SHF: Small: Multi-criteria optimization control for temperature constrained energy efficient data center using fuzzy decision making theory

SHF：小型：利用模糊决策理论对温度受限节能数据中心进行多准则优化控制

• 批准号: 1527249
• 负责人: Jun Wang
• 金额: $ 36.91万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527249&HistoricalAwards=false
• 关键词: SHF; Small; Multi; criteria; optimization

Recent years have seen many well-recognized energy conservation schemes developed for big data computing infrastructures, which aggregate heavy workloads on either a few chips or devices. While both of these methods reduce energy consumption, they can also elevate temperature levels on long standing IT instruments and ultimately cause them to overheat. As a consequence, the reliability of these devices can be significantly degraded as shown in many recent studies. In the worst cases, they can fail or malfunction. There is an imperative need for developing new power and energy control solutions in the multibillion-dollar industry of big data computing.This research project is directed towards system-level solutions for temperature constrained data center energy management. It will develop methods and tools for controlling energy consumption in high-performance data centers operating under various temperature constraints by exploring the use of fuzzy decision-making techniques. Most of the existing studies rely on a well-developed relationship model between each criterion including open-loop search and optimization methods and rigid control schemes with fixed temperature constraint assumptions. In contrast, this project pursues solutions from a different angle: given that each criterion is in a non-linear relationship with another in a data center, how can new modeless control techniques that do not rely on fixed constraints work successfully? The project, if successful, will achieve foreseeable societal gains in terms of environmental benefits of energy conservation, potential for commercial impact of reducing costs and increasing operational efficiency in both warehouse-scale computer systems and data centers and reducing the data-center temperature to maintain reliability.

近年来，针对大数据计算基础设施开发了许多广受认可的节能方案，这些方案将繁重的工作负载聚集在少数芯片或设备上。虽然这两种方法都可以降低能耗，但它们也会提高长期使用的 IT 仪器的温度水平，最终导致它们过热。因此，如许多最近的研究所示，这些设备的可靠性可能会显着降低。在最坏的情况下，它们可能会失败或发生故障。在价值数十亿美元的大数据计算行业中，迫切需要开发新的电源和能源控制解决方案。该研究项目针对温度受限数据中心能源管理的系统级解决方案。它将通过探索模糊决策技术的使用来开发方法和工具，用于控制在各种温度限制下运行的高性能数据中心的能耗。大多数现有研究依赖于每个标准之间完善的关系模型，包括开环搜索和优化方法以及具有固定温度约束假设的刚性控制方案。相比之下，该项目从不同的角度寻求解决方案：鉴于数据中心中的每个标准都与另一个标准呈非线性关系，不依赖于固定约束的新型无模式控制技术如何成功工作？该项目如果成功，将在节能的环境效益、降低仓库规模计算机系统和数据中心的成本和提高运营效率以及降低数据中心温度以维持环境的商业影响方面实现可预见的社会收益。可靠性。