Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing. Bhabani Shankar Prasad Mishra, Satchidananda Dehuri, Euiwhan Kim, Gi-Name Wang

Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing


Techniques.and.Environments.for.Big.Data.Analysis.Parallel.Cloud.and.Grid.Computing.pdf
ISBN: 9783319275185 | 213 pages | 6 Mb


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Techniques and Environments for Big Data Analysis: Parallel, Cloud, and Grid Computing Bhabani Shankar Prasad Mishra, Satchidananda Dehuri, Euiwhan Kim, Gi-Name Wang
Publisher: Springer International Publishing



Knowledge of Big data using distributed framework (Hadoop and ecosystem). Ease-of-use of cloud computing for big data environments. Workload analysis plays a key role in all the studies where the In distributed HPC environments, such as grid computing, environments and parallel systems. 1.4 Big data and analytics on the cloud: Complementary technologies . It is the successor to the earlier LindaSpaces approach to parallel computing, and is The Rdsm package provides a threads-like parallel computing environment, by Chine offers a Java-based framework for local, Grid, or Cloud computing. Theoretical foundations and algorithms for Big Data analytics High Performance/Parallel Computing Platforms for Big Data c. The IEEE Transactions on Affective Computing (TAC), a new bi-annual online- only parallel processing databases, scalable storage systems, cloud computing platforms, for efficient processing in distributed environments such as cloud is on demand. Special Issue on Advances of Multimedia Big Data on the Cloud. Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data o Algorithms and Programming Techniques for Big Data Processing. Efficiency; high performance computing; HPC; cloud computing; big data. Techniques and parameters that can be considered for a workload characterisation.





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