Scalable and Real-Time Deep Packet Inspection

Research output: Contribution to conferencesPaperContributedpeer-review



Internet traffic has continued to grow at a spectacular rate over the past ten years. Understanding and managing network traffic have become an important issue for network operators to meet service-level agreements with their customers. In addition, the emergence of high-speed networks, such as 20 Gbps, 40Gbps Ethernet and beyond, requires fast analysis of a large volume of network traffic and this is beyond the capabilities of a single machine. Distributed parallel processing schemes have recently been developed to analyze high quantities of traffic data. However, scalable Internet traffic analysis in real-time is difficult because of a large dataset requires high processing intensity. In this paper, we describe a real-time Deep Packet Inspection (DPI) system based on the MapReduce programming model. We combine a stand-alone classification engine (L7-filter) with the distributed programming MapReduce model. Our experimental results show that the MapReduce programming paradigm is a useful approach for building highly scalable real-time network traffic processing systems. We generate 20 Gbps network traffic to validate the real-time analysis ability of the proposed system.


Original languageEnglish
Number of pages6
Publication statusPublished - 2013


TitleWorkshop on Distributed Cloud Computing (DCC 2013) (UCC '13), IEEE Computer Society, 2013
Conference number
Duration9 December 2013

External IDs

Scopus 84901650410


Research priority areas of TU Dresden

DFG Classification of Subject Areas according to Review Boards


  • Network traffic analysis, Distributed system, MapReduce, Cloud Computing, Deep packet inspection, Real-time systems, Inspection, Programming, Deep packet inspection