An approach for optimizing resource allocation and usage in cloud computing systems by predicting traffic flow

Keywords: Monte Carlo technique, Extreme Gradient Boosting (XGBoost), Autoregressive integrated moving average (ARIMA)


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.



Download data is not yet available.


Zheng, M., Huang, R., Wang, X., Li, X. Do firms adopting cloud computing technology exhibit higher future performance? A textual analysis approach. Journal of International Review of Financial Analysis, 90, (2023).[Online].Available:

Apat, H. K., Nayak, R., Sahoo, B. A comprehensive review on Internet of Things application placement in Fog computing environment.Journal of Internet of Things, 23,(2023).[Online]. Available:

Cheng, M., Qu, Y., Jiang, C., Zhao, C. Is cloud computing the digital solution to the future of banking? Journal of Financial stability,63,(2022).[Online]. Available:

Luo, J., Gong, Y. Air pollutant prediction based on ARIMA-WOA-LSTM model. journal of Atmospheric Pollution Research,14,(2023).[Online].Available:

Jing, J., Magnin, I.E., Frindel, C. Monte Carlo simulation of water diffusion through cardiac tissue models. journal of Medical Engineering and Physics.120,(2023).[Online]. Available:

Alés, A., Lanzini, F. Modelling of chemical and magnetic order in Ni-Mn-Al shape memory alloys using Monte Carlo simulations.Journal of Journal of Magnetism and Magnetic Materials, 285,(2023).[Online].Available:

Meerasri , J., Sothornvit, R. Artificial neural networks (ANNs) and multiple linear regression (MLR) for prediction of moisture content for coated pineapple cubes. Journal of Case Studies in Thermal Engineering,33,(2022). [Online]. Available:

Deng, T., Wu, J. Efficient graph neural architecture search using Monte Carlo Tree search and prediction network.Journal of Expert Systems With Applications,213, (2023). [Online]. Available:

Lee, K., Im, S., Lee, B. Prediction of renewable energy hosting capacity using multiple linear regression in KEPCO system.Journal of Energy Reports,9 , pp.: 343-347 (2023).[Online]. Available:

Afrasiabian, B., Eftekhari, M. Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming.Journal of Rock Mechanics and Geotechnical Engineering, 14 , pp.: 1421-1432 (2023). [Online]. Available:

Silagyi, D.V, Liu, D. Prediction of severity of aviation landing accidents using support vector machine models. Journal of Accident Analysis and Prevention,187, (2023)[Online]. Available:

Sharma, R., Awasthi, A. An embedded element based 2D finite element model for the strength prediction of mineralized collagen fibril using Monte-Carlo type of simulations.Journal of Biomechanics,108,(2020)[Online]. Available:

Nadjafi, M., Gholami,P. Probability fatigue life prediction of pin-loaded laminated composites by continuum damage mechanics-based Monte Carlo simulation. Journal of Composites Communications, 32,(2022).[Online]. Available:

He, H., Fan, Y. A novel hybrid ensemble model based on tree-based method and deep learning method for default prediction. journal of Expert Systems With Applications, 176,(2021).[Online].Available :

Santhusitha, D., Karunasingha, K.: Root mean square error or mean absolute error? Use their ratio as well: Information Sciences,585, pp.:609-629(2022).[Online].Available:

How to Cite
S. P. Sekwatlakwatla and V. Malele, “An approach for optimizing resource allocation and usage in cloud computing systems by predicting traffic flow”, LAJC, vol. 11, no. 1, pp. 80-89, Jan. 2024.
Research Articles for the Regular Issue