Enhancing Capacity Planning in Data Centers through Probabilistic Workload Modeling

Abstract

Efficient capacity planning is vital in data center operations to ensure optimal resource allocation and maintain consistent service performance. Traditional planning methods often use deterministic models that overlook the inherent unpredictability of real-world workloads. This study introduces a probabilistic modeling framework designed to better capture the stochastic nature of workload behavior in data centers. Our approach incorporates models such as Gaussian processes and Markov chains to analyze historical workload data, identifying patterns and dependencies that enable more accurate forecasting of future demands. A novel uncertainty quantification method is also introduced, allowing planners to evaluate the reliability of their predictions. We validate our framework through extensive experiments using real-world data from various data centers. The results demonstrate improved prediction accuracy and robustness compared to traditional methods. A case study further highlights the framework’s practical benefits in optimizing resource allocation and reducing operational expenses. Overall, this work presents a compelling argument for integrating probabilistic modeling into data center capacity planning to enhance adaptability, efficiency, and resilience.