RESEARCH · RESEARCH · #1703
MIT’s Christina Delimitrou applies machine learning to improve data‑center efficiency
Christina Delimitrou, an associate professor at MIT and member of CSAIL, leads research applying machine learning to make large-scale data centers more efficient, secure, and reliable by redesigning cloud systems, managing shared hardware resources, and developing tools (such as Seer) that anticipate and prevent application problems. Her work aims to increase utilization of existing servers so operators can deliver predictable performance while reducing the need to build new, power-hungry data centers.
KEY POINTS
- Christina Delimitrou, an associate professor at MIT and member of CSAIL, leads research applying machine learning to make large-scale data centers more efficient, secure, and reliable by redesigning cloud systems, managing shared hardware resources, and developing tools (such as Seer) that anticipate and prevent application problems.
- Her work aims to increase utilization of existing servers so operators can deliver predictable performance while reducing the need to build new, power-hungry data centers.
- Improving data‑center utilization with ML can reduce electrical grid strain and fossil‑fuel reliance by extracting more computation from existing hardware, lowering the need for new facilities.
WHY IT MATTERS
Improving data‑center utilization with ML can reduce electrical grid strain and fossil‑fuel reliance by extracting more computation from existing hardware, lowering the need for new facilities.