New PDF release: Cloud Computing for Data-Intensive Applications

By Xiaolin Li, Judy Qiu

ISBN-10: 1493919040

ISBN-13: 9781493919048

ISBN-10: 1493919059

ISBN-13: 9781493919055

This publication offers various cloud computing systems for data-intensive medical purposes. It covers platforms that carry infrastructure as a carrier, together with: HPC as a carrier; digital networks as a provider; scalable and trustworthy garage; algorithms that deal with tremendous cloud assets and functions runtime; and programming versions that let pragmatic programming and implementation toolkits for eScience purposes. Many clinical purposes in clouds also are brought, akin to bioinformatics, biology, climate forecasting and social networks. such a lot chapters comprise case experiences. Cloud Computing for Data-Intensive purposes ambitions advanced-level scholars and researchers learning desktop technology and electric engineering. execs operating in cloud computing, networks, databases and extra also will locate this e-book precious as a reference.

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This naturally happens in the educational research community quite frequently. Second, to efficiently utilize the compute and data infrastructure researchers may not run Hadoop or MPI jobs continuously. At times they may need a Hadoop environment. At other times they may prefer a traditional message passing environment while at the same time being under resource constraints. The idea of myHadoop is to submit a job to the queuing system that sets up a Hadoop cluster for the length of the reservation and the researcher can then use it to conduct experiments either via predefined jobs or in interactive mode.

As part of our project management in FutureGrid, we have designed a simple project application procedure that includes prior to a project being granted access, gathers information about which technologies are anticipated to be used within the project. The list of technologies is fairly extensive and includes Grid, HPC, and Cloud computing systems, services, and software. However, for this paper we will focus primarily on technologies that are dominantly requested and depicted in Fig. 9. Clearly we can identify the trend that shows the increased popularity of OpenStack within the services offered on FutureGrid.

13. Bertram Ludäscher, Ilkay Altintas, Chad Berkley, Dan Higgins, Efrat Jaeger, Matthew Jones, Edward A. Lee, Jing Tao, and Yang Zhao. Scientific workflow management and the Kepler system. Concurrency and Computation: Practice and Experience, 18(10):1039–1065, Aug. 2006. 14. M. Mao and M. Humphrey. Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In Proceedings of the 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC’11), 2011.

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Cloud Computing for Data-Intensive Applications by Xiaolin Li, Judy Qiu


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