Predicting Traffic Patterns in Cloud Computing Systems to Optimize Resource Usage in Organization

One of the most vital instruments for the company to control traffic and distribute system resources effectively is the short-term traffic flow forecast. To incorporate breakthroughs like artificial intelligence and machine learning and monitor traffic, cloud computing requires focused processing resources and prediction tools. This paper proposes three traffic flow prediction tools: Gaussian process regression, linear support vector machine (SVM), and stepwise linear regression. The findings demonstrate that stepwise linear regression outperforms the models in traffic flow prediction. In the future, we propose to explore the use of a combination of machine learning models with hourly monitoring and resource allocation.