This thrust develops mechanisms and management strategies that reduce operational carbon by aligning computing demand with clean energy availability. The research spans three layers: demand response frameworks that coordinate grid operators, datacenter operators, and end users; power mechanisms such as energy proportionality and computational sprinting; and power management algorithms using game theory, reinforcement learning, and real-time scheduling across heterogeneous infrastructure.
Key Research Questions
- How should demand response programs coordinate grid operators, facility managers, and workload schedulers?
- Can sprinting and energy-proportional designs achieve carbon savings without violating quality-of-service guarantees?
- What role do game-theoretic and learning-based approaches play in managing power across competing workloads?
| Role | PI | Institution |
|---|---|---|
| Lead | Adam Wierman | Caltech |
| Collaborator | Benjamin Lee | Penn |
| Collaborator | Linh Phan | Penn |
| Collaborator | Christopher Stewart | Ohio State |

