By Olivier Terzo, Lorenzo Mossucca
The quantity of information in way of life has been exploding. this knowledge raise has been particularly major in medical fields, the place titanic quantities of information has to be captured, communicated, aggregated, kept, and analyzed. This e-book describes how cloud computing can considerably enhance info administration in numerous clinical fields, corresponding to bioinformatics, earth technology, and desktop technological know-how. This booklet makes a speciality of how cloud computing is helping within the parts of source regulate, vertical and horizontal scalability, and adaptive scheduling.
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Extra info for Cloud Computing with e-Science Applications
E. Roloff, M. Diener, A. Carissimi, and P. O. A. Navaux. High performance computing in the cloud: deployment, performance and cost efficiency. In Proceedings of the 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CLOUDCOM), 2012, pp. 371–378. 2 Cloud-Based Infrastructure for Data-Intensive e-Science Applications: Requirements and Architecture Yuri Demchenko, Canh Ngo, Paola Grosso, Cees de Laat, and Peter Membrey CONTENTS Summary.................................................................................................................
Currently, the company owns products that cover all types of service models. Online Services is a set of products that are provided as SaaS, while Windows Azure provides both PaaS and IaaS. Windows Azure PaaS is a platform developed to provide the user the capability to develop and deploy a complete application into Microsoft’s infrastructure. To have access to this service, the user needs to develop an application following the provided framework. NET languages, Python, Java, and PHP. A generic framework is provided, in which the user can develop in any programming language that is supported by the Windows operating system (OS).
The first one is the cost of the cloud systems. This cost, in the great majority of cloud providers, is expressed as cost per hour. The second value is the overhead factor. To determine this factor, it is necessary to execute the same workload in all the candidate systems and in the base system. The overhead factor OF is the execution time in the candidate system ETCS divided by the execution time in the base system ETBS. The following equation represents this calculation: OF = ETCS ETBs As an example, we want to compare a traditional server against a machine in the cloud.
Cloud Computing with e-Science Applications by Olivier Terzo, Lorenzo Mossucca