Probabilistic Modeling of Workload Patterns for Capacity Planning in Data Center Environments

Abstract

Capacity planning in data center environments is crucial for ensuring efficient resource allocation and maintaining service quality. Traditional capacity planning approaches often rely on deterministic workload models, which may fail to capture the inherent variability and complexity of real-world workload patterns. In this paper, we propose a novel probabilistic modeling framework for workload patterns to enhance capacity planning accuracy. Our approach leverages probabilistic models, such as Gaussian processes and Markov chains, to capture the stochastic nature of workload behavior. By incorporating historical workload data, our model learns underlying patterns and dependencies, enabling more accurate predictions of future workload variations. Furthermore, we introduce a novel method for uncertainty quantification, allowing capacity planners to assess the confidence level of their predictions.