(untitled) (International Journal of Engineering and Computer Science, 2018)
Cloud computing services have quickly proven to be one of the primary technologies that can meet the dynamic needs of an organization, regarding IT resources distribution. But the problem of efficient resource provisioning is still considered quite pressing, whenever traditional approaches are applied, it results in resources over-provisioning or under-provisioning and, thus, is followed by increased costs, poor performance, and inefficient use of energy. In order to overcome these challenges there has been proposed dynamic resource provisioning concept based on the use of predictive analytics. This approach makes use of machine learning and data science aspects for continuously predicting the resource demand in order that cloud environments could accurately allocate the necessary resources in real time depending on workloads. The usage of predictive analytics in context with dynamic resource provisioning for cloud computing is the focus of this paper. This part talks about the basics of cloud computing and resources, the importance of machine learning models in the context of demand forecasting, and a plethora of dynamic provisioning techniques consisting of elastic scaling, load forecasting and cost consideration provisioning, and many more. This paper also explores day-to-day examples of how predictive analytics has been deployed to streamline the feature-entailing provisioning operation in cloud-based applications, from e-business sites to green data centers. In addition, the paper outlines the following imperatives: data quality, scalability of the reaches of the models in the paper, and latency issues that need to be resolved to facilitate the broader use of prediction analysis in the management of cloud resources. At last, it underscores the future scope, such as incorporating edge computing, using AI algorithms and more advanced machine learning algorithms which will pave the way to fortify the dynamics of resource provisioning. This study would therefore seek to provide further input into the onward evolution of more intelligent and effective as well as cheaper models of cloud computing. This abstract aims to present the goals of the paper and main ideas considered in it, besides, it highlights the main context for a better understanding of the use of dynamic resource provisioning facilitated by the predictive analytics in cloud environments.