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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | Indiana University |
| Country | United States |
| Start Date | Jul 01, 2024 |
| End Date | Jun 30, 2029 |
| Duration | 1,825 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2340722 |
Cloud computing's growing carbon gas emissions footprint (2% of global emissions) threatens sustainability, especially with the rise of energy-hungry artificial intelligence (AI) and internet of things (IoT) applications. While renewable energy adoption is increasing, integrating it effectively into cyberinfrastructure remains a hurdle due to variability and location dependence.
The proposed work will lay the scientific and technical foundations of a distributed computing infrastructure which is both efficient and sustainable, using carbon as the first-order system-wide objective. It will enable the seamless deployment and use of low-carbon applications such as web services, AI, IoT, and data analytics. The models, software, and datasets produced through the research will be open-sourced and integrated into undergraduate curriculum and research.
Using new scalable pedagogical software such as "policy gyms", we will provide cross-disciplinary hands-on training to undergraduate students in the fields of computer engineering, AI, and sustainability.
The project will develop "Green Functions as a Service", a new abstraction for decarboninzing latency-sensitive applications on the edge-cloud continuum. Our approach will be grounded in fundamental principles of sustainability such as demand response, carbon pricing, and eco-feedback, and use modern AI techniques such as surrogate models for carbon modeling and optimization.
We will extend serverless computing with new capabilities such as polymorphic functions for carbon-efficient execution on heterogeneous CPU and GPU architectures. Our distributed resource management algorithms will use spatio-temporal carbon and workload modeling and optimization. The geographical load balancing will combine machine learning and carbon credits to provide carbon and performance management for distributed cyberinfrastructure.
Our multi-faceted research and education plan will introduce key sustainability principles to both system design and pedagogy, and contribute to a sustainable digital world.
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.
Indiana University
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