AI-Driven Capacity Planning for Next-Generation Data Centers

Abstract

Contemporary data centers face significant resource management challenges due to workload uncertainties and dynamic demand fluctuations. Traditional capacity planning models relying on averages and predictive functions cannot address cloud-native ecosystem complexity. These linear models fail to match modern infrastructure supporting AI applications, big data analytics, and distributed computing, which exhibit nonlinear resource requirements. This mismatch causes persistent over-allocation or performance degradation during peaks. The proposed capacity planning framework utilizing machine learning algorithms provides proactive, non-reactive predictive capabilities. The architecture incorporates automated policy enforcement and scenario simulation for evaluating provisioning strategies. Multi-dimensional demand projection captures compute, storage, and network resource interdependencies ignored by single-resource models. Governance mechanisms ensure decision explainability and audit trail preservation for regulated environments. Human-AI collaboration architectures position intelligent systems as infrastructure assistance tools rather than replacements. The framework provides essential capabilities for managing next-generation data center infrastructure at scale with unprecedented resilience.