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Recently, the computational requirements for large scale data-intensive analysis of scientific data have grown significantly. In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. This huge amount of data are processed on more than 140 computing centers distributed across 34 countries. The MapReduce paradigm has emerged as a highly successful...
The Software as a Service (SaaS) methodology is a key paradigm of Cloud computing. In this paper, we focus on an interesting topic - to implement a Cloud computing functionality, the SaaS model, on existing production Grid infrastructures. In general, production Grids employ a Job-Submission-Execution (JSE) model with rigid access interfaces. In this paper we develop the Cyberaide onServe, a lightweight...
Virtual machines offer unique advantages to the scientific computing community, such as Quality of Service(QoS) guarantee, performance isolation, easy resource management, and the on-demand deployment of computing environments. Using virtual machines as a computing resource within a distributed environment, such as Service Oriented Architecture (SOA), creates a variety of new issues and challenges...
Cloud computing emerges as a new computing paradigm which aims to provide reliable, customized and QoS guaranteed computing dynamic environments for end-users. This paper reviews recent advances of Cloud computing, identifies the concepts and characters of scientific Clouds, and finally presents an example of scientific Cloud for data centers
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