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

近年来，针对大数据计算基础设施开发了许多公认的节能方案，这些基础设施将繁重的工作负载聚集在几个芯片或设备上。虽然这两种方法都减少了能源消耗，但它们也会提高长期使用的IT仪器的温度水平，并最终导致它们过热。因此，正如许多最近的研究所显示的那样，这些设备的可靠性可能会大大降低。在最坏的情况下，它们可能会失败或故障。在数十亿美元的大数据计算行业中，迫切需要开发新的电力和能源控制解决方案。该研究项目旨在为温度受限的数据中心能源管理提供系统级解决方案。它将通过探索模糊决策技术的使用，开发在各种温度约束下运行的高性能数据中心控制能耗的方法和工具。现有的研究大多依赖于每个准则之间的关系模型，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，本项目从不同的角度寻求解决方案：考虑到数据中心中的每个标准都与另一个标准处于非线性关系，不依赖于固定约束的新型非模态控制技术如何成功工作？该项目如果成功，将在节约能源的环境效益、降低成本和提高仓库级计算机系统和数据中心的操作效率的潜在商业影响以及降低数据中心温度以保持可靠性方面取得可预见的社会收益。

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.