Integrating Artificial Intelligence with Cloud Platforms to Optimize Performance, Scalability, and Reliability in Distributed Computing Systems

This non-stop advancement of distributed computing systems due to the necessity of achieving big data processing for real-time data processing and high work complexity requires better solutions for enhancing high-performance, scalability, and reliability. This study focuses on the interaction between AI and Cloud Computing, to improve the performance of Distributed Systems. It is the hope of this paper that with the use of AI-adaptive algorithms and machine learning, resource distribution can be optimized, system constraints can be forecasted with + precision, and summaries can be made fault resilient across various computational platforms. The focus techniques are reinforcement learning for dynamic resource management, genetic algorithms for load distribution, and federated learning for enhanced data confidentiality and distributed model training. Further, workload prediction is done using time-series based or machine-learning based predictive Analytics, and deep learning-based anomaly detection for health monitoring and autoencoder for fault diagnosis. Container orchestration and micro services architecture has been implemented in the proposed framework for achieving flexibility and scalability in multi-cloud and hybrid cloud systems. The experimental results show the effectiveness of the proposed implementation to enhance the system performance, scalability and reliability and thus signaling a new dawn for AI based solutions in distributed computing systems.