Investigating the Use of Cloudbursts for High-Throughput Medical Image Registration.
Investigating the Use of Cloudbursts for High-Throughput Medical Image Registration.
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研究使用暴雨进行高通量医学图像配准。
DOI：
10.1109/grid.2009.5353065
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Yang,Lin
中科院分区：
文献类型：
--
作者：
Kim,Hyunjoo;Parashar,Manish;Foran,DavidJ;Yang,Lin
本文研究了使用云和自主云爆发，以支持医学图像配准。我们的目标是实现一个虚拟计算云，它集成了本地计算环境和公共云服务，并支持来自不同分布式研究人员团体的图像配准请求，这些研究人员团体具有不同的计算要求和QoS约束。虚拟云本质上实现了共享和协调的任务空间，它协调了由一组动态的研究小组提交到其本地作业队列的作业的调度。策略驱动的调度代理使用QoS约束沿着性能历史和资源状态来确定应分配给特定请求的公共云和私有云资源的适当大小和组合。虚拟计算云和医学图像配准服务是使用CometCloud引擎开发的，并已部署在罗格斯大学和新泽西癌症研究所的私有云以及Amazon EC2上。一个实验评估，并证明了自主的云暴和基于策略的自主调度这个应用程序的有效性。
This paper investigates the use of clouds and autonomic cloudbursting to support a medical image registration. The goal is to enable a virtual computational cloud that integrates local computational environments and public cloud services on-the-fly, and support image registration requests from different distributed researcher groups with varied computational requirements and QoS constraints. The virtual cloud essentially implements shared and coordinated task-spaces, which coordinates the scheduling of jobs submitted by a dynamic set of research groups to their local job queues. A policy-driven scheduling agent uses the QoS constraints along with performance history and the state of the resources to determine the appropriate size and mix of the public and private cloud resource that should be allocated to a specific request. The virtual computational cloud and the medical image registration service have been developed using the CometCloud engine and have been deployed on a combination of private clouds at Rutgers University and the Cancer Institute of New Jersey and Amazon EC2. An experimental evaluation is presented and demonstrates the effectiveness of autonomic cloudbursts and policy-based autonomic scheduling for this application.