Mathematical Modeling of Data Growth in Cloud Computing Using Differential Equations (Journal of Advances and Scholarly Researches in Allied Education, 2026)
The rapid expansion of data in cloud computing environments has introduced significant challenges in storage scalability, resource allocation, and system optimization. Accurate modeling of data growth is essential for efficient infrastructure planning. This paper presents a mathematical framework based on differential equations to model data growth in cloud systems. Both the exponential model and logistic models are explored for their short- and long-term behavior. Simulation experiments reveal that although the exponential growth model accurately predicts growth at the early stages, the logistic model is more realistic in that it takes into account capacity limitations within the system.This research underscores the role of mathematical modeling in optimizing cloud computing operations.