rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Traffic Measurement for Big Network Data
  name:
    name part: Chen, Shigang.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Chen, Min.
    role:
      role term: author.
  name:
    name part: Xiao, Qingjun.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: VII, 104 p. 45 illus., 2 illus. in color. online resource.
  abstract: 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.
  table of contents: Introduction -- Per-Flow Size Measurement -- Per-Flow Cardinality Measurement -- Persistent Spread Measurement.
  note: by Shigang Chen, Min Chen, Qingjun Xiao.
  subject:
    topic: Engineering
  subject:
    topic: Computer communication systems
  subject:
    topic: Electrical engineering
  subject:
    topic: Engineering
  subject:
    topic: Communications Engineering, Networks
  subject:
    topic: Computer Communication Networks
  subject:
    topic: Information Systems Applications (incl. Internet)
  classification: TK1-9971
  classification: 621.382
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Wireless Networks
  identifier: 9783319473406
  identifier: http://dx.doi.org/10.1007/978-3-319-47340-6
  record info:
    record creation date: 161101
    record change date: 20161101110804.0
    record identifier: 978-3-319-47340-6