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Networking for Big Data

Udgivet af Taylor & Francis Ltd

Bogdetaljer

  • Hæftet
  • 432 sider
  • Engelsk
ISBN 9780367377533
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Networking for Big Data supplies an unprecedented look at cutting-edge research on the networking and communication aspects of Big Data. Starting with a comprehensive introduction to Big Data and its networking issues, it offers deep technical coverage of both theory and applications.The book is divided into four secti…

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Bogdetaljer
Forlag Taylor & Francis Ltd
Kategori Databaser
ISBN-13 9780367377533
EAN 9780367377533
Format Hæftet
Sprog Engelsk
sider 432
Produktbeskrivelse
Networking for Big Data supplies an unprecedented look at cutting-edge research on the networking and communication aspects of Big Data. Starting with a comprehensive introduction to Big Data and its networking issues, it offers deep technical coverage of both theory and applications.The book is divided into four sections: introduction to Big Data, networking theory and design for Big Data, networking security for Big Data, and platforms and systems for Big Data applications. Focusing on key networking issues in Big Data, the book explains network design and implementation for Big Data. It examines how network topology impacts data collection and explores Big Data storage and resource management.Addresses the virtual machine placement problemDescribes widespread network and information security technologies for Big DataExplores network configuration and flow scheduling for Big Data applicationsPresents a systematic set of techniques that optimize throughput and improve bandwidth for efficient Big Data transfer on the InternetTackles the trade-off problem between energy efficiency and service resiliencyThe book covers distributed Big Data storage and retrieval as well as security, trust, and privacy protection for Big Data collection, storage, and search. It discusses the use of cloud infrastructures and highlights its benefits to overcome the identified issues and to provide new approaches for managing huge volumes of heterogeneous data.The text concludes by proposing an innovative user data profile-aware policy-based network management framework that can help you exploit and differentiate user data profiles to achieve better power efficiency and optimized resource management.

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