SETI@home: an experiment in public-resource computing
Communications of the ACM
Production Storage Resource Broker Data Grids
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
Queue - Virtualization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
CTO Roundtable: Virtualization Part II
Communications of the ACM - Surviving the data deluge
MediGRID: Towards a user friendly secured grid infrastructure
Future Generation Computer Systems
Journal of Theoretical and Applied Electronic Commerce Research
E-EPR: a cloud-based architecture of an electronic emergency patient record
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Personal health record architectures: Technology infrastructure implications and dependencies
Journal of the American Society for Information Science and Technology
From cloud computing to cloud manufacturing
Robotics and Computer-Integrated Manufacturing
Knowledge cloud system for network collaboration: A case study in medical service industry in China
Expert Systems with Applications: An International Journal
Cloud computing environments for biomedical data services
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Assessing the Usability of a Science Gateway for Medical Knowledge Bases with TRENCADIS
Journal of Grid Computing
Resource virtualization methodology for on-demand allocation in cloud computing systems
Service Oriented Computing and Applications
Implementation of Telecytology in Georgia for Quality Assurance Programs
Journal of Information Technology Research
Cloud based intelligent system for delivering health care as a service
Computer Methods and Programs in Biomedicine
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We examine how the biomedical informatics (BMI) community, especially consortia that share data and applications, can take advantage of a new resource called ''cloud computing''. Clouds generally offer resources on demand. In most clouds, charges are pay per use, based on large farms of inexpensive, dedicated servers, sometimes supporting parallel computing. Substantial economies of scale potentially yield costs much lower than dedicated laboratory systems or even institutional data centers. Overall, even with conservative assumptions, for applications that are not I/O intensive and do not demand a fully mature environment, the numbers suggested that clouds can sometimes provide major improvements, and should be seriously considered for BMI. Methodologically, it was very advantageous to formulate analyses in terms of component technologies; focusing on these specifics enabled us to bypass the cacophony of alternative definitions (e.g., exactly what does a cloud include) and to analyze alternatives that employ some of the component technologies (e.g., an institution's data center). Relative analyses were another great simplifier. Rather than listing the absolute strengths and weaknesses of cloud-based systems (e.g., for security or data preservation), we focus on the changes from a particular starting point, e.g., individual lab systems. We often find a rough parity (in principle), but one needs to examine individual acquisitions-is a loosely managed lab moving to a well managed cloud, or a tightly managed hospital data center moving to a poorly safeguarded cloud?