AI-Powered Cloud Computing for Performance Optimization and Scalability in Distributed Systems

The IT profession has observed the ingenious application of Artificial Intelligence in the field of cloud computing as a solution to increase performance along with scalability within the distributed computing environment. Conventional cloud computing context has issues with management of resources, responsiveness, and finally security. The following paper will discuss an AI based architecture which will incorporate ML algorithms to optimize the usage of the resources available in the computer cloud to balance the load between different users and ensure security. Thus, the proposed model incorporates reinforcement learning for the dynamic load distribution, predictive analytics for workload forecasting, and deep learning based on anomaly detection to improve the security features of the system. The performance comparison with the baseline and the experiments conducted show satisfactory results in more than one performance indicators. For CPU usage, it rose to 26.4%, which was achieved alongside the provision of the 48% higher request throughput and the 40% better response time under the scenarios of the heavy workload. Moreover, the security framework using CNNs enhanced the detection accuracy of intrusion to 96.7% and a reduction of the false positives by sixty-six percent. These are some of the implications suggesting that the application of artificial intelligence can bring positive changes in the goals regarding cloud performance, resource utilization and overall security features. In this way, using AI-driven automation and prediction integrated in this work is offered a powerful, adaptive, and safe cloud model for expanded usage. With the proposed approach that has been developed the businesses and the service providers will be in a position to attain efficient business operational costs together with strong system reliability that will assist in developing the next generation of intelligent cloud computing.