Abstract-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 taskspaces, 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.
Image registration was used as the driver of an investigation into virtual computational cloud that integrates local computational environments and public cloud services on-the-fly, and support image registration requests from different distributed research groups REF .
Investigating the use of autonomic cloudbursts for high-throughput medical image registration
2009 10th IEEE/ACM International Conference on Grid Computing
2009 10th IEEE/ACM International Conference on Grid Computing
Computer Science