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医疗云平台资源调度策略研究

史宝鹏,段迅,孔广黔,吴云   

  1. (贵州大学 计算机科学与技术学院,贵阳 550025)
  • 收稿日期:2016-12-20 出版日期:2017-08-15 发布日期:2017-08-15
  • 作者简介:史宝鹏(1990—),男,硕士研究生,主研方向为云平台、OpenStack、资源调度优化;段迅(通信作者)、孔广黔、吴云,副教授、博士。
  • 基金项目:
    中央引导地方科技发展专项“贵州省基层远程诊断服务平台云数据中心”(黔科中引地[2016]4008号)。

Research on Resource Scheduling Strategy of Medical Cloud Platform

SHI Baopeng,DUAN Xun,KONG Guangqian,WU Yun   

  1. (College of Computer Science and Technology,Guizhou University,Guiyang 550025,China)
  • Received:2016-12-20 Online:2017-08-15 Published:2017-08-15

摘要: 针对云平台中资源调度策略过于简单,不能有效适应医疗业务需求的问题,分析不同医疗系统对资源的不同需求,以此为根据提出IB-Choose资源调度策略。基于OpenStack平台构建包含医生诊疗系统、实验科检验系统和影像归档系统的医疗云平台,并在该平台上实现IB-Choose策略。实验结果表明,与OpenStack默认资源调度策略Chance相比,IB-Choose可将启动虚拟机的服务时间缩短25%~30%,同时减少云资源开销并提高其利用率。

关键词: 大数据, 云平台, OpenStack平台, 资源调度优化, 医疗系统, 虚拟化

Abstract: The resource scheduling policies for the cloud platform are too simple to meet the needs of the medical service effectively.Aiming at this problem,this paper analyzes different demands of different medical systems on resources and proposes the IB-Choose resource scheduling strategy on this basis.It builds a medical cloud platform with doctor diagnosis and treatment system,laboratory test system and image archiving system based on OpenStack platform,and implements the IB-Choose strategy on this platform.Experimental results show that,compared with the default resource scheduling policy in OpenStack,named Chance,the IB-Choose resource scheduling policy can shorten the service time of starting virtual machines by 25%~30%.At the same time,it reduces the cloud resources overhead and improves the utilization rate of cloud resources.

Key words: big data, cloud platform, OpenStack platform, resource scheduling optimization, medical system, virtualization

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