Multi-objective optimization of energy and performance management in distributed data centers

被引：0
作者：
Hu C.-Y. [1 ]
Yu G. [1 ]
Yan X.-S. [1 ]
Gong W.-Y. [1 ]
Cai J.-Y. [1 ]
机构：
[1] School of Computer Science, China University of Geosciences (Wuhan), Wuhan
来源：
Kongzhi yu Juece/Control and Decision | 2021年 / 36卷 / 01期
关键词：
Adaptive mutation; Crowding distance; Data center; Multi-objective optimization; Workload scheduling;
D O I：
10.13195/j.kzyjc.2019.0702
中图分类号：
学科分类号：
摘要：
In data center operations, operators need to consider how to maximize profits, reduce carbon emissions and improve service quality. However, the balance between these objectives is a huge challenge, and in practical problems, we need get a group of solutions with good distribution quickly. Aiming at this problems, this paper establishes a multi-objective optimization model for distributed data centers energy and performance management, and proposes an improved adaptive mutation non-dominated sorting genetic algorithm (ICDA-NSGA-Ⅱ) which improves the crowding distance and crossover operator. The crowding distance is improved in order to improve the dispersion and convergence speed of the algorithm based on the NSGA-Ⅱ algorithm. Meanwhile, normal distribution crossover (NDX) operators and adaptive adjustment mutation operators are introduced to enhance the diversity of the population, so that the Pareto solution set can be obtained quickly and accurately. The experimental results on benchmark problems show that the improved algorithm has better convergence and distribution compared with the NSGA-Ⅱ and the MOEA/D, and further results on the model of data centers show that the proposed algorithm can solve this problem quickly and accurately. Copyright ©2021 Control and Decision.
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页码：159 / 165
页数：6
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