August 3, 2015 by Chapman and Hall/CRC
Reference – 432 Pages – 159 B/W Illustrations
ISBN 9781482263497 – CAT# K24371
Series: Chapman & Hall/CRC Big Data Series
Supplies an unprecedented look at the networking and communication aspects of Big Data
Offers deep technical coverage of both theory and applications
Explains network design and implementation for Big Data
Explores Big Data storage and resource management
Addresses network security, privacy, and trust
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 problem
Describes widespread network and information security technologies for Big Data
Explores network configuration and flow scheduling for Big Data applications
Presents a systematic set of techniques that optimize throughput and improve bandwidth for efficient Big Data transfer on the Internet
Tackles the trade-off problem between energy efficiency and service resiliency
The 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.