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Design and Practice of Elastic Scaling Mechanism for Medical Cloud-Edge Collaborative Architecture

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  • Zhengyang Qi

    (University of California, Irvine, CA, 92697, US)

Abstract

Medical test peaks can triple within a few minutes, while traditional threshold-based scaling lags by 10 minutes and incurs a 45% higher cost, resulting in report delays exceeding 6 hours. This study proposes an integrated mechanism of “edge preprocessing + cloud elasticity + prediction trigger.” The edge filters invalid data in real time and reports the load, while the cloud pool scales up within 5 minutes based on “CPU > 75%” or “Flu-Prophet seasonal prediction.” Docker NS ensures CLIA-compliant hard isolation for multi-tenants. The experiment, based on 41 million real orders, maintained 99.93% availability and stabilized report issuance time at 2.4 hours under a 3.2× peak on Black Friday, with a 22% reduction in cloud bills. This study is the first to embed medical seasonal events into a cloud-edge collaborative closed loop, achieving non-collapse during peaks, cost savings, and compliance for easy replication in grassroots medical clouds.

Suggested Citation

  • Zhengyang Qi, 2025. "Design and Practice of Elastic Scaling Mechanism for Medical Cloud-Edge Collaborative Architecture," Journal of Innovations in Medical Research, Paradigm Academic Press, vol. 4(5), pages 13-18, October.
    • Handle: RePEc:bdz:joimer:v:4:y:2025:i:5:p:13-18
    DOI: 10.63593/JIMR.2788-7022.2025.10.003
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