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，许多公认的节能方案为大数据计算基础架构制定了，这些方案汇总了一些芯片或设备上的大量工作量。尽管这两种方法都减少了能源消耗，但它们也可以在长期具有IT仪器的仪器上升高温度水平，并最终导致它们过热。结果，这些设备的可靠性可以显着降低，如许多最近的研究所示。在最坏的情况下，它们可能失败或故障。在数十亿美元的大数据计算行业中，必须开发新的功率和能源控制解决方案。该研究项目针对系统级解决方案，以实现温度约束的数据中心能源管理。它将通过探索模糊决策技术的使用，开发用于在各种温度限制下运行的高性能数据中心中能源消耗的方法和工具。大多数现有研究都依赖于每个标准之间的完善的关系模型，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，该项目从不同角度追求解决方案：鉴于每个标准都与数据中心中的另一个标准之间存在非线性关系，那么不依赖固定约束的新的无模型控制技术如何成功地工作？如果成功的话，该项目将在节能的环境益处，降低成本的商业影响以及提高仓库规模计算机系统和数据中心的运营效率以及降低数据中心温度以维持可靠性的情况下实现可预见的社会收益。