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LEARNING-BASED OPTIMIZATION OF RESOURCE REDISTRIBUTION IN LARGE-SCALE HETEROGENEOUS DATACENTERSChang-Lin Chen (20370300) 04 December 2024 (has links)
<p dir="ltr">This thesis addresses critical optimization challenges in large-scale, heterogeneous data centers: logical cluster formation for virtual machine placement and physical rack movement for efficient infrastructure management. As data centers grow in size and complexity, these systems face rising demands to minimize costs related to fault tolerance, reformation, and resource constraints while adapting to diverse hardware and operational requirements. </p><p dir="ltr">The first part focuses on logical cluster formation, where capacity guarantees must be maintained across millions of servers despite ongoing infrastructure events, such as maintenance and failures. Traditional offline methods fall short under these dynamic, large-scale conditions. To address this, a two-tier approach combining deep reinforcement learning (DRL) with mixed-integer linear programming (MILP) enables real-time resource allocation, reducing server relocations and enhancing resilience across complex server environments.</p><p dir="ltr">The second part tackles optimized rack placement in highly heterogeneous settings, where balancing fault tolerance, energy efficiency, and load distribution is essential. Static layouts struggle to accommodate diverse hardware configurations and fluctuating resource needs. This research proposes a scalable, tiered optimization approach using the Leader Reward method and a gradient-based heuristic to handle the computational demands of large-scale rack positioning.</p><p dir="ltr">By integrating DRL and heuristic techniques, this work provides a robust, scalable solution for cost efficiency and operational resilience in managing large, heterogeneous data centers, advancing intelligent data center management for modern cloud infrastructure.</p>
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