1. 1 End-to-end Monitoring of High Performance Network Paths Les Cottrell, Connie Logg, Jerrod Williams SLAC, for the ESCC meeting, Columbus Ohio, July 2004 www.slac.stanford.edu/grp/scs/net/talk03/escc-jul04.ppt Partially funded by DOE/MICS Field Work Proposal on Internet End-to-end Performance Monitoring (IEPM), also supported by IUPAP

2. 2 Need Data intensive science (e.g. HENP) needs to share data at high speeds Needs high-performance, reliable e2e paths and the ability to use them End users need long and short term estimates of network and application performance for: Planning, setting expectations & trouble shooting You can’t manage what you can’t measure

3. 3 IEPM-BW Toolkit: –Enables regular, E2E measurements with user selectable: Tools: iperf (single & multi-stream), bbftp, bbcp, GridFTP, ping (RTT), traceroute Periods (with randomization) Remote hosts to monitor –Hierarchical to match the tiered approach of BaBar & LHC computation / collaboration infrastructures –Includes: Auto-clean up of hung processes at both ends Management tools to look for failures (unreachable hosts, failing tools etc.) Web navigation of results Visualization of data as time-series, histograms, scatter plots, tables Access to data in machine readable form Documentation on host etc. requirements, program logic manuals, methods

4. 4 Requirements –Requires: Monitoring toolkit installed on Linux monitoring host –Host provided & administered by monitoring site personnel –No need for root privileges –Appropriate iperf, bbftp etc. ports to be opened –SLAC can do initial install & configuration for monitoring host »50 line configuration file for each remote host, tells where directories, applications are located, options for various tools etc (mainly defaults) Small toolkit installed at remote (monitored hosts) Ssh access to an account at remote hosts –This is the biggest problem with deployment

5. 5 Achievable throughput & file transfer IEPM-BW –High impact (iperf, bbftp, GridFTP …) measurements 90+-15 min intervals Select focal area Fwd route change Rev route change Min RTT Iperf bbftp iperf1 abing Avg RTT

6. 6 Visualization: traceroutes Compact table to see correlations between many routes Identify significant changes in routes –Differences in > 1 hop, NOT same first 3 octets, NOT same AS Report all traceroute pathologies: –! Annotations, ICMP checksum errs, non-responding interfaces, unreachable end host, stutters, multi-homed end host Note, we observe: – most route changes (>98%) do not result in significant performance changes –Many performance changes (~50+-20%) are NOT due to route changes Applications, host congestion, level 2 changes etc.

7. 7 Route table Example Compact so can see many routes at once History navigation Multiple route changes (due to GEANT), later restored to original route Available bandwidth Raw traceroute logs for debugging Textual summary of traceroutes for email to ISP Description of route numbers with date last seen User readable (web table) routes for this host for this day Route # at start of day, gives idea of root stability Mouseover for hops & RTT

8. 8 Another example TCP probe type Host not pingable Intermediate router does not respond ICMP checksum error Level change Get AS information for routes

9. 9 Topology Choose times and hosts and submit request DL CLRCCLRC CLRC IN2P3 CESnet ESnet JAnet GEANT Nodes colored by ISP Mouseover shows node names Click on node to see subroutes Click on end node to see its path back Also can get raw traceroutes with AS’ Alternate rt SLAC Alternate route Hour of day

10. 10 IEPM-BW HENP Deployment June 2004 Measurements from SLAC & FNAL –BaBar, CMS, D0, CDF + 60-70 remote hosts in 12 countries Toolkits needed in monitor & remote hosts Range of bandwidths:500Kbps to 1 Gbps

11. 11 Working on: Provide more options for security for remote hosts Web services API access to data Provide & integrate low network utilization tool: –~ 25% of Abilene traffic is net measurement Automate detection of anomalous step changes in performance Evaluate using QOS or HSTCP-LP to reduce impact of iperf traffic –Evidence that causes packet loss (ESnet/FNAL/SLAC)

12. 12 Simplify remote security Currently use ssh to start, kill servers, check things etc. Instead run servers all time at remote host –Check & restart with cron job –Also kill hung processes with cron jobs –More work for remote admin –More difficult to check why things not working NASA very hard to get account (requires training etc.), so this will be a work-around

