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CAREER: Data-Driven Hardware and Software Techniques to Enable Sustainable Data Center Services

CAREER: Data-Driven Hardware and Software Techniques to Enable Sustainable Data Center Services
职业:数据驱动的硬件和软件技术,以实现可持续的数据中心服务
批准号:
2340042
负责人:
Akshitha Sriraman
金额:
$58.33万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2024
资助国家:
美国
项目状态:
未结题
起止时间:
2024-03-01 至 2029-02-28

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中文摘要
翻译
在支持不断升级的计算需求的同时,实现环境上可持续的数据中心系统已成为我们这个时代的一项“重大挑战”。遗憾的是,减少排放对于现代数据中心应用程序来说尤其具有挑战性,因为它们的设计通常将性能和可扩展性放在首位,这会导致(1)执行无关工作和(2)需要更多计算硬件带来的排放增加。为了在实现性能和可扩展性目标的同时减少排放,该项目将利用数据驱动技术从头开始从根本上重新思考跨层硬件-软件数据中心计算堆栈,并将碳效率放在首位。该项目还包括一项全面的教育和推广计划,该计划与研究无缝结合。重点包括:(A)在高中、本科和研究生课程中增加新的可持续发展单元;(B)在学术会议上举办以可持续发展为重点的讲习班和教程;以及(C)让代表不足群体的学生参与可持续发展研究。因此,该项目的直接影响是通过重新定义以可持续发展为优先的方式设计、管理和教授未来数据中心系统的方式来减少碳排放以应对气候变化。该项目旨在减少微服务造成的排放,通过可持续发展的视角系统地研究微服务。该项目的关键创新是从根本上重新设计了硬件-软件系统堆栈,其中包括碳感知硬件和利用此类硬件的新型软件系统,同时实现了微服务的可扩展性目标。在硬件方面,这项工作将采取一种新的方法,利用减少和重复使用的可持续发展原则,在服务器、设备组件和微体系结构级别系统地设计碳感知硬件。首先,这项工作将确定何时重新使用较旧的服务器来运行延迟容忍的微服务。其次,为了减少设备,本工作将开发一个工具,只加速关键的通用原语,并分析资源浪费,以设计减少的组件。第三,这项工作将设计更精简的微体系结构。在软件方面,这项工作将开发一个新颖的碳感知数据驱动的资源管理框架,以实现每个硬件级别的碳效率设计。通过追求数据驱动的技术,这项工作将突出使用数据中心的始终在线遥测来实现其设计的价值。这项跨学科的研究涉及三个领域:计算机体系结构、软件系统和新的应用范例,为可持续计算的跨学科工作开辟了新的机会。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Enabling environmentally sustainable data center systems while supporting escalating computing needs has emerged as a “grand challenge” of our times. Unfortunately, reducing emissions is especially challenging for modern data center applications, since their design often prioritizes performance and scalability, causing increased emissions from (1) performing extraneous work and (2) requiring more computing hardware. To reduce emissions while achieving performance and scalability goals, this project will leverage data-driven techniques to radically rethink the cross-layer hardware-software data center computing stack from the ground-up, with carbon efficiency at the forefront. This project also includes a comprehensive education and outreach plan that is seamlessly integrated with the research. The highlights include: (a) adding new sustainability modules in high school, undergraduate, and graduate curricula; (b) conducting sustainability-focused workshops and tutorials at academic conferences; and (c) engaging students from underrepresented groups in sustainability research. Thus, this project’s direct impact is reducing carbon emissions to combat climate change by redefining how future data center systems are designed, managed, and taught in a sustainability-first manner.Aiming at reducing emissions caused by microservices, this project systematically studies microservices through a sustainability lens. The project’s key innovation is a radically redesigned hardware-software systems stack with carbon-aware hardware and novel software systems that leverage such hardware while achieving microservices' scalability goals. On the hardware front, this work will take a new approach using the sustainability principles of reducing and reusing to systematically design carbon-aware hardware at the server, device component, and microarchitecture levels. First, this work will identify when to reuse older servers to run latency-tolerant microservices. Second, to reduce devices, this work will develop a tool to only accelerate key common primitives and analyze resource wastage to design reduced components. Third, this work will design leaner microarchitectures. On the software front, this work will develop a novel carbon-aware data-driven resource management framework to enable the carbon-efficient designs at each hardware level. By pursuing data-driven techniques, this work will highlight the value of using data centers' always-on telemetry to achieve its designs. This cross-disciplinary research cuts across three areas: computer architecture, software systems, and new application paradigms, opening new opportunities for interdisciplinary work on sustainable computing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位:
基于Linked Open Data的Web服务语义互操作关键技术
  • 批准号:
    61373035
  • 项目类别:
    面上项目
  • 资助金额:
    77.0万元
  • 批准年份:
    2013
  • 负责人:
    冯志勇
  • 依托单位: