Towards a Model for Predicting Traffic Flow for Optimizing Resource Allocation and Usage in a Cloud Computing Systems

Abstract

Abstract Short-term traffic flow prediction is one of the most important tools for the organisation to manage traffic and properly allocate system resources. Cloud computing requires concentrated computational resources and predictive tools to monitor traffic and integrate innovations such as artificial intelligence and machine learning. This paper uses compares a stepwise linear regression, linear support vector machine, and Gaussian process regression for tools for predicting traffic flow. The results show that stepwise linear regression performs better than other models in predicting traffic flow in Organisational Cloud Computing Systems. The use of a combination of machine learning models with hourly monitoring and resource allocation will be considered for future studies.