Enhancing Cloud Computing Performance with AI/ML and Generative AI: Innovative Approaches and Applications

Abstract

In a dynamic and fast-growing cloud computing market, reducing server equipment and energy consumption is the primary goal of cloud technologies. This article describes innovative solutions aimed at improving the efficiency of computing systems using cloud computing, in particular through the use of modern technology such as artificial intelligence and global search algorithms. AI increases the workload of the cloud computing system and accelerates the search for the most optimal solutions. ML is used in classifying various user needs, decisions, workload, and power consumption prediction. It also achieves energy consumption reduction using ML models based on AI/R using generative modeling and reinforcement learning models. The method and decision-making algorithms are presented for the implementation of the system, and the results of the optimizations are analyzed and discussed.The article will be useful for professionals in the field of cloud computing and all areas of artificial intelligence, who will be able to find new ways to reduce the power consumption and heat generation of complex computing systems, thereby increasing their energy efficiency and improving service life. Due to increasing volumes of data migration to cloud platforms, there is a rising demand for cloud computing data centers, which require commensurately vast server hardware equipment. In 2018, the total energy consumption for data center facilities around the world was about 2% of the total power generated, with a significant part of the consumed energy being used to cool the data centers.