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https://doi.org/10.1007/978-1-4615-1337-7aspects. While grid focuses on federating resources and fostering collaboration, cloud focuses on flexibility and on-demand provisioning of virtualized resources. Due to their complementarity, it is clear that both models, or at least some of their concepts and techniques, will coexist and cooperate

ARK

https://doi.org/10.1007/978-3-663-14572-1 cloud computing on scientific workflow applications. We examine the benefits and drawbacks of cloud computing for workflows, and argue that the primary benefit of cloud computing is not the economic model it promotes, but rather the technologies it employs and how they enable new features for workf

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在驾驶

https://doi.org/10.1007/978-3-663-01984-8n efficiently manage and offer a virtually unlimited number of resources and can minimize the costs incurred by organizations when providing Internet services. However, as Cloud providers often rely on large data centres to sustain their business and offer the resources that users need, the energy c

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https://doi.org/10.1007/978-3-658-35103-8ability is becoming increasingly important with increasing volumes of data, multiple sources of data as well as resource types. The primary aim of this chapter is to understand different ways in which application-level interoperability can be provided across distributed infrastructure. We achieve th

欢呼

Parameter Sweep Job Submission to Clouds,job submission to various Grids by the 3G Bridge. Then it proposes several possible solution variants how to extend the parameter sweep job submission mechanism of P-GRADE and 3G Bridge toward Cloud systems. Finally, it shows the results of performance measurements that were gained for the proposed solution variants.

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