Collaborative Research: NGSDI: CarbonFirst: A Sustainable and Reliable Carbon-Centric Cloud-Edge Software Infrastructure

合作研究：NGSDI：CarbonFirst：可持续且可靠的以碳为中心的云边缘软件基础设施

• 批准号: 2105648
• 负责人: Adam Wierman
• 金额: $ 36.11万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105648&HistoricalAwards=false
• 关键词: Collaborative; Research; NGSDI; CarbonFirst; Sustainable

Cloud computing platforms continue to grow exponentially, and are becoming the foundation of our information-based economy. While the cloud’s energy demand grew more slowly than expected over the past decade due to aggressive energy-efficiency optimizations, there are few remaining optimization opportunities using traditional methods. As a result, the cloud’s continued exponential growth will translate into exponentially rising energy demand, which will position it as one of the primary contributors to global carbon emissions. To address the problem, this project elevates carbon to a first-class metric in designing a sustainable and reliable cloud-edge software infrastructure that can enable continued exponential growth.The project's foundation is a software-defined energy virtualization layer that provides applications visibility into, and control of, their own energy and carbon usage. The project will leverage this foundation to develop higher-level systems abstractions for supporting carbon-efficient applications at different geographical scales including: a cluster balloon technique, which automatically adjusts applications’ energy usage to match a volatile clean energy supply at local edge sites; edge hopping mechanisms, which exploit lower regional energy volatility to balance energy across edge sites; and carbon capping policies, which track applications’ global grid carbon emissions and restrict grid energy after reaching the cap.The project has the potential for significant societal impact by enabling commercial cloud platforms to sustainably continue their exponential growth. The project will conduct outreach by incorporating topics from the proposal into summer programs for local middle and high school students at the partner institutions. The project will also impact the curriculum at these institutions by adopting elements of edge, cloud, and sustainable computing into graduate and advanced undergraduate courses. Finally, the project will recruit a diverse group of students by leveraging institutional diversity efforts and will involve undergraduate students through Research Experience for Undergraduate (REU) projects.The project will make its software artifacts, datasets, and research results available to the research community on the project website at http://www.carbonfirst.org and via the UMass Trace Repository at http://traces.cs.umass.edu. Artifacts derived from this project will be maintained on the project website and the trace repository for a minimum of five years after the project's conclusion.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.

云计算平台继续呈指数级增长，并正在成为我们基于信息的经济的基础。 虽然由于积极的能效优化，云计算的能源需求在过去十年中的增长速度低于预期，但使用传统方法的优化机会很少。因此，云计算的持续指数增长将转化为指数增长的能源需求，这将使其成为全球碳排放的主要贡献者之一。为了解决这一问题，该项目将碳排放提升为设计可持续、可靠的云边缘软件基础设施的一流指标，以实现持续的指数级增长。该项目的基础是一个软件定义的能源虚拟化层，为应用提供对自身能源和碳排放使用的可见性和控制。该项目将利用这一基础来开发更高级别的系统抽象，以支持不同地理尺度的碳效率应用，包括：集群气球技术，该技术自动调整应用的能源使用，以匹配当地边缘站点的不稳定清洁能源供应;边缘跳跃机制，该机制利用较低的区域能源波动来平衡边缘站点的能源;以及碳排放上限政策，该政策跟踪应用程序的全球电网碳排放量，并在达到上限后限制电网能源。该项目通过使商业云平台可持续地继续其指数增长，具有重大的社会影响力。 该项目将通过将提案中的主题纳入合作机构为当地初中和高中学生提供的暑期课程来进行推广。该项目还将通过将边缘，云和可持续计算的元素纳入研究生和高级本科课程来影响这些机构的课程。 最后，该项目将通过利用机构多样性的努力招募一个多样化的学生群体，并将通过本科生研究经验（REU）项目吸引本科生参与。该项目将在项目网站http://www.carbonfirst.org和通过UMass Trace Repository http://traces.cs.umass.edu上向研究社区提供其软件工件，数据集和研究结果。项目结束后，项目网站和跟踪存储库将至少保存该项目的成果五年。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load Shifting

在线暂停和恢复问题：最优算法和碳感知负载转移的应用

• DOI: 10.1145/3626776
• 发表时间: 2023
• 期刊: Proceedings of the ACM on Measurement and Analysis of Computing Systems
• 作者: Lechowicz, Adam;Christianson, Nicolas;Zuo, Jinhang;Bashir, Noman;Hajiesmaili, Mohammad;Wierman, Adam;Shenoy, Prashant
• 通讯作者: Shenoy, Prashant

Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems

• 发表时间: 2021-06
• 作者: Yiheng Lin;Yang Hu;Haoyuan Sun;Guanya Shi;Guannan Qu;A. Wierman

Scalable Reinforcement Learning for Multiagent Networked Systems

• DOI: 10.1287/opre.2021.2226
• 发表时间: 2019-12
• 期刊: Oper. Res.
• 作者: Guannan Qu;A. Wierman;N. Li

Competitive Control with Delayed Imperfect Information

• DOI: 10.23919/acc53348.2022.9867421
• 发表时间: 2020-10
• 期刊: 2022 American Control Conference (ACC)
• 作者: Chenkai Yu;Guanya Shi;Soon-Jo Chung;Yisong Yue;A. Wierman

Stability Constrained Reinforcement Learning for Real-Time Voltage Control

• DOI: 10.23919/acc53348.2022.9867476
• 发表时间: 2021-09
• 期刊: 2022 American Control Conference (ACC)
• 作者: Yuanyuan Shi;Guannan Qu;S. Low;Anima Anandkumar;A. Wierman