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Cloud networking for big data

By: Zeng, Deze
Title By: Gu, Lin | Guo, Song
Material type: BookSeries: Publisher: Cham : Springer, c2015.Description: xiv, 102 p. : ill. ; 25 cm.ISBN: 978-3319247182Subject(s): Cloud computing | Big data | COMPUTERS / Computer Science | COMPUTERS / Data Processing | COMPUTERS / Information Technology | COMPUTERS / Machine TheoryDDC classification: 004.6782 ZE CL Online resources: Location Map
Summary:
This book introduces two basic big data processing paradigms for batch data and streaming data. Representative programming frameworks are also presented, as well as software defined networking (SDN) and network function virtualization (NFV) technologies as key cloud networking technologies. The authors illustrate that SDN and NFV can be applied to benefit the big data processing by proposing a cloud networking framework. Based on the framework, two case studies examine how to improve the cost efficiency of big data processing. Cloud Networking for Big Data targets professionals and researchers working in big data, networks, wireless communications and information technology. Advanced-level students studying computer science and electrical engineering will also find this book valuable as a study guide.
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Item type Home library Call number Status Date due Barcode Item holds
REGULAR University of Wollongong in Dubai
Main Collection
004.6782 ZE CL (Browse shelf) Available T0053958
Total holds: 0

Networking Evolution towards Cloud Networking.- Background Introduction.- Fundamental Concepts.- Cloud Networking.- Cost Efficient Big Data Processing in Cloud Networking enabled Data Centers.- Cost Minimization for Big Data Processing in Geo-Distributed Data Centers.- A General Communication Cost Optimization Framework for Big Data Stream Processing in Geo-distributed Data Centers.- Conclusion and Future Work.

This book introduces two basic big data processing paradigms for batch data and streaming data. Representative programming frameworks are also presented, as well as software defined networking (SDN) and network function virtualization (NFV) technologies as key cloud networking technologies. The authors illustrate that SDN and NFV can be applied to benefit the big data processing by proposing a cloud networking framework. Based on the framework, two case studies examine how to improve the cost efficiency of big data processing. Cloud Networking for Big Data targets professionals and researchers working in big data, networks, wireless communications and information technology. Advanced-level students studying computer science and electrical engineering will also find this book valuable as a study guide.

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