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Cloud Networking for Big Data Softcover reprint of the original 1st ed. 2015 [Mīkstie vāki]

  • Formāts: Paperback / softback, 102 pages, height x width: 235x155 mm, weight: 454 g, 1 Illustrations, color; 36 Illustrations, black and white; XIV, 102 p. 37 illus., 1 illus. in color., 1 Paperback / softback
  • Sērija : Wireless Networks
  • Izdošanas datums: 21-Mar-2019
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319796666
  • ISBN-13: 9783319796666
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  • Mīkstie vāki
  • Cena: 46,91 €*
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  • Standarta cena: 55,19 €
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  • Formāts: Paperback / softback, 102 pages, height x width: 235x155 mm, weight: 454 g, 1 Illustrations, color; 36 Illustrations, black and white; XIV, 102 p. 37 illus., 1 illus. in color., 1 Paperback / softback
  • Sērija : Wireless Networks
  • Izdošanas datums: 21-Mar-2019
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319796666
  • ISBN-13: 9783319796666
Citas grāmatas par šo tēmu:

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.

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.