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  • © 2017

Traffic Measurement for Big Network Data

  • Introduces a new concept, virtual data structures, that measures traffic in a compact way
  • Offers insight into one of the world’s most common types of data
  • Covers a fast and scalable counter architecture called Counter Tree
  • Includes supplementary material: sn.pub/extras

Part of the book series: Wireless Networks (WN)

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Table of contents (4 chapters)

  1. Front Matter

    Pages i-vii
  2. Introduction

    • Shigang Chen, Min Chen, Qingjun Xiao
    Pages 1-9
  3. Per-Flow Size Measurement

    • Shigang Chen, Min Chen, Qingjun Xiao
    Pages 11-45
  4. Per-Flow Cardinality Measurement

    • Shigang Chen, Min Chen, Qingjun Xiao
    Pages 47-76
  5. Persistent Spread Measurement

    • Shigang Chen, Min Chen, Qingjun Xiao
    Pages 77-104

About this book

This book presents several compact and fast methods for online traffic measurement of big network data. It describes challenges of online traffic measurement, discusses the state of the field, and provides an overview of the potential solutions to major problems.


The authors introduce the problem of per-flow size measurement for big network data and present a fast and scalable counter architecture, called Counter Tree, which leverages a two-dimensional counter sharing scheme to achieve far better memory efficiency and significantly extend estimation range. 


Unlike traditional approaches to cardinality estimation problems that allocate a separated data structure (called estimator) for each flow, this book takes a different design path by viewing all the flows together as a whole: each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. A framework of virtual estimators is designed to apply the idea of sharing to an array of cardinality estimation solutions, achieving far better memory efficiency than the best existing work. 


To conclude, the authors discuss persistent spread estimation in high-speed networks. They offer a compact data structure called multi-virtual bitmap, which can estimate the cardinality of the intersection of an arbitrary number of sets. Using multi-virtual bitmaps, an implementation that can deliver high estimation accuracy under a very tight memory space is presented. 


The results of these experiments will surprise both professionals in the field and advanced-level students interested in the topic. By providing both an overview and the results of specific experiments, this book is useful for those new to online traffic measurement and experts on the topic.




Authors and Affiliations

  • Department of Computer & Information Science, University of Florida, Gainesville, USA

    Shigang Chen, Min Chen

  • School of Computer Science and Engineering, Southeast University of China, Nanjing, China

    Qingjun Xiao

Bibliographic Information

Buy it now

Buying options

eBook USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access