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Enhancing Network Management with IPFIX Traces: Trace2Flow's Algorithm Exploration

The 3rd NMRG Workshop focused on innovative uses of NetFlow/IPFIX in network management. Our team aims to enhance analysis tools through the development of Trace2Flow, which generates IPFIX traces from existing packet traces. This flexible and easy-to-use tool works across various operating systems and supports numerous protocols and IPFIX fields. Trace2Flow enables users to compare packet and IPFIX algorithms seamlessly and includes features for custom statistics generation. Explore new methodologies for traffic capture, sampling, and throughput estimation.

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Enhancing Network Management with IPFIX Traces: Trace2Flow's Algorithm Exploration

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  1. Trace2Flow Karol Adamski, Maciej Korczyński, Lucjan Janowski, Krzysztof Rusek 3rd NMRG Workshop on Netflow/IPFIX Usage in Network Management July 30, 2010, Maastricht

  2. Motivation I am a member of the COST TMA Action IC0703 (see www.tma-portal.eu) Look, I have a nice algorithm I do not know … Doesitwork for IPFIX?

  3. Solution • We should have a tool to generate a IPFIX trace from packet trace and use both of them … • … so that anyone can compare both packet and IPFIX algorithms • The conversion should be easy and flexible • It should work for many operating systems • We have TracesPlay (http://tracesplay.sourceforge.net/ ) • a tool reading packet traces which work for different operating systems

  4. Nice To Have • Direct MATLAB connection • Support for lots of different protocols • Support for numerous IPFIX fields • It should be easy to add a custom statistics not necessarily IPFIX one • On-line generating – artificial IPFIX node

  5. Whole system IPFIX , Different samplig methods ETH, 802.1q, 802.11, ICMPv4, IPv4, TCP, UDP, RTP, H264, NetFlow v1,5,7,8,9 ......................... Formats: PCap, ERF, TSH, FR, FR+, SNOOPv2 On-line with lib PCAP From a buffer Binary Data Reading and Analyzis Statistics Generator Data Providing CSV Extend Software MATLAB

  6. Trace2flow Options • -IPFIX <value> the flow definition • –c the first column of CVS file contains the column name • -e show error • –r <file name> list of the traces to analyze • –o <fields name> list of the IPFIX fields • –s <sampling methods> OneToOne, OneToN <value>, RandomFromN <value>, FirstFromTimeIntercal <value> • –w <file name> output file, out.txt by default

  7. Supported fields

  8. Examples trace2flow.exe -e -s OneToOne –IPFIX 1 -r test.pcap -o IPFIX.inPkts IPFIX.inBytes IPFIX.MinTTL -w out_OneToOne.txt trace2flow.exe -s OneToN 25 –IPFIX 3 -r test.pcap -o IPFIX.IP.src IPFIX.inBytes IPFIX.MaxTTL –c -w out_OneToN_3.txt MATLAB: Data = trace2flow(-s OneToN 100 –IPFIX 1 -r test.pcap -o IPFIX.inPkts);

  9. A Simple Example • Packet trace captured within one of subnetworks in campus network in AGH University of Technology in Krakow: • amount of packets: 1273722 • packet duration: 180.8632 s • trace size: 100MB • average throughput at the time of traffic capturing: 3.45 Mb/s

  10. Throughput Estimation • Two representative count-based methods were examined (as they are used in Cisco routers): • Deteministic Sampling (every n-th packet) • Random Sampling (1-out-of-N packet)

  11. Throughput Estimation - Results • Underestimation of the throughput for sampling rates lower than 10%

  12. Throughput Estimation - Results • For sampling rates lower than 10% the time required for processing all packets is lower than 2s in the particular case

  13. Where You Can Find Us The project web page http://tracesplay.sourceforge.net/traces2flow/index.html The Mather project http://tracesplay.sourceforge.net/

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