Predictive Machine Learning for Intelligent Risk Analytics and Infrastructure Optimization in Scalable Multi-Cloud Enterprises
DOI:
https://doi.org/10.15662/IJEETR.2023.0506030Keywords:
Predictive machine learning, risk analytics, infrastructure optimization, multi-cloud computing, cloud enterprises, resource forecasting, anomaly detection, predictive analytics, cloud management, workload prediction, capacity planning, intelligent automationAbstract
Scalable multi-cloud enterprises increasingly depend on distributed cloud infrastructure to support applications, data processing, business operations, and digital services across multiple providers. This expansion creates opportunities for flexibility and resilience but also introduces complex challenges involving operational risk, resource utilization, performance variability, cost management, security exposure, and service availability. Predictive machine learning (ML) provides an intelligent mechanism for analyzing historical and real-time infrastructure data to anticipate risks and optimize resource allocation before operational problems become critical. This study examines the application of predictive ML to intelligent risk analytics and infrastructure optimization in multi-cloud enterprise environments. The proposed approach integrates telemetry collection, data preprocessing, feature engineering, predictive modeling, risk scoring, resource forecasting, and continuous model monitoring within an automated analytical pipeline. Historical workload patterns, resource consumption, application performance indicators, service-level measurements, network behavior, incidents, and cost information are used to develop models capable of predicting infrastructure anomalies, capacity requirements, performance degradation, and operational risks. The methodology combines supervised learning, time-series forecasting, and anomaly-detection techniques with cloud orchestration and monitoring mechanisms. Model evaluation considers predictive accuracy, forecasting error, risk-detection capability, computational efficiency, and resource optimization. The study further emphasizes model explainability, data governance, interoperability, and continuous learning because multi-cloud environments evolve rapidly. Predictive ML can consequently support proactive infrastructure management, improve resource efficiency, reduce avoidable operational disruptions, and strengthen evidence-based decision-making across complex enterprise cloud ecosystems
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