13. 13 Data Access Interactive web accessible –Most data can be downloaded in space or comma separated etc. (accessible via link or to program (e.g. using lynx to access URL)) –However non standard Web services (GGF NMWG definitions) Working (with Warren Matthews/GATech/I2) on defining / providing access to traceroutes for AMP & IEPM-LITE MonALISA is accessing data via Web services CharacteristicToolname path.bandwidth.achievable.TCP iperf path.bandwidth.achievable.TCP.multiStreamIperf,bbftp, bbcp, GridFTP CharacteristicToolname path.bandwidth.capacityABwE path.bandwidth.utilizationABwE

14. 14 Low impact bandwidth measurement Goals: –Make a measurement in < second rather than tens of seconds –Injects little network traffic –Provide reasonable agreement with more intense methods (e.g. iperf) Enables: –Measurements of low performance links (e.g. to developing countries) –Helps avoid need for scheduling –More frequent measurements (minutes vs. hours) –Lower impact more friendly

15. 15 Low impact Bandwidth Use 20 packet pairs to roughly estimate dynamic bw Capacity & Xtraffic, then Available = Capacity – Xtraffic –Capacity  min pair separation; Xtraffic  packet pair dispersion Dynamic bandwidth capacity (DBC) Available bandwidth = DBC – X-traffic Cross-traffic Iperf ABwE SLAC to Caltech Mar 19, 2004

16. 16 Anomalous Event Detection Too many graphs to scan by hand, need to automate –SLAC Caltech link performance dropped by factor 5 for ~ month before noticed, fixed within 4 hours of reporting Looking for long-term step down changes in bandwidth Use modified “plateau” algorithm from NLANR –Divide data into history & trigger buffer –If y <  h –  *  h then trigger, else history (  When trigger buffer fills: if  t <  *  h, then have an event

17. 17 Anomalous Event Detection Length of trigger buffer (  ) determines how long a step down must last before being interesting, we use 1 to 3 hours –E.g. 20 mins saw 9 events, 40mins saw 3, 60mins none Works well unless strong (>40%) diurnal changes –Next step incorporate diurnal checks Events caused by application on Caltech host (not network related)

18. 18 Putting it together     ESnet CENIC Abilene SLAC Supernet SOX

19. 19 Future plans Looking for funding… Integrate it all Improve distribution and management tools Add monitoring sites e.g. HENP tier 0 & 1 sites such as CERN, BNL, IN2P3, DESY …; ESnet, StarLight, Caltech … Add extra functionality: –Improved event detection include diurnals, multivariate –Filter alerts –Upon detecting anomaly gather relevant information (network, host etc.) including on-demand measurements (e.g. NDT) and prepare web page & email –Improved web services access

20. 20 Thanks: Development Jiri Navratil (Prague) – bandwidth estimation (ABwE) Paola Grosso (SLAC) & Warren Matthews (GATech) - web services Maxim Grigoriev (FNAL) – event detection, IEPM visualization, major monitoring site Ruchi Gupta (Stanford) – event visualization Prof Arshad Ali & Fahad Khalid (NIIT, Pakistan) – data collection after event Rich Carlson (I2), NDT

21. 21 Thanks: on-going Foreign: –Andrew Daviel (TRIUMF), Simon Leinen (SWITCH), Olivier Martin (CERN), Sven Ubik (CESnet), Kars Ohrenberg (DESY), Bruno Hoeft (FZK), Dominique (IN2P3), Fabrizio Coccetti (INFN), Cristina Bulfon (INFN), Yukio Karita (KEK), Takashi Ichihara (RIKEN), Yoshinori Kitasuji (APAN), Antony Antony (NIKHEF), Arshad Ali (NIIT), Serge Belov (BINP), Robin Tasker (DL & RAL), Yee Ting Lee (UCL), Richard Hughes-Jones (Manchester) US –Shawn McKee (Michigan), Tom Hacker (Michigan), Eric Boyd (I2), Stanislav Shalunov (SOX), George Uhl (GSFC), Brian Tierney (LBNL), John Hicks (Indiana), John Estabrook (UIUC), Maxim Grigoriev (FNAL), Joe Izen (UT Dallas), Chris Griffin (U Florida), Tom Dunigan (ORNL), Dantong Yu (BNL), Suresh Singh (Caltech), Chip Watsom (JLab), Robert Lukens (JLab), Shane Canon (NERSC), Kevin Walsh (SDSC), David Lapsley (MIT/Haystack/ISI-E)

