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
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
收藏
页码:1 / 14
页数:14
相关论文
共 50 条
  • [21] Deep Reinforcement Learning-Based Detection Framework for False Data Injection Attacks in Power Systems
    Prabhu, T. N.
    Ranjeethkumar, C.
    Mohankumar, B.
    Rajaram, A.
    INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH, 2024, 14 (02): : 311 - 323
  • [22] Deep Reinforcement Learning-based Power Allocation in Uplink Cell-Free Massive MIMO
    Rahmani, Mostafa
    Bashar, Manijeh
    Dehghani, Mohammad J.
    Xiao, Pei
    Tafazolli, Rahim
    Debbah, Merouane
    2022 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), 2022, : 459 - 464
  • [23] Deep Reinforcement Learning-based resource allocation strategy for Energy Harvesting-Powered Cognitive Machine-to-Machine Networks
    Xu, Yi-Han
    Tian, Yong-Bo
    Searyoh, Prosper Komla
    Yu, Gang
    Yong, Yueh-Tiam
    COMPUTER COMMUNICATIONS, 2020, 160 : 706 - 717
  • [24] Deep Reinforcement Learning-Based Optimal Parameter Design of Power Converters
    Bui, Van-Hai
    Chang, Fangyuan
    Su, Wencong
    Wang, Mengqi
    Murphey, Yi Lu
    Da Silva, Felipe Leno
    Huang, Can
    Xue, Lingxiao
    Glatt, Ruben
    2023 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS, ICNC, 2023, : 25 - 29
  • [25] Deep reinforcement learning and fuzzy logic controller codesign for energy management of hydrogen fuel cell powered electric vehicles
    Rostami, Seyed Mehdi Rakhtala
    Al-Shibaany, Zeyad
    Kay, Peter
    Karimi, Hamid Reza
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [26] Deep reinforcement learning-based energy management strategy for fuel cell buses integrating future road information and cabin comfort control
    Jia, Chunchun
    Liu, Wei
    He, Hongwen
    Chau, K. T.
    ENERGY CONVERSION AND MANAGEMENT, 2024, 321
  • [27] Deep Reinforcement Learning-Based Resource Management for UAV-Assisted Mobile Edge Computing Against Jamming
    Shao, Ziling
    Yang, Helin
    Xiao, Liang
    Su, Wei
    Chen, Yifan
    Xiong, Zehui
    IEEE TRANSACTIONS ON MOBILE COMPUTING, 2024, 23 (12) : 13358 - 13374
  • [28] Multi-Slot Secure Offloading and Resource Management in VEC Networks: A Deep Reinforcement Learning-Based Method
    Li, Zhen
    Gong, Jialong
    Xiong, Xiong
    Wang, Dong
    IEEE ACCESS, 2025, 13 : 4533 - 4546
  • [29] Energy management optimization of fuel cell hybrid electric vehicle based on deep reinforcement learning
    Wang, Hao-Cong
    Wang, Yue-Yang
    Fu, Zhu-Mu
    Chen, Qi-Hong
    Tao, Fa-Zhan
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2024, 41 (10): : 1831 - 1841
  • [30] Energy Management of Data Centers Powered by Fuel Cells and Heterogeneous Energy Storage
    Hu, Xiaoxuan
    Li, Peng
    Wang, Kun
    Sun, Yanfei
    Zeng, Deze
    Guo, Song
    2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2018,