Generative Artificial Intelligence–Powered Enterprise Multi-Cloud Infrastructure for Intelligent Workload Optimization
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Abstract
Modern enterprise IT environments increasingly rely on heterogeneous multi-cloud architectures to achieve high availability, prevent vendor lock-in, and optimize operational costs. However, managing distributed resources across disparate public and private cloud environments introduces significant complexity regarding dynamic workload placement, resource allocation, latency minimization, and cost governance. Traditional rule-based and static telemetry systems fail to adapt to real-time execution anomalies and fluctuating user demands. This paper presents a novel framework integrating Generative Artificial Intelligence (GenAI) into multi-cloud control planes for intelligent, predictive workload optimization. By synthesizing high-dimensional telemetry streams, workload dependencies, and pricing APIs into contextual multimodal embeddings, GenAI models predict utilization spikes, generate adaptive infrastructure topology configurations, and synthesize automated infrastructure-as-code (IaC) deployment policies in real time. The proposed framework employs transformer-based architecture combined with reinforcement learning from operational feedback to continuously refine resource balancing across AWS, Azure, and Google Cloud Platform. Experimental validation demonstrates that GenAI-driven orchestration reduces multi-cloud operational expenditure by 28%, decreases cross-cloud latency by 34%, and mitigates service-level agreement (SLA) breaches by 42% compared to conventional heuristic methods. This research establishes a scalable blueprint for autonomous, self-healing, and cost-aware enterprise cloud management powered by generative AI paradigms.
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