Toward scalable internet traffic measurement and analysis with Hadoop

  • Authors:

  • Yeonhee Lee;Youngseok Lee

  • Affiliations:

  • Dept. of Computer Engineering, Chungnam National University, Daejon, South Korea;Dept. of Computer Engineering, Chungnam National University, Daejon, South Korea

  • Venue:
  • ACM SIGCOMM Computer Communication Review
  • Year:
  • 2012

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Abstract

Internet traffic measurement and analysis has long been used to characterize network usage and user behaviors, but faces the problem of scalability under the explosive growth of Internet traffic and high-speed access. Scalable Internet traffic measurement and analysis is difficult because a large data set requires matching computing and storage resources. Hadoop, an open-source computing platform of MapReduce and a distributed file system, has become a popular infrastructure for massive data analytics because it facilitates scalable data processing and storage services on a distributed computing system consisting of commodity hardware. In this paper, we present a Hadoop-based traffic monitoring system that performs IP, TCP, HTTP, and NetFlow analysis of multi-terabytes of Internet traffic in a scalable manner. From experiments with a 200-node testbed, we achieved 14 Gbps throughput for 5 TB files with IP and HTTP-layer analysis MapReduce jobs. We also explain the performance issues related with traffic analysis MapReduce jobs.