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Using Netflow data for forecasting

This presentation discusses the use of Netflow data for traffic forecasting in network monitoring systems. It covers the benefits of Netflow, data collection methods, traffic analysis, and challenges in predicting traffic patterns. The presentation also explores the comparison of active and passive measurements for traffic monitoring and includes insights on optimizing network performance based on flow analysis.

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Using Netflow data for forecasting

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  1. Using Netflow data for forecasting Les CottrellSLAC and Fawad NazirNIIT, Presented at the CHEP06 Meeting, Mumbai India, February 2006 www.slac.stanford.edu/grp/scs/net/talk06/icfa-chep06.ppt Partially funded by DOE/MICS for Internet End-to-end Performance Monitoring (IEPM)

  2. Why Netflow • Traceroute dead for dedicated paths • Some things continue to work • Ping, owamp • Iperf, thrulay, bbftp … but • Packet pair dispersion needs work, its time may be over • Passive looks promising with Netflow • SNMP needs AS to make accessible - perfSONAR • Capture expensive • ~$100K (Joerg Micheel) for OC192Mon

  3. Netflow • Router/Switch identifies flow by sce/dst ports, protocol • Cuts record for each flow: • src, dst, ports, protocol, TOS, start, end time • Collect records and analyze • Can be a lot of data to collect each day, needs lot cpu • Hundreds of MBytes to GBytes • No extra traffic injected, & real: traffic, collaborators, applications • No accounts/pwds/certs/keys • No reservations etc • Characterize traffic: top talkers, applications, flow lengths etc. • Internet 2 backbone • http://netflow.internet2.edu/weekly/ • SLAC: • www.slac.stanford.edu/comp/net/slac-netflow/html/SLAC-netflow.html

  4. Typical day’s flows • Very much a work in progress … • Look at SLAC border • Typical day: • For >100KB flows • ~ 28K flows/day • ~ 75 sites with > 100KByte bulk-data flows • Few hundred flows > GByte

  5. Forecasting? • Collect records for several weeks • Filter 40 major collaborator sites, big (> 100KBytes) flows, bulk transport apps/ports (bbcp, bbftp, iperf, thrulay, scp, ftp • Divide by remote site, aggregate parallel streams • Fold data onto one week, see bands at known capacities and RTTs ~ 500K flows/mo

  6. Netflow et. al. Peaks at known capacities and RTTs RTTs might suggest windows not optimized

  7. How many sites have enough flows? • In May ’05 found 15 sites at SLAC border with > 1440 (1/30 mins) flows • Maybe enough for time series forecasting for seasonal effects • Three sites (Caltech, BNL, CERN) were actively monitored • Rest were “free” • Only 10% sites have big seasonal effects in active measurement • Remainder need fewer flows • So promising

  8. Compare active with Passive • Poor correlation usually caused by long flows • i.e. one stream of parallel flows lingers well after others • See • www.slac.stanford.edu/comp/net/bandwidth-tests/web100/ Scatter plot: thru_active vs. thru_passive has strong correlation

  9. Compare active with passive • Predict flow throughputs from Netflow data for SLAC to Padova for May ’05 • Compare with E2E active ABwE measurements

  10. Mining data for sites

  11. One month for one site • Bbcp SLAC to Padova

  12. Multi months • Bbcp SLAC to Padova Bbcp throughput from SLAC to Padova

  13. Diurnal behavior • Some evidence of diurnal behavior

  14. Effect of multiple streams • Dilemma what do you recommend: • Maximize throughput but unfair, pushes other flows aside • Use another TCP stack, e.g. BIC-TCP, H-TCP etc.

  15. Netflow limitations • Use of dynamic ports. • GridFTP, bbcp, bbftp can use fixed ports • P2P often uses dynamic ports • Discriminate type of flow based on headers (not relying on ports) • Types: bulk data, interactive … • Discriminators: inter-arrival time, length of flow, packet length, volume of flow • Use machine learning/neural nets to cluster flows • E.g. http://www.pam2004.org/papers/166.pdf • Aggregation of parallel flows (needs care, but not difficult) • SCAMPI/FFPF/MAPI allows more flexible flow definition • See www.ist-scampi.org/ • Use application logs (OK if small number)

  16. More challenges • Throughputs often depend on non-network factors: • Host interface speeds (DSL, 10Mbps Enet, wireless) • Configurations (window sizes, hosts) • Applications (disk/file vs mem-to-mem) • Looking at distributions by site, often multi-modal • Predictions may have large standard deviations • How much to report to application

  17. Any questions? • Comparisons of Active, web100 & Netflow measurements • http://www.slac.stanford.edu/comp/net/bandwidth-tests/web100/

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