Networking for Big Data

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applications
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Big Data
Big Data Applications
Big Data Management
Big Data Transfer
Big Network Data
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cloud
Cloud Computing
Competitive Ratio
computing
data centre networking
Data Set
detection
distributed
Distributed Le Systems
energy efficient big data infrastructure
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HDFS
IBM ILOG CPLEX Optimization Studio
intrusion
Intrusion Detection
MapReduce Framework
network topology optimisation
Network Trac
NoSQL Database
openflow
OpenFlow Controller
OpenFlow Switch
Power Consumption
privacy protection techniques
Proposed System Model
resource management algorithms
Science Big Data
security protocols in data systems
SLA Violation
systems
TCP Connection
Trac Ow
Trac Ows
transfers
virtual machine allocation

Product details

  • ISBN 9780367377533
  • Weight: 453g
  • Dimensions: 178 x 254mm
  • Publication Date: 25 Sep 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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.

Shui Yu, Xiaodong Lin, Jelena Miši?, Xuemin (Sherman) Shen