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A New Algorithm for Long Flows Statistics—MGCBF

  • Zhou Mingzhong
  • Gong Jian
  • Ding Wei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3994)

Abstract

Long flows identification and characteristics analysis play more and more important role in modern traffic analysis because long flows take main traffic payload of network. Based on the flows length distribution and long flows characteristics of the Internet, this paper presents a novel long flows’ counting and information maintenance algorithm called Multi-granularity Counting Bloom Filter (MGCBF). Using a little fix memory, the MGCBF maintains the counters for all incoming flows with small error probability, and keeps information of long flows whose length are bigger than an optional threshold set by users. This paper builds up an architecture for long flows’ information statistics based on this algorithm. And the space used, calculation complexity and error probability of this architecture are also discussed at following. The experiment applied this architecture on the CERNET TRACEs, which indicates that the MGCBF algorithm can reduce the resource usage in counting flows and flows information maintenance dramatically with losing little measurement’s accuracy.

Keywords

Error Probability Flow Information Flow Number Flow Identification Bloom Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhou Mingzhong
    • 1
  • Gong Jian
    • 1
  • Ding Wei
    • 1
  1. 1.Department of Computer Science and EngineeringSoutheast UnivercityNanjingChina

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