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Fog computing framework for Big Data processing using cluster management in a resource-constraint environment

Fog computing framework for Big Data processing using cluster management in a resource-constraint environment

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This article presents the implementation details related to the distributed storage and processing of big datasets in fog computing cluster environment. The implementation details of fog computing framework using Apache Spark for big data applications in a resource-constrained environment are given. The results related to Big Data processing, modeling, and prediction in a resource-constraint fog computing framework are presented by considering the evaluation of case studies using the e-commerce customer dataset and bank loan credit risk datasets.

Chapter Contents:

  • 9.1 Introduction
  • 9.2 Literature survey
  • 9.2.1 Cluster computing
  • 9.2.1.1 Cluster computing using commodity hardware (Raspberry Pi)
  • 9.2.1.2 Load balancing algorithms
  • 9.2.2 Utility computing
  • 9.2.3 Peer-to-peer computing
  • 9.2.4 Distributed computing frameworks
  • 9.2.4.1 MapReduce
  • 9.2.4.2 Apache Hadoop
  • 9.2.4.3 Apache Spark
  • 9.2.4.4 Microsoft's DMTK and CNTK
  • 9.2.5 Gaps identified in the existing research work
  • 9.2.6 Objectives of the chapter
  • 9.3 System description
  • 9.4 Implementation details
  • 9.4.1 Using resource constraint device (Raspberry Pi)
  • 9.4.2 Spark fog cluster evaluation
  • 9.5 Results and discussion
  • 9.6 Conclusion and future work
  • References

Inspec keywords: cloud computing; bank data processing; cluster computing; retail data processing; Big Data

Other keywords: Apache Spark; cluster management; distributed storage; resource-constraint environment; fog computing cluster environment; e-commerce customer dataset; Big Data processing; bank loan credit risk datasets

Subjects: Retailing and distribution computing; Data handling techniques; Computer communications; Financial computing; Internet software; Other DBMS; Local area networks

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