处理大型医疗数据 (BHD) 工作负载的可持续方法
Tia Haddad1, Pushpa Kumarapeli1
1School of Computer Science and Mathematics, Kingston University London.
Studies in health technology and informatics
|May 17, 2025
概括
可持续的大型医疗数据 (BHD) 处理至关重要. 本研究介绍了一个使用微服务架构 (MSA) 的架构框架,以优化云扩展以减少环境影响.
科学领域:
- 计算机科学 计算机科学
- 环境科学 环境科学
- 医疗保健信息学 医疗保健信息学
背景情况:
- 越来越多的大型医疗数据 (BHD) 带来了重大的环境挑战.
- 需要可持续的解决方案来最大限度地减少BHD工作负载的生态足迹.
研究的目的:
- 提出一个试点架构框架,以提高BHD工作流程的可持续性.
- 优化云扩展策略,以提高医疗数据处理中的能源效率.
主要方法:
- 在BHD工作流程中利用微服务架构 (MSA).
- 将节能模型与自动缩放工具集成在一起.
- 使用Kubernetes事件驱动自动缩放 (KEDA) 和水平自动缩放 (HPA).
主要成果:
- 开发了一个试点架构框架.
- 该框架展示了优化云资源利用的潜力.
- 研究了自动缩放工具与节能模型的整合.
结论:
- 拟议的MSA框架为更可持续的BHD处理提供了一条途径.
- 优化云扩展可以显著减少医疗数据工作负载对环境的影响.
- 需要进一步的研究来验证该框架的可扩展性和长期有效性.
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