CAREER: Carbon Footprint Modeling and Elastic Caching for Greening Services

职业：绿化服务的碳足迹建模和弹性缓存

• 批准号: 1350941
• 负责人: Christopher Stewart
• 金额: $ 37.62万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1350941&HistoricalAwards=false
• 关键词: CAREER; Carbon; Footprint; Modeling; Elastic

As Internet services scale, their growing energy and carbon footprints present conflicting challenges. On one hand, services must keep energy costs low. On the other, they should make costly investments to undo environmental harm caused by their energy footprint. Carbon offsets are transferable certificates that undo greenhouse gas emission, even when offset holders are located in carbon-heavy regions, making them attractive to large-scale, geographically distributed services. This research project lays the foundation for greening services, a new class of Internet services that buy carbon offsets for user requests routed through their servers (i.e., a service that makes other services green). People who use popular large-scale services could undo their carbon footprint by simply routing their requests through a greening service. The greening service would manage costs.The intellectual challenge for greening services is to model or confirm carbon footprints for servers outside of their control. The key insight is that emerging trends within cloud computing, e.g., energy-efficient servers, auto scaling, and open source software, provide uniformity. Dissimilarity between services, in terms of response times and energy footprints, is increasingly due to service-specific features. We use black-box machine learning approaches to infer these features.Beyond greening services, the proposed research will help system managers identify performance bugs, especially costly bugs that shift energy consumption toward datacenters with high energy costs. As part of the proposed research, the PI will conduct outreach to underserved institutions and to local, Columbus, OH, area high schools.

随着互联网服务规模的扩大，其不断增长的能源和碳足迹带来了相互矛盾的挑战。 一方面，服务必须保持低能源成本。 另一方面，它们应该进行昂贵的投资，以消除其能源足迹造成的环境损害。 碳抵消是一种可转让的证书，可以消除温室气体排放，即使抵消持有者位于碳排放量高的地区，这使得它们对大规模的地理分布服务具有吸引力。 该研究项目为绿色服务奠定了基础，绿色服务是一类新的互联网服务，可以为通过其服务器路由的用户请求购买碳抵消（即，使其它服务成为绿色的服务）。 使用流行的大规模服务的人可以通过简单地将他们的请求路由到绿色服务来消除他们的碳足迹。 绿色服务将管理成本，绿色服务面临的智力挑战是为不受其控制的服务器建立碳足迹模型或确认碳足迹。 关键的见解是云计算中的新兴趋势，例如，高能效服务器、自动缩放和开源软件提供了一致性。 在响应时间和能源足迹方面，服务之间的差异越来越多地归因于服务特定的功能。 我们使用黑盒机器学习方法来推断这些功能。除了绿色服务，拟议的研究将帮助系统管理人员识别性能错误，特别是将能源消耗转移到高能源成本的服务中心的昂贵错误。 作为拟议研究的一部分，PI将对服务不足的机构和俄亥俄州哥伦布当地的高中进行外展。

项目成果

A Pareto Framework for Data Analytics on Heterogeneous Systems: Implications for Green Energy Usage and Performance

• DOI: 10.1109/icpp.2017.62
• 发表时间: 2017-08
• 期刊: 2017 46th International Conference on Parallel Processing (ICPP)
• 作者: Aniket Chakrabarti;S. Parthasarathy;Christopher Stewart

Scaling Analytics through Approximate and Distributed Computing

通过近似和分布式计算扩展分析

• 发表时间: 2017
• 期刊: OSU Masters Thesis
• 作者: Chakrabarti, Aniket

Early work on modeling computational sprinting

• DOI: 10.1145/3127479.3132691
• 发表时间: 2017-09
• 期刊: Proceedings of the 2017 Symposium on Cloud Computing
• 作者: Nathaniel Morris;Christopher Stewart;R. Birke;L. Chen;Jaimie Kelley