Networking for Big Data (Chapman & Hall/CRC Big Data: Aims and Scope) 🔍
Shui Yu, Xiaodong Lin, Jelena Misic, Xuemin (Sherman) Shen (eds.) CRC Press, Taylor & Francis Group; Chapman and Hall/CRC, Chapman & Hall/CRC Big Data Series, 2015
英语 [en] · PDF · 8.6MB · 2015 · 📘 非小说类图书 · 🚀/lgli/lgrs/nexusstc/zlib · Save
描述
**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.
备用文件名
lgli/Networking_for_Big_Data-1482263491.pdf
备用文件名
lgrsnf/Networking_for_Big_Data-1482263491.pdf
备用文件名
zlib/Computers/Networking/Shui Yu, Xiaodong Lin, Jelena Misic, Xuemin (Sherman) Shen (eds.)/Networking for Big Data_2628077.pdf
备选作者
Yu, Shui (Computer scientist), Lin, Xiaodong (Computer scientist), Jelena Mišić, X. Shen
备选作者
edited by Shui Yu, Xiaodong Lin, Jelena Mišić, Xuemin (Sherman) Shen
备用出版商
Chapman & Hall Crc
备用版本
Chapman & Hall/CRC big data series, Boca Raton, Florida, 2016
备用版本
Chapman & Hall/CRC big data series, 2, Boca Raton, 2016
备用版本
CRC Press (Unlimited), Boca Raton, 2016
备用版本
United States, United States of America
备用版本
Boca Raton, 2015
备用版本
1, PS, 2015
元数据中的注释
0
元数据中的注释
lg1417991
元数据中的注释
{"isbns":["1482263491","9781482263497"],"last_page":432,"publisher":"Chapman and Hall/CRC","series":"Chapman & Hall/CRC Big Data Series"}
元数据中的注释
Includes bibliographical references and index.
备用描述
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. 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. -- Provided by publisher
开源日期
2015-12-17
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