<ul><li>This work presents a two-tier optimization framework for data center resource management in large-scale heterogeneous environments.</li><li>The framework combines deep reinforcement learning (DRL) with a gradient-based heuristic for optimal rack positioning.</li><li>The high-level DRL agent determines optimal rack type ordering, while the low-level heuristic minimizes movement counts and ensures fault-tolerant resource distribution.</li><li>The proposed approach outperformed the gradient-based heuristic and mixed-integer programming (MIP) solver in terms of objective value and computational efficiency.</li></ul>

Rack Position Optimization in Large-Scale Heterogeneous Data Centers

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