Mathematical Models for Predicting Network Traffic in Cloud Computing Environments

Effective network traffic prediction is crucial for optimizing resource allocation and ensuring efficient performance in cloud computing environments. In this research, mathematical models are developed to use historical data, factors, and other data to predict future network traffic patterns more effectively. In this context, we then compare an array of time series models such as ARIMA, LSTM, as well as the Prophet model so that we can determine the cloud environments most appropriate for each. These models include time and day of the week as well as the general activities of the users in the network in order to mimic the real flow of network traffics. The experimental results concern the efficiency of the proposed models as opposed to existing approaches and give a lot of information to network administrators and cloud service providers. The outcomes make a significant contribution as to the formulation of intelligent approaches in resource management and improve the dependability and performance of cloud computing environments.