TY - GEN
T1 - Temporal-variation-aware profit-maximized and delay-bounded task scheduling in green data center
AU - Yuan, Haitao
AU - Bi, Jing
AU - Zhou, Meng Chu
N1 - Funding Information:
Acknowledgment. This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 61802015 and 61703011, in part by the Major Science and Technology Program for Water Pollution Control and Treatment of China under Grant 2018ZX07111005, and in part by the National Defense Pre-Research Foundation of China under Grants 41401020401 and 41401050102.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - An increasing number of enterprises deploy their business applications in green data centers (GDCs) to address irregular and drastic natures in task arrival of global users. GDCs aim to schedule tasks in the most cost-effective way, and achieve the profit maximization by increasing green energy usage and reducing brown one. However, prices of power grid, revenue, solar and wind energy vary dynamically within tasks’ delay constraints, and this brings a high challenge to maximize the profit of GDCs such that their delay constraints are strictly met. Different from existing studies, a Temporal-variation-aware Profit-maximized Task Scheduling (TPTS) algorithm is proposed to consider dynamic differences, and intelligently schedule all tasks to GDCs within their delay constraints. In each interval, TPTS solves a constrained profit maximization problem by a novel Simulated-annealing-based Chaotic Particle swarm optimization (SCP). Compared to several state-of-the-art scheduling algorithms, TPTS significantly increases throughput and profit while strictly meeting tasks’ delay constraints.
AB - An increasing number of enterprises deploy their business applications in green data centers (GDCs) to address irregular and drastic natures in task arrival of global users. GDCs aim to schedule tasks in the most cost-effective way, and achieve the profit maximization by increasing green energy usage and reducing brown one. However, prices of power grid, revenue, solar and wind energy vary dynamically within tasks’ delay constraints, and this brings a high challenge to maximize the profit of GDCs such that their delay constraints are strictly met. Different from existing studies, a Temporal-variation-aware Profit-maximized Task Scheduling (TPTS) algorithm is proposed to consider dynamic differences, and intelligently schedule all tasks to GDCs within their delay constraints. In each interval, TPTS solves a constrained profit maximization problem by a novel Simulated-annealing-based Chaotic Particle swarm optimization (SCP). Compared to several state-of-the-art scheduling algorithms, TPTS significantly increases throughput and profit while strictly meeting tasks’ delay constraints.
KW - Chaotic search
KW - Green computing
KW - Hybrid clouds
KW - Particle swarm optimization
KW - Profit maximization
KW - Simulated annealing
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U2 - 10.1007/978-3-030-34914-1_20
DO - 10.1007/978-3-030-34914-1_20
M3 - Conference contribution
AN - SCOPUS:85075874341
SN - 9783030349134
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 203
EP - 212
BT - Internet and Distributed Computing Systems 12th International Conference, IDCS 2019, Proceedings
A2 - Montella, Raffaele
A2 - Ciaramella, Angelo
A2 - Fortino, Giancarlo
A2 - Guerrieri, Antonio
A2 - Liotta, Antonio
PB - Springer
T2 - 12th International Conference on Internet and Distributed Computing Systems, IDCS 2019
Y2 - 10 October 2019 through 12 October 2019
ER -