves lower total cost and higher throughput than two typical scheduling methods. Note to Practitioners - This paper investigates the cost minimization problem for DGCDCs while meeting delay-bound constraints for all arriving tasks. Previous task scheduling methods do not jointly investigate the spatial diversity in bandwidth prices of ISPs, electricity prices, and the availability of renewable green energy. Therefore, they fail to cost-effectively schedule all arriving tasks of heterogeneous applications during their delay-bound constraints. In this paper, a new method that overcomes the shortcomings of the existing methods is proposed. It is obtained by using the proposed SBA that solves a constrained optimization problem. Simulation results demonstrate that compared with two typical scheduling methods, it increases the throughput and decreases the cost. It can be readily implemented and integrated into real-world industrial DGCDCs. The future work needs to investigate the indeterminacy of renewable energy and the uncertainty in arriving tasks with rough deep neural network approaches on STSRO.
KW - Bat algorithm
KW - cost minimization
KW - distributed computing
KW - green data centers
KW - hybrid metaheuristic optimization
KW - simulated annealing (SA)
KW - task scheduling
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U2 - 10.1109/TASE.2018.2857206
DO - 10.1109/TASE.2018.2857206
M3 - Article
AN - SCOPUS:85059111353
SN - 1545-5955
VL - 16
SP - 729
EP - 740
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 2
M1 - 8425724
ER -