A Deep Reinforcement Learning-Based Power Resource Management for Fuel Cell Powered Data Centers

被引：3

作者：

Hu, Xiaoxuan ^{[1
]}

Sun, Yanfei ^{[2
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Sch Automat, Nanjing 210003, Peoples R China

[2] Nanjing Univ Posts & Telecommun, Sch Internet Things, Nanjing 210003, Peoples R China

来源：

ELECTRONICS | 2020年 / 9卷 / 12期

基金：

中国国家自然科学基金;

关键词：

green data center; deep reinforcement Learning; fuel cell; workload scheduling; ENERGY-COST MINIMIZATION; INTERNET DATA CENTERS;

D O I：

10.3390/electronics9122054

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the increase of data storage demands, the energy consumption of data centers is also increasing. Energy saving and use of power resources are two key problems to be solved. In this paper, we introduce the fuel cells as the energy supply and study power resource use in data center power grids. By considering the limited load following of fuel cells and power budget fragmentation phenomenon, we transform the main two objectives into the optimization of workload distribution problem and use a deep reinforcement learning-based method to solve it. The evaluations with real-world traces demonstrate the better performance of this work over state-of-art approaches.

引用

页码：1 / 14

页数：14

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