Machine Learning–Enhanced Citrix Framework for Zero-Downtime Data Exchange and DC–DC Converter Optimization in Mobile Cloud Ecosystems
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Abstract
The convergence of mobile cloud computing, machine learning (ML), and power electronics offers a transformative approach to data exchange and resource optimization. This paper proposes a Machine Learning–Enhanced Citrix Framework designed to achieve zero-downtime data exchange and adaptive DC–DC converter optimization within distributed mobile cloud ecosystems. The framework integrates Citrix virtualization for seamless workload migration and ML-driven predictive analytics to ensure continuous data flow and energy efficiency across edge and cloud nodes. Using dynamic voltage regulation and converter control algorithms, the model minimizes latency and enhances reliability in high-demand data environments. The study further evaluates system scalability, energy efficiency, and network resilience under varying load conditions. Experimental results demonstrate that the proposed framework significantly reduces downtime, improves data throughput, and achieves optimal power utilization, making it suitable for next-generation intelligent cloud infrastructures.
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