AI-Enabled Multicloud Architecture for Real-Time Healthcare Analytics with Enterprise Storage and SAP Workloads
DOI:
https://doi.org/10.15680/13q7vy95Keywords:
Artificial Intelligence, Multicloud Architecture, Healthcare Analytics, Enterprise Storage, SAP Workloads, Real-Time Data Processing, Workload ReliabilityAbstract
The rapid expansion of digital health services has underscored the necessity for resilient, scalable, and intelligent computing infrastructure capable of supporting real-time data processing and autonomous diagnostics. Current healthcare technology implementations often rely on monolithic single-cloud deployments or on-premises systems that struggle under high workload variability and provide limited fault tolerance. This study proposes a Unified Multicloud AI Architecture designed to harness distributed computing resources from multiple cloud vendors to deliver real-time analytics and autonomous healthcare diagnostics with robust enterprise workload reliability. The architecture integrates real-time stream processing, distributed machine learning inference, and automated service failover using cloud orchestration technologies. We evaluate the framework through simulated healthcare workloads involving continuous monitoring data, diagnostic imaging, and electronic health record analysis to measure latency, throughput, diagnostic accuracy, and system availability under normal and degraded conditions. Results indicate improved responsiveness, higher overall uptime, enhanced diagnostic performance, and reduced service interruptions compared to traditional single-cloud solutions. These findings demonstrate that a unified multicloud approach offers tangible benefits for mission-critical healthcare applications, addressing challenges related to vendor lock-in, disaster recovery, and compliance with healthcare regulations. The research provides a foundation for scalable and reliable healthcare AI systems in real-world environments.
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