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Active CONTINUING GRANT National Science Foundation (US)

CAREER: Green Functions as a Service: Towards Sustainable and Efficient Distributed Computing Infrastructure

$2.31M USD

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
Grant Description

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.

All Grantees

Indiana University

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