Statistical Models for Predicting Scalability and Elasticity in Cloud Applications

Authors

  • Alkhansa Alawi Shakeabubakor Department of Data Science, Faculty of Computing Um Alqura University

Keywords:

Cloud Computing, Scalability, Elasticity, Statistical Modeling, Predictive Accuracy, Time Series Forecasting, Linear Regression, Bayesian Inference, Model Evaluation, Computational Cost.

Abstract

With the pervasive adoption of cloud applications, the importance of scalability and elasticity has become paramount. This paper delved into the statistical models used for predicting these crucial aspects, evaluating their performance, and subsequently, recommending practices for their implementation. Through an exploration of linear regression models, time series forecasting, Bayesian inference, and ensemble approaches, the research offered insights into their applicability in real-world scenarios, such as e-commerce platforms and streaming services. By comparing model performances using metrics like MAE, RMSE, and R², it showcased the implications of predictive inaccuracies. The study concluded with robust recommendations for the training, deployment, and continuous refinement of these models, emphasizing the balance between predictive accuracy and computational costs.

Downloads

Published

2024-05-27

How to Cite

Alkhansa Alawi Shakeabubakor. (2024). Statistical Models for Predicting Scalability and Elasticity in Cloud Applications. Journal of Computational Analysis and Applications (JoCAAA), 33(4), 491–500. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1422

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.