TY - JOUR
T1 - Time-Aware Multi-Application Task Scheduling with Guaranteed Delay Constraints in Green Data Center
AU - Yuan, Haitao
AU - Bi, Jing
AU - Zhou, Mengchu
AU - Ammari, Ahmed Chiheb
N1 - Funding Information:
Manuscript received April 27, 2017; revised June 25, 2017; accepted August 7, 2017. Date of publication September 21, 2017; date of current version July 2, 2018. This paper was recommended for publication by Associate Editor J. Civera and Editor M. P. Fanti upon evaluation of the reviewers’ comments. This work was supported in part by the National Natural Science Foundation of China under Grant 61703011, in part by the China Postdoctoral Science Foundation under Grant 2016M600912, in part by the China Postdoctoral Science Foundation under Grant 2017T100034, and in part by the High Dynamic Navigation Technology Beijing Key Laboratory under Grant HDN2017101. (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 - 2018/7
Y1 - 2018/7
N2 - A growing number of companies deploy their applications in green data centers (GDCs) and provide services to tasks of global users. Currently, a growing number of GDC providers aim to maximize their profit by deploying green energy facilities and decreasing brown energy consumption. However, the temporal variation in the revenue, price of grid, and green energy in tasks' delay bounds makes it challenging for GDC providers to achieve profit maximization while strictly guaranteeing delay constraints of all admitted tasks. Unlike existing studies, a time-aware task scheduling (TATS) algorithm that investigates the temporal variation and schedules all admitted tasks to execute in GDC meeting their delay bounds is proposed. In addition, this paper provides the mathematical modeling of task refusal and service rates. In each iteration, TATS solves the formulated profit maximization problem by hybrid chaotic particle swarm optimization based on simulated annealing. Compared with several existing scheduling algorithms, TATS can increase profit and throughput without violating delay constraints of all admitted tasks. Note to Practitioners - This paper investigates the profit maximization problem for a green data center (GDC) while meeting delay constraints for all admitted tasks. Previous task scheduling algorithms do not jointly investigate temporal variation in revenue, green energy, and price of grid. Thus, they fail to meet the delay constraints of all admitted tasks. In this paper, a new approach that overcomes drawbacks of existing algorithms is proposed. It is obtained by using a hybrid metaheuristic algorithm that solves a constrained nonlinear optimization problem. Simulation results show that compared with several existing algorithms, it increases both throughput and profit. It can be readily incorporated into real-life industrial GDCs. The future work needs to investigate the repair/failure effect of GDCs on the proposed time-aware task scheduling.
AB - A growing number of companies deploy their applications in green data centers (GDCs) and provide services to tasks of global users. Currently, a growing number of GDC providers aim to maximize their profit by deploying green energy facilities and decreasing brown energy consumption. However, the temporal variation in the revenue, price of grid, and green energy in tasks' delay bounds makes it challenging for GDC providers to achieve profit maximization while strictly guaranteeing delay constraints of all admitted tasks. Unlike existing studies, a time-aware task scheduling (TATS) algorithm that investigates the temporal variation and schedules all admitted tasks to execute in GDC meeting their delay bounds is proposed. In addition, this paper provides the mathematical modeling of task refusal and service rates. In each iteration, TATS solves the formulated profit maximization problem by hybrid chaotic particle swarm optimization based on simulated annealing. Compared with several existing scheduling algorithms, TATS can increase profit and throughput without violating delay constraints of all admitted tasks. Note to Practitioners - This paper investigates the profit maximization problem for a green data center (GDC) while meeting delay constraints for all admitted tasks. Previous task scheduling algorithms do not jointly investigate temporal variation in revenue, green energy, and price of grid. Thus, they fail to meet the delay constraints of all admitted tasks. In this paper, a new approach that overcomes drawbacks of existing algorithms is proposed. It is obtained by using a hybrid metaheuristic algorithm that solves a constrained nonlinear optimization problem. Simulation results show that compared with several existing algorithms, it increases both throughput and profit. It can be readily incorporated into real-life industrial GDCs. The future work needs to investigate the repair/failure effect of GDCs on the proposed time-aware task scheduling.
KW - Cloud data center
KW - delay bounded tasks
KW - green computing
KW - hybrid optimization
KW - metaheuristic
KW - profit maximization
KW - resource provisioning
KW - task scheduling
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U2 - 10.1109/TASE.2017.2741965
DO - 10.1109/TASE.2017.2741965
M3 - Article
AN - SCOPUS:85030771789
SN - 1545-5955
VL - 15
SP - 1138
EP - 1151
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 3
ER -