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清华大学学报(自然科学版)  2022, Vol. 62 Issue (12): 1864-1874    DOI: 10.16511/j.cnki.qhdxxb.2022.21.016
  信息科学 本期目录 | 过刊浏览 | 高级检索 |
分布式数据中心信息能量协同优化策略
刘迪1, 曹军威2, 刘明爽3
1. 清华大学 自动化系, 北京 100084;
2. 清华大学 北京信息科学与技术国家研究中心, 北京 100084;
3. 深圳市腾讯计算机系统有限公司, 深圳 518057
Collaborative optimization strategy of information and energy for distributed data centers
LIU Di1, CAO Junwei2, LIU Mingshuang3
1. Department of Automation, Tsinghua University, Beijing 100084, China;
2. Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China;
3. Shenzhen Tencent Computer System Co., Ltd., Shenzhen 518057, China
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摘要 随着数据中心规模的不断扩大, 其能耗巨大的问题也日益突出。分布式数据中心既可以通过计算任务在多个数据中心间的分配实现功率的转移, 也可以通过单个数据中心的功率控制实现功耗和计算时延的均衡。这2种优化手段相互耦合, 且面临着来自于信息层和能量层的多元不确定性的影响, 需要快速可靠的控制手段实现数据中心信息层和能量层的协同优化。该文首先构建了分布式数据中心协同优化调节架构, 并分析了多数据中心计算任务分配与单数据中心功率优化的动态特性。其次, 构建了基于动态微分方程的信息层和能量层耦合优化问题的统一调节模型。最后, 综合考虑系统运营成本及计算时延构建目标函数, 引入最优控制理论对该问题求解, 实现数据中心信息能量的秒级协同优化控制。仿真结果表明, 相比分钟级的控制, 基于该策略的快速控制能够较好的追踪可再生能源出力以及计算任务的波动, 从而有效提升系统的经济效益及可再生能源就地消纳率。
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刘迪
曹军威
刘明爽
关键词 分布式数据中心微分方程协同优化可再生能源最优控制    
Abstract:With the continuous expansion of data centers, the problem of large energy consumption has become increasingly prominent. Distributed data centers can enable power transfer through the distribution of computing tasks among multiple data centers and realize the balance between power consumption and computing delay through the power control of a single data center. Scheduling of computing tasks and power control of data center interact with each other, and their control effects are affected by multiple uncertainties. Therefore, a fast and reliable control method is required for realizing the collaborative optimization of the information and energy layers of the data center. First, a distributed data center collaborative optimization architecture is constructed. Then, the dynamic characteristics of multiple data center computing task allocation and single data center power optimization are analyzed based on the dynamic differential equation, and a unified adjustment model of the coupling optimization problem is constructed. Given the system operating cost and computing delay in constructing the objective function, the optimal control theory is introduced to solve the problem and realize the second-level collaborative optimal control of the information energy of the data center. Simulation results show that the high-frequency control based on the proposed algorithm can better track the fluctuation of renewable energy output and calculation tasks than the minute-level control and effectively improve the economic benefits of the system and the local consumption rate of renewable energy.
Key wordsdistributed data center    differential equation    collaborative optimization    renewable energy    optimal control
收稿日期: 2022-01-18      出版日期: 2022-11-10
基金资助:曹军威, 研究员, E-mail:jcao@tsinghua.edu.cn
引用本文:   
刘迪, 曹军威, 刘明爽. 分布式数据中心信息能量协同优化策略[J]. 清华大学学报(自然科学版), 2022, 62(12): 1864-1874.
LIU Di, CAO Junwei, LIU Mingshuang. Collaborative optimization strategy of information and energy for distributed data centers. Journal of Tsinghua University(Science and Technology), 2022, 62(12): 1864-1874.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2022.21.016  或          http://jst.tsinghuajournals.com/CN/Y2022/V62/I12/1864
  
  
  
  
  
  
  
  
  
  
  
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