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Simple and Efficient Identification of Heavy Hitters Based on Bitcount

Feng Wang,Lixin Gao

Published 2019 in International Conference on High Performance Switching and Routing

ABSTRACT

Detecting and identifying heavy hitters on very high speed links is important for many network management applications, ranging from detecting network attacks and traffic anomalies, to relieving link congestion, planning network capacity and traffic engineering. Most existing solutions to the problem are based on sampling and sketch-based techniques. Sampling-based solutions use partial observations of packets or flow records to extract flow-level information. Sketch-based approaches use a compressed data structure to keep a summary of the original data and estimate traffic statistics such as flow size for all traffic flows. However, these approaches either induce information losses due to sampling or incur computational and space overheads for key recovery. In this paper, we propose a novel method, Bitcount, which uses a flexible counter array to detect and identify heavy hitters. Both theoretical analysis and experimental evaluations show that Bitcount can provide higher precision for identifying heavy hitters with low memory and computational overheads.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    International Conference on High Performance Switching and Routing

  • Publication date

    2019-05-26

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.1109/HPSR.2019.8808101
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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