Online traffic measurement and analysis in big data

Comparative research review

Lena T. Ibrahim, Rosilah Hassan, Kamsuriah Ahmad, Asrul Nizam Asat, Halizah Omar

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The Internet traffic measurement and analysis is important to avoid many problems of data transferring via online networks such as data losing and slow data transferring. The Internet traffic measuring and analysis could be effective to avoid the data transferring challenges. The Internet traffic measuring and analysis flexibility is important due to many reasons such as dynamicity of transferred data such as size and format, the data transferring protocols and the dynamicity of measure the traffic based on the networks available resources depend on the transferred data characteristics. The main objective of this paper is to review the most flexible Internet traffic measuring and analysis tools that could be adopted to handle the dynamicity of data transferring characteristics. The significance results show that the Hadoop/MapReduce tool has many advantages over other traffic measuring and analysis tools. The Hadoop/MapReduce features are easy to be modified based on various selections of Internet traffic measuring, the Hadoop/MapReduce is compatible with various format of data transferring such as texts, videos and images and the Hadoop/MapReduce can analyze the better ways of data transferring depend on many transferring protocols such as Transmission Control Protocol (TCP) and User Datagram Protocol (UDP).

Original languageEnglish
Pages (from-to)420-431
Number of pages12
JournalAmerican Journal of Applied Sciences
Volume13
Issue number4
DOIs
Publication statusPublished - 27 Apr 2016

Fingerprint

Internet
Network protocols
Transmission control protocol
Big data

Keywords

  • Data Transferring
  • Flexibility
  • Hadoop/MapReduce
  • Traffic Analysis
  • Traffic Measurement

ASJC Scopus subject areas

  • General

Cite this

Online traffic measurement and analysis in big data : Comparative research review. / Ibrahim, Lena T.; Hassan, Rosilah; Ahmad, Kamsuriah; Asat, Asrul Nizam; Omar, Halizah.

In: American Journal of Applied Sciences, Vol. 13, No. 4, 27.04.2016, p. 420-431.

Research output: Contribution to journalArticle

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