Networking for Big Data von Shui (Hrsg.) Yu

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ISBN: 978-1-4822-6349-7
Einband: Fester Einband
Verfügbarkeit: Lieferbar in ca. 10-20 Arbeitstagen
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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.

"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. ?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."-D. Papamichail, The College of New Jersey, Choice, May 2016

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.

"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. ?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."-D. Papamichail, The College of New Jersey, Choice, May 2016
AutorYu, Shui (Hrsg.) / Lin, Xiaodong (Hrsg.) / Misic, Jelena (Hrsg.) / Shen, Xuemin (Sherman) (Hrsg.)
EinbandFester Einband
Erscheinungsjahr2015
Seitenangabe432 S.
LieferstatusLieferbar in ca. 10-20 Arbeitstagen
AusgabekennzeichenEnglisch
AbbildungenFarb., s/w. Abb.
MasseH25.4 cm x B17.8 cm 975 g
CoverlagChapman and Hall/CRC (Imprint/Brand)
VerlagTaylor and Francis

Über den Autor Shui (Hrsg.) Yu

Dr. Shui Yu received the B.Eng. and M.Eng. degrees from University of Electronic Science and Technology of China, Chengdu, P. R. China, in 1993 and 1999, respectively, and the Ph.D. degree from Deakin University, Victoria, Australia, in 2004. He is currently a Senior Lecturer with the School of Information Technology, Deakin University, Melbourne, Australia. He has published around 100 peer review papers; many of them are in top journals and top conferences, such as IEEE TPDS, IEEE TIFS, IEEE TFS, IEEE TMC, and IEEE INFOCOM. His research interests include networking theory, network security, and mathematical modeling. Dr. Yu actively serves his research communities in various roles, which include the editorial boards of IEEE Transactions on Parallel and Distributed Systems, IEEE Communications Surveys and Tutorials, and three other international journals, TPC of IEEE INFOCOM, symposium co-chairs of IEEE ICC 2014, IEEE ICNC 2013 and 2104, and many different roles of international conference organizing committees. He is a senior member of IEEE, and a member of AAAS. Dr. Song Guo received the PhD degree in computer science from the University of Ottawa, Canada in 2005. He is currently a Senior Associate Professor at School of Computer Science and Engineering, the University of Aizu, Japan. His research interests are mainly in the areas of protocol design and performance analysis for reliable, energy-efficient, and cost effective communications in wireless networks. Dr. Guo is an associate editor of the IEEE Transactions on Parallel and Distributed Systems and three other journals, and an editor of Wireless Communications and Mobile Computing. He has published over 120 papers in referred journals and conferences. He received the Best Paper Awards at IEEE HPCC 2008 and IEEE SCE 2011, the Outstanding Leadership Award at IEEE ISPA 2008, the Outstanding Service Award at IEEE ICESS 2010, and the prestigious Information Technology Award of Funai Foundation for Information Technology 2008. He guest edited special issues in IEEE Transactions on Emergent Topics in Computing, and four other journals. He has also served on organizing committees and program committees for over 70 international conferences and workshops, like IEEE INFOCOM 2012, IEEE MASS 2011, IEEE WCNC 2008-2012, PIMRC 2009-2011, etc. He is a senior member of the IEEE and the ACM.

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