Performance Analysis of Cost Prediction Algorithms in Cloud Computing through a Hybrid Model for Organizational Budget Optimization

Abstract

The rapid growth of cloud technologies has led to increasing organizational dependency on cloud services, consequently raising significant challenges in managing associated costs. Due to the dynamic, scalable, and heterogeneous nature of cloud services, cost prediction and control have become complex tasks. A promising approach to addressing these challenges is the adoption of cost prediction algorithms and the development of hybrid models to enhance both accuracy and efficiency. This study focuses on analyzing widely used cost prediction algorithms in cloud computing, including machine learning models, classical statistical methods, and hybrid frameworks, while evaluating their strengths and weaknesses. Real-world organizational data were collected, encompassing CPU usage, memory consumption, storage, and bandwidth logs. Based on these datasets, a hybrid model was developed that simultaneously leverages the accuracy of machine learning algorithms and the interpretability of statistical methods. Results indicate that the proposed hybrid model reduced the average prediction error by up to 25% compared to the best standalone algorithm. Furthermore, multi-parameter tables and diagrams illustrate how this model facilitates organizational budget optimization across various infrastructure domains. Ultimately, this research provides IT managers and organizational decision-makers with a new pathway toward smarter cloud cost management and more efficient resource allocation.