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Chapman & Hall/CRC big data series
Physical Description:
xxii, 410 pages : illustrations ; 26 cm


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Item Category 1
30000010343579 QA76.9.C92 N48 2016 Open Access Book Book

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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 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.

Author Notes

Shui Yu, Xiaodong Lin, Jelena Misić, Xuemin (Sherman) Shen

Reviews 1

Choice Review

Big data commonly describes amounts of information that cannot be easily processed using traditional computational techniques. The term is relatively new, but the underlying issues have existed for a long time. Still, the new term and the application domains that encompass it have brought forth a renaissance of new techniques developed specifically for processing big data. This edited collection of contributions provides articles that explain issues surrounding big data processing, recent advancements in moving and storing big data, techniques utilizing big data to detect intrusions and securely transport information, as well as other topics such as cloud storage, data management, and analytics. The four editors are well published researchers in the field, and the contributed chapters were solicited from appropriate experts. As expected in an edited volume, articles tend to be somewhat disparate, have varying quality, and often require a wide range of prerequisites to fully comprehend the material. The editors made a good effort to include several review articles with the appropriate level of detail. Overall, the volume will serve as a useful reference on recent advancements in the field. Summing Up: Recommended. Graduate students, researchers/faculty, and professionals/practitioners. --Dimitris Papamichail, The College of New Jersey

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