An approach for optimizing resource allocation and usage in cloud computing systems by predicting traffic flow
Título del documento: An approach for optimizing resource allocation and usage in cloud computing systems by predicting traffic flow
Revista: Latin-American Journal of Computing (LAJC)
Base de datos:
Número de sistema: 000565154
ISSN: 1390-9134
Autores: 1
1
Instituciones: 1North-West University,
Año:
Volumen: 11
Número: 1
Paginación: 80-89
País: Ecuador
Idioma: Inglés
Resumen en inglés The cloud provides computing resources as a service (scalable and cost-effective storage, management, and accessibility of data and applications) through the Internet. Even though cloud computing offers many opportunities for ICT (information and communication technology), many issues still remain, and the increasing demand for resource management and traffic flow is also becoming increasingly problematic. The amount of data in the cloud computing environment is increasing on a daily basis, which increases data traffic flow. Due to this problem, clients complained about the network speed. Autoregressive Integrated Moving Average (ARIMA), Monte Carlo, Extreme gradient boosting regression (XGBoost), is used in this paper for predicting traffic flow. A Monte Carlo prediction of 84% outperformed ARIMA's prediction of 79.8% and XGBoost's prediction of 71.5%, indicating that Monte Carlo is more accurate than other models when predicting traffic flow in organizational cloud computing systems. A machine learning model will be used for future studies, along with hourly monitoring and resource allocation.
Resumen en español The cloud provides computing resources as a service (scalable and cost-effective storage, management, and accessibility of data and applications) through the Internet. Even though cloud computing offers many opportunities for ICT (information and communication technology), many issues still remain, and the increasing demand for resource management and traffic flow is also becoming increasingly problematic. The amount of data in the cloud computing environment is increasing on a daily basis, which increases data traffic flow. Due to this problem, clients complained about the network speed. Autoregressive Integrated Moving Average (ARIMA), Monte Carlo, Extreme gradient boosting regression (XGBoost), is used in this paper for predicting traffic flow. A Monte Carlo prediction of 84% outperformed ARIMA's prediction of 79.8% and XGBoost's prediction of 71.5%, indicating that Monte Carlo is more accurate than other models when predicting traffic flow in organizational cloud computing systems. A machine learning model will be used for future studies, along with hourly monitoring and resource allocation.
Palabras clave: Autoregressive integrated moving average (ARIMA),
Extreme Gradient Boosting (XBoost),
Monte Carlo technique
Keyword: Monte Carlo technique,
Extreme Gradient Boosting (XGBoost),
Autoregressive integrated moving average (ARIMA)
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