Smart Enterprise Cloud Computing Using Artificial Intelligence and Machine Learning Techniques: An Analytical Study of Intelligent Resource Optimization, Predictive Security, and Business Automation
Abstract
Enterprise cloud computing has advanced from a capacity replacement model to a smart operating model where telemetry, automation, security analytics and business workflows are integrated continuously. The paper is mainly focused on the role that AI and ML based solutions have on intelligent enterprise cloud computing through techniques such as workload forecasting, adaptive resource provisioning, security predictions and business automation. The researcher uses a mixed analytical and secondary-data-based research design. The article synthesizes peer-reviewed literature, cloud architecture frameworks, security standards and public workload/security datasets including Google Borg traces, Alibaba cluster traces, Microsoft Azure public traces and CSE-CIC-IDS2018 on AWS. As we did not download and empirically model any raw traces in this manuscript, the paper presents a verified dataset-mapping and analysis framework as opposed to fictitious performance values. According to the analysis, regression models, decision trees, random forests, neural networks, reinforcement learning, clustering, isolation forests, autoencoders and time-series models can help achieve different cloud-management tasks. Nevertheless, the actual benefit will depend on data quality, observability coverage, governance maturity and explainability. The model under consideration combines telemetry collection with Artificial Intelligence and Machine Learning analytics along with intelligent orchestration. The intention of the paper is to contribute an IEEE-style conceptual and analytical framework that connects AI-enabled resource optimization, ZTO oriented predictive security and business automation. Further, it emphasizes verifiable data, cautious interpretation and empirical validation. Future research should evaluate the framework with production-grade workload traces, sector-specific cloud cost data and amenable security-event datasets.