AI-Assisted Resource Allocation for Improved Business Efficiency and Profitability

The emergence of smart devices has raised demand for numerous computational paradigms, including cloud computing, fog computing, and the Internet of Things. But even in these paradigms, efficient resource allocation is still difficult. This article provides a thorough assessment of the literature on the use of machine learning (ML) and deep learning (DL) techniques in computational paradigms for resource allocation optimization. As far as we are aware, there are no studies of resource allocation methods based on AI in various computing paradigms. The analyzed machine learning-based methods are divided into two categories: multi - layer perception and evolutionary computation. DL-based techniques are also surveyed, as well as how they work in conjunction with RL. An examination of unexplored study areas and a conclusion round out the review.