DESC: Type I: Enabling Carbon-Zero Colocation Data Centers via Agile and Coordinated Resource Management

University Of California-Riverside, Riverside CA

Investigators

Abstract

Multi-tenant colocation data centers (a.k.a. colocation) are critical infrastructures to support the booming artificial intelligence industry and digital economy. Rapid expansion in both number and scale of data centers results in formidable energy demand. While both colocation operators and tenants are eager to achieve carbon sustainability and ultimately "carbon-zero", turning this vision into reality faces significant challenges. Given tenants' siloed server management in a colocation, this project aims to answer, how can the combined energy demands be reimagined to make use of the intermittent renewable energy supply to reduce the carbon footprint? How can centers dynamically provision tenants' servers for operational carbon efficiency while addressing the negative impact on servers' lifespan to avoid increases in embodied carbon footprint? And, how can tenants' server power consumption be modulated in an agile manner to track intermittent renewables at a fine granularity? To address these challenging questions, this project proposes to develop (1) a computationally-efficient mechanism to coordinate the allocation of intermittent green energy to different tenants; (2) a learning-based algorithm to dynamically provision tenants' servers to minimize the operational carbon footprint while keeping the embodied carbon footprint low; and (3) an agile server power modulation framework that combines hardware power knobs (DVFS, Sleep states) with software power knobs (multi-power binaries) to enable faster and greater power adaptation to track green energy availability. This project has broader impacts on the industry and the scientific community. To keep the momentum for an environmentally sustainable and healthy data center industry, this project extends the exploration of optimizing owner-operated data centers to a critical and under-explored segment of data centers --- multi-tenant colocation data centers where tenants' server management is siloed. It can catalyze a shift in the management of future data centers and ultimately transforms the way that the artificial intelligence industry and digital economy evolve. This project also includes a significant educational component and provides abundant opportunities to nurture and attract students from under-represented groups to engage in computer science. The research results will be transitioned into the existing curriculum. Further, leveraging the large population of minority students in the PI's institution, this project will bear a profound impact on the education and careers of minority students. 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.