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.

随着互联网服务的规模，他们不断增长的能源和碳足迹会带来矛盾的挑战。 一方面，服务必须保持能源成本较低。 另一方面，他们应该进行昂贵的投资，以减轻其能源足迹造成的环境伤害。 碳偏移是可转让的证书，即使偏移持有人位于碳含量较重的地区，也使其对大规模，地理分布的服务有吸引力。 该研究项目为绿色服务奠定了基础，这是一种新的互联网服务，可为通过其服务器路由的用户请求购买碳偏移（即，使其他服务绿色）。 使用流行的大规模服务的人们可以通过简单地通过绿色服务来解除要求的要求，从而消除其碳足迹。 绿化服务将管理成本。绿色服务的智力挑战是建模或确认其控制之外的服务器的碳足迹。 关键的见解是，云计算中的新兴趋势，例如节能服务器，自动扩展和开源软件，提供均匀性。 在响应时间和能源足迹方面，服务之间的差异越来越多，这是由于特定于服务的功能。 我们使用黑盒机器学习方法来推断这些功能。拟议的研究将帮助系统经理确定性能错误，尤其是昂贵的错误，这些错误将能源消耗转移到具有高能源成本的数据中心。 作为拟议的研究的一部分，PI将向服务不足的机构以及俄亥俄州哥伦布市高中进行宣传。

项目成果

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