Abstract
This disclosure describes the use of reinforcement-learning (RL) techniques to optimize datacenter planning and rack placement. An RL agent operates within a state space that includes physical infrastructure (rack space, power, cooling, network), resource availability (machine inventory, backup power), constraints (emissions, workload demands), etc. The RL agent takes actions such as allocating capacity and placing machines. A reward function guides the agent towards optimal solutions by rewarding utilization, balancing load, achieving compliance with service-level objectives, and penalizing resource stranding and costs. RL algorithms balance exploration and exploitation, adapt to dynamic environments, and scale to large datacenters. Advantages of the described techniques include improved resource utilization, enhanced scalability, automated optimization, etc.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
NA, "Datacenter Planning and Rack Placement Optimization Using Reinforcement Learning", Technical Disclosure Commons, (July 08, 2025)
https://www.tdcommons.org/dpubs_series/8319