CAREER: Carbon Footprint Modeling and Elastic Caching for Greening Services

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，他们应该进行昂贵的投资，以消除他们的能源足迹造成的环境危害。碳抵消是一种可转让的证书，可消除温室气体排放，即使抵消持有人位于碳重地区，也使其对大规模、地理分布的服务具有吸引力。这个研究项目为绿色服务奠定了基础，绿色服务是一种新型的互联网服务，它为通过其服务器路由的用户请求购买碳补偿（即，一种服务使其他服务变得绿色）。使用流行的大型服务的人可以通过简单地通过绿色服务发送请求来消除他们的碳足迹。绿化服务将管理成本。绿化服务的智力挑战是模拟或确认不受其控制的服务器的碳足迹。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）提供了一致性。服务之间在响应时间和能源消耗方面的差异越来越多地源于服务特定的特性。我们使用黑盒机器学习方法来推断这些特征。除了绿色服务之外，拟议的研究还将帮助系统管理人员识别性能缺陷，特别是那些将能源消耗转向能源成本高的数据中心的昂贵缺陷。作为拟议研究的一部分，PI将向服务不足的机构和俄亥俄州哥伦布市地区的当地高中进行推广。

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.