Learning Burst-Aware Early Warning Models for Capacity Stress under AI Workload Surges in Hyperscale Data Centers

Researchers propose a machine learning framework for predicting capacity stress in hyperscale data centers operating under intensive AI workloads like LLM training and inference. The XGBoost-based early warning system achieves 91.4% recall in detecting stress-prone periods, enabling proactive interventions such as workload throttling and resource scaling before system degradation occurs.

The explosive growth of AI workloads has created unprecedented operational challenges for data center managers. Traditional reactive threshold-based monitoring systems fail to handle the bursty, high-intensity resource demands characteristic of large language model training and inference jobs. This research addresses a critical operational gap by developing a predictive framework that shifts data centers from reactive to proactive management.

The work emerges from a broader infrastructure challenge facing cloud providers and AI companies operating at scale. As AI adoption accelerates, data centers experience sudden, unpredictable capacity surges that cascade into service degradation and increased operational costs. The paper's contribution—integrating workload intensity signals, temporal patterns, and system pressure metrics into a lightweight tree-based model—represents a practical engineering solution to an increasingly urgent problem. The 91.4% recall rate demonstrates meaningful detection capability for real-world deployment.

For infrastructure operators and cloud providers, this framework offers tangible value in optimizing resource allocation and preventing cascading failures. Proactive capacity management directly reduces costs associated with emergency scaling, prevents service-level agreement violations, and improves overall system resilience. The methodology's emphasis on deployment-oriented threshold selection reflects realistic operational constraints rather than purely academic optimization.

The framework's integration into operational control loops opens pathways for autonomous resource management in AI-heavy environments. Future applications may extend to cross-region load balancing and predictive resource reservation strategies. As AI workload diversity increases, similar burst-aware prediction systems will become essential infrastructure components for any organization operating large-scale machine learning systems.

• →XGBoost-based early warning system achieves 91.4% recall in detecting data center capacity stress from AI workload surges
• →Framework integrates workload intensity, temporal variation, and system pressure signals to predict stress before degradation occurs
• →Proactive capacity prediction enables operational interventions like workload throttling and dynamic resource scaling
• →High-recall design prioritizes stress detection even at the cost of acceptable false-alarm rates for practical deployment
• →Research demonstrates viability of learning-based approaches for managing unpredictable AI workload patterns in hyperscale infrastructure