云数据中心的热建模和热感知节能方法:综述

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Jianpeng Lin;Weiwei Lin;Huikang Huang;Wenjun Lin;Keqin Li
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引用次数: 0

摘要

建设高能效的云数据中心(CDC)是云计算进一步发展的必由之路。作为数据中心的核心子系统之一,冷却系统在为 IT 设备的安全运行提供可靠热环境的同时,也带来了巨大的能耗和碳排放问题。由此可见,要实现数据中心的绿色低碳发展,优化具有巨大节能潜力的冷却系统的能源管理至关重要。因此,为跟踪数据中心热管理技术的研究进展,本综述将重点关注热建模和热感知节能方法这两项研究工作。首先,综述了风冷和液冷数据中心的各种热建模方法。其次,从热感知 IT 负载调度、冷却系统控制优化以及 IT 和冷却系统联合优化这三个角度,对现有的先进热管理方法进行了全面回顾。最后,我们提出了一些尚未完全解决的热管理开放问题和未来研究方向。本综述旨在为提高冷却能效提供合理建议,进一步推动 CDC 向低能耗和可持续方向转型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Thermal Modeling and Thermal-Aware Energy Saving Methods for Cloud Data Centers: A Review
Constructing energy-efficient cloud data centers (CDCs) is an essential path for the further expansion of cloud computing. As one of the core subsystems of a data center, the cooling system provides a reliable thermal environment for the safe operation of IT equipment while posing a huge energy consumption and carbon emission problem. Thus, it is evident that optimizing energy management of cooling systems with considerable energy-saving potential will be essential to realize the green and low-carbon development of CDCs. Therefore, to track the research progress of data center thermal management technologies, this review focuses on two research efforts: thermal modeling and thermal-aware energy saving methods. First, various thermal modeling approaches are reviewed for air-cooled and liquid-cooled data centers. Secondly, a comprehensive review of existing advanced thermal management approaches is conducted from three perspectives: thermal-aware IT load scheduling, cooling system control optimization, and joint optimization of the IT and cooling systems. Finally, we put forward some open issues and future research directions for thermal management that have not been completely solved. This review aims to provide reasonable suggestions to enhance cooling energy efficiency and further promote the transformation of CDCs to lower energy consumption and sustainable direction.
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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