An overall statistical analysis of AI tools deployed in Cloud computing and networking systems

Abstract

As the vast amount of data destined to cloud systems never stop growing in seconds, minutes, hours and daily basis, the development of dynamic, autonomous and proactive architectures for cloud resources scheduling becomes a veritable prerequisite. This dominant trend is inciting to further seek for complete and accurate forecasting and predictive models to support decision making in several cloud-scheduling levels. In this perspective, this paper is a result of a meticulous statistical analysis of about five hundred relevant research articles dealing with proactive resources scheduling in cloud, fog, edge computing and networking systems, using a complete panoply of Artificial Intelligence (AI) predictive techniques. The first aim is to highlight for the first time current trends bridging the gap between cloud services management and AI tools.