Cohort: 3

Members: Lu Bo, Ken Wong, Michael Yao, Simon Liu, Lin Xinhua

Problem: As AI, enterprise and HPC workloads continue to grow rapidly, data centers are facing increasing pressure from rising energy consumption, constrained power availability and the need to scale computing capacity while reducing carbon emissions.

Approach: Building an AI-driven orchestration system that continuously optimizes enterprise, AI and HPC workloads across cloud and data center infrastructure, transforming energy into a schedulable resource for next-generation computing.

Powered by Lenovo xCloud AIOps, the solution introduces a unified full-stack decision engine spanning workload scheduling, compute resources, cloud infrastructure, cooling and power systems, enabling real-time, closed-loop optimization across the entire data center stack.  

Result: Deployed and validated at the Shanghai Jiao Tong University data center, the solution achieved up to 11% reduction in energy consumption and up to 310 tonnes less CO₂ emissions annually, while enabling greater compute output and infrastructure efficiency under the same power budget.