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Project Title: Understanding carbon intensity data approaches to optimize carbon emissions
Project Title: Understanding carbon intensity data approaches to optimize carbon emissions
The Laboratory for Advanced System Software (LASS) investigates systems issues for distributed systems ranging from large server clusters to networks of small sensors. Our lab comprises two research groups: the Distributed Systems Group and the Sustainable Computing Group.
The Distributed Systems Research Group focuses on topics in cloud computing, edge computing, data centers, virtualization, and data management.
The Sustainable Computing research group focuses on computational methods for sustainability and decarbonization of engineered systems and infrastructure. We apply our research to energy grids, built environment, renewables, energy storage, and transportation. We take a systems and data analytics approach to addressing problems in this area.
Project Description
Students will work on understanding the impact of various energy sources on the carbon intensity of the grid and how to utilize this information to optimize the carbon efficiency of energy usage. For example, what are the differences in running my web application in Virginia vs. Texas? By understanding this information, students will be able to answer questions such as: How does carbon intensity vary across locations in the US and worldwide? When and where to deploy your compute workloads? To answer these and other questions, students will develop skills in big data analysis/visualization and gain background on technical topics, resource management, and scheduling.
Learning Objectives:
Understand grid's carbon intensity data
Learn key approaches in carbon-aware resource management
Create an interactive method to convey scheduling advice
Learn Data Visualization, SDK design, and soft skills
Skills to learn:
Experience with Python and related frameworks (Pandas, SciPy, NumPy)
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