Harnessing Artificial Intelligence and Machine Learning to Transform Cloud Computing with Enhanced Efficiency and Personalization

This work seeks to evaluate how ML and GAI could be integrated into the cloud computing model with an effort of optimizing the use of resources, minimizing energy consumption and providing value added services. Similar to other systems of its nature, which are large-scale distributed systems, cloud computing systems have several topics of concern including dynamic resource management, security issues, and the issues regarding with the user interface. To address these discrepancies, this work proposes a single D-PAL framework that uses the predictive ML model application and GAI for synthetic data generation. In the cloud environments of the framework, it employs workload prediction and scheduling for resource estimation through ML, and scheduling through Reinforcement Learning, and for data augmentation through GAN. From the experimental assessment one is able to observe implicit improvements in the performance of the cloud resources, energy consumption, and customised user services. In this regard, this paper advances theoretical and empirical understanding on personnel characteristics of AI on cloud systems and deploy new methods that improve cloud performance and maintain security and usability. Future work will be more focused on expanding of the proposed models to scale and integrating other AI techniques to increase the cloud control.