Collaborative Research: DESC: Type I: Extending lifetimes of partially broken machines to repurpose e-waste
合作研究:DESC:类型 I:延长部分损坏机器的使用寿命以重新利用电子垃圾
基本信息
- 批准号:2324858
- 负责人:
- 金额:$ 34.15万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-15 至 2026-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The goal of this project is to improve sustainability of computing hardware by extending the lifetimes of computing equipment even when they start to partially break down. The carbon emissions from producing computing equipment is high and it is wasteful to simply throw away computers when most of the functionality may still work. Yet this is a common practice due to high diagnosis and repair costs. This project investigates a new approach where instead of repairing devices, the remaining working components are utilized as-is. Software systems are not designed to handle partially broken machines, so the investigators will develop new approaches for managing these partially broken machines, both for consumer-grade hardware and datacenter-grade hardware. One key component the investigators will focus on is Solid State Drives (SSDs), which by design will wear-out with use. By incorporating device age into scheduling decisions, this investigation will discover how to extend their effective lifetimes through intelligent age-aware scheduling and management. The results of this research will enable an alternative option for utilizing failing hardware instead of repair or disposal. Utilizing this otherwise useless equipment will provide additional compute capacity, which would reduce the amount of new hardware that needs to be produced. This fits within the Design for Environmental Sustainability in Computing (DESC) program where the focus is on the entire lifecycle of computing equipment and its corresponding environmental impact. Specifically, extending the lifetimes of computing equipment reduces the amount of equipment that needs to be produced, thereby lowering the associated carbon emissions and toxic byproducts from the manufacturing process. The investigators will engage with both industry and academic researchers to raise awareness of the problem and disseminate results, including incorporating sustainability topics in the curriculum at the investigators' respective institutions. The investigators will also involve undergraduates and high school students where applicable to allow for broader and more diverse participation in the research.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.
该项目的目的是通过延长计算设备的寿命来提高计算硬件的可持续性,即使它们开始部分分解。生产计算设备产生的碳排放量很高,在大多数功能仍然可以工作时,简单地扔掉计算机是浪费的。然而,由于诊断和维修成本很高,这是一种常见的做法。该项目调查了一种新方法,其余的工作组件不是修复设备,而是使用AS。软件系统并非旨在处理部分损坏的机器,因此调查人员将开发用于管理这些部分破损机器的新方法,无论是用于消费级硬件还是数据中心级硬件。调查人员将重点关注的一个关键组成部分是固态驱动器(SSD),该驱动器会随着设计而磨损。通过将设备年龄纳入计划决策,这项调查将发现如何通过智能年龄的调度和管理来延长其有效的生命。这项研究的结果将为使用故障硬件而不是维修或处置提供另一种选择。利用这种无用的设备将提供额外的计算能力,这将减少需要生产的新硬件的数量。这符合计算环境可持续性(DESC)计划的设计,其中重点放在计算设备的整个生命周期及其相应的环境影响上。具体而言,延长计算设备的寿命减少了需要生产的设备数量,从而降低了制造过程中相关的碳排放和有毒的副产品。研究人员将与行业和学术研究人员互动,以提高对问题的认识并传播结果,包括将可持续性主题纳入研究人员各自机构的课程中。调查人员还将涉及本科生和高中生,适用,以允许对研究更广泛,更多样化的参与。该奖项反映了NSF的法定任务,并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准的评估值得支持的。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Timothy Zhu其他文献
TraceUpscaler: Upscaling Traces to Evaluate Systems at High Load
TraceUpscaler:升级跟踪以评估高负载下的系统
- DOI:
10.1145/3627703.3629581 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Sultan Mahmud Sajal;Timothy Zhu;Bhuvan Urgaonkar;Siddhartha Sen - 通讯作者:
Siddhartha Sen
Timothy Zhu的其他文献
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{{ truncateString('Timothy Zhu', 18)}}的其他基金
CAREER: Auto-generated experimentation for performance diagnosis of distributed systems
职业:自动生成分布式系统性能诊断实验
- 批准号:
2239291 - 财政年份:2023
- 资助金额:
$ 34.15万 - 项目类别:
Continuing Grant
CNS Core: Small: A Multi-Stakeholder Integrated Approach to Reduce Tail Latency Using Heterogeneity
CNS 核心:小型:利用异构性减少尾部延迟的多利益相关者集成方法
- 批准号:
1909004 - 财政年份:2019
- 资助金额:
$ 34.15万 - 项目类别:
Standard Grant
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相似海外基金
Collaborative Research: Conference: DESC: Type III: Eco Edge - Advancing Sustainable Machine Learning at the Edge
协作研究:会议:DESC:类型 III:生态边缘 - 推进边缘的可持续机器学习
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2342498 - 财政年份:2024
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$ 34.15万 - 项目类别:
Standard Grant
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- 批准号:
2342497 - 财政年份:2024
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Standard Grant
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合作研究:DESC:类型 I:FLEX:通过跨层可扩展和自适应设计构建面向未来的、支持学习的网络物理系统
- 批准号:
2324936 - 财政年份:2024
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Standard Grant
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2324937 - 财政年份:2024
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2324865 - 财政年份:2023
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