22. 22 More information IEPM-BW home page –http://www-iepm.slac.stanford.edu/bw/http://www-iepm.slac.stanford.edu/bw/ Comparison of Internet E2E Measurement infrastructures; –http://www- iepm.slac.stanford.edu/grp/scs/net/proposals/infra-mon.htmlhttp://www- iepm.slac.stanford.edu/grp/scs/net/proposals/infra-mon.html ABwE lightweight bandwidth estimation –http://www-iepm.slac.stanford.edu/abing/http://www-iepm.slac.stanford.edu/abing/ Anomalous Event Detection –www.slac.stanford.edu/grp/scs/net/papers/sigcomm2004/nts26-logg.pdfwww.slac.stanford.edu/grp/scs/net/papers/sigcomm2004/nts26-logg.pdf IEPM Web Services –http://www-iepm.slac.stanford.edu/tools/web_services /http://www-iepm.slac.stanford.edu/tools/web_services /

23. 23 Extra Slides

24. 24 Web Services See http://www-iepm.slac.stanford.edu/tools/web_services/http://www-iepm.slac.stanford.edu/tools/web_services/ Working for: RTT, loss, capacity, available bandwidth, achievable throughput No schema defined for traceroute (hop-list) PingER –Definition WSDL –http://www-iepm.slac.stanford.edu/tools/soap/wsdl/PINGER_profile.wsdlhttp://www-iepm.slac.stanford.edu/tools/soap/wsdl/PINGER_profile.wsdl path.delay.roundTrip ms (min/avg/max + RTTs), path.loss.roundTrip IPDV(ms), Also dups, out of order, IPDV, TCP thru estimate Require to provide packet size, units, timestamp, sce, dst –path.bandwidth.available, path.bandwidth.utilized, path.bandwidth.capacity Mainly for recent data, need to make real time data accessible Used by MonALISA so need coordination to change definitions

25. 25 Perl access to PingER

26. 26 PingER WSDL

27. 27 Output from script

28. 28 Perl AMP traceroute

29. 29 AMP traceroute output

30. 30 Intermediate term access Provide access to analyzed data in tables via.tsv format download from web pages.

31. 31 Bulk Data For long term detailed data, we tar and zip the data on demand. Mainly for PingER data.

32. 32 AbWE Iperf 28 days bandwidth history. During this time we can see several different situations caused by different routing from SLAC to CALTECH Drop to 100 Mbits/s by Routing (BGP) errors Drop to 622 Mbits/s path back to new CENIC path New CENIC path 1000 Mbits/s Reverse Routing changes Forward Routing changes Scatter plot graphs of Iperf versus ABw on different paths (range 20– 800 Mbits/s) showing agreement of two methods (28 days history) RTT Bbftp Iperf 1 stream

33. 33 Changes in network topology (BGP) can result in dramatic changes in performance Snapshot of traceroute summary table Samples of traceroute trees generated from the table ABwE measurement one/minute for 24 hours Thurs Oct 9 9:00am to Fri Oct 10 9:01am Drop in performance (From original path: SLAC-CENIC-Caltech to SLAC-Esnet-LosNettos (100Mbps) -Caltech ) Back to original path Changes detected by IEPM-Iperf and AbWE Esnet-LosNettos segment in the path (100 Mbits/s) Hour Remote host Dynamic BW capacity (DBC) Cross-traffic (XT) Available BW = (DBC-XT) Mbits/s Notes: 1. Caltech misrouted via Los-Nettos 100Mbps commercial net 14:00-17:00 2. ESnet/GEANT working on routes from 2:00 to 14:00 3. A previous occurrence went un-noticed for 2 months 4. Next step is to auto detect and notify Los-Nettos (100Mbps)