1. 1 High Performance Network Monitoring Challenges for Grids Les Cottrell, SLAC Presented at the International Symposium on Grid Computing 2006, Taiwan www.slac.stanford.edu/grp/scs/net/talk05/iscg-06.ppt Partially funded by DOE/MICS for Internet End-to-end Performance Monitoring (IEPM)

2. 2 Why & Outline Data intensive sciences (e.g. HEP) needs to move large volumes of data worldwide –Requires understanding and effective use of fast networks –Requires continuous monitoring and interpretation For HEP LHC-OPN focus on tier 0 and tier 1 sites, i.e. just a few sites Outline of talk: –What does monitoring provide? –Active E2E measurements today and some challenges –Visualization, forecasting, problem ID –Passive monitoring Netflow, Some conclusions

3. 3 Uses of Measurements Automated problem identification & trouble shooting: –Alerts for network administrators, e.g. Bandwidth changes in time-series, iperf, SNMP –Alerts for systems people OS/Host metrics Forecasts for Grid Middleware, e.g. replica manager, data placement Engineering, planning, SLA (set & verify), expectations Also (not addressed here): –Security: spot anomalies, intrusion detection –Accounting

4. 4 Heterogeneous: –Several NRENs, layers 2 (network) & 3 (routing) –Level of access an open issue

5. 5 LHC-OPN: Logical view The diagram to the right is a logical representation of the LHC-OPN showing monitoring hosts The LHC-OPN extends to just inside the T1 “edge” Read/query access should be guaranteed on LHC-OPN “owned” equipment. We also request RO access to devices along the path to enable quick fault isolation Courtesy: Shawn McKee

6. 6 Active E2E Monitoring

7. 7 E.g. Using Active IEPM-BW measurements Focus on high performance for a few hosts needing to send data to a small number of collaborator sites, e.g. HEP tiered model Makes regular measurements with tools –ping (RTT, connectivity), traceroute (routes) –pathchirp, ABwE, pathload (available bandwidth) –iperf (one & multi-stream), thrulay, (achievable throughput) –possibly bbftp, bbcp (file transfer applications, not network) Looking at GridFTP but complex requiring renewing certificates Lots of analysis and visualization Running at major HEP sites: CERN, SLAC, FNAL, BNL, Caltech to about 40 remote sites –http://www.slac.stanford.edu/comp/net/iepm- bw.slac.stanford.edu/slac_wan_bw_tests.htmlhttp://www.slac.stanford.edu/comp/net/iepm- bw.slac.stanford.edu/slac_wan_bw_tests.html

8. 8 IEPM-BW Measurement Topology 40 target hosts in 13 countries Bottlenecks vary from 0.5Mbits/s to 1Gbits/s Traverse ~ 50 AS’, 15 major Internet providers 5 targets at PoPs, rest at end sites

9. 9 Ping/traceroute Ping still useful (plus ca reste …) –Is path connected/node reachable? –RTT, jitter, loss –Great for low performance links (e.g. Digital Divide), e.g. AMP (NLANR)/PingER (SLAC) –Nothing to install, but blocking OWAMP/I2 similar but One Way –But needs server installed at other end and good timers –Being built into IEPM-BW Traceroute –Needs good visualization (traceanal/SLAC) –No use for dedicated λ layer 1 or 2 However still want to know topology of paths

10. 10 Packet Pair Dispersion Used by pathload, pathchirp, ABwE available bw Send packets with known separation See how separation changes due to bottleneck Can be low network intrusive, e.g. ABwE only 20 packets/direction, also fast < 1 sec From PAM paper, pathchirp more accurate than ABwE, but –Ten times as long (10s vs 1s) –More network traffic (~factor of 10) Pathload factor of 10 again more –http://www.pam2005.org/PDF/34310310.pdfhttp://www.pam2005.org/PDF/34310310.pdf IEPM-BW now supports ABwE, Pathchirp, Pathload Bottleneck Min spacing At bottleneck Spacing preserved On higher speed links

11. 11 BUT… Packet pair dispersion relies on accurate timing of inter packet separation –At > 1Gbps this is getting beyond resolution of Unix clocks –AND 10GE NICs are offloading function Coalescing interrupts, Large Send & Receive Offload, TOE Need to work with TOE vendors –Turn off offload (Neterion supports multiple channels, can eliminate offload to get more accurate timing in host) –Do timing in NICs –No standards for interfaces Possibly use packet trains, e.g. pathneck

12. 12 Achievable Throughput Use TCP or UDP to send as much data as can memory to memory from source to destination Tools: iperf (bwctl/I2), netperf, thrulay (from Stas Shalunov/I2), udpmon … Pseudo file copy: Bbcp and GridFTP also have memory to memory mode to avoid disk/file problems

13. 13 BUT… At 10Gbits/s on transatlantic path Slow start takes over 6 seconds –To get 90% of measurement in congestion avoidance need to measure for 1 minute (5.25 GBytes at 7Gbits/s (today’s typical performance) Needs scheduling to scale, even then … It’s not disk-to-disk or application-to application –So use bbcp, bbftp, or GridFTP

14. 14 AND … For testbeds such as UltraLight, UltraScienceNet etc. have to reserve the path –So the measurement infrastructure needs to add capability to reserve the path (so need API to reservation application) –OSCARS from ESnet developing a web services interface (http://www.es.net/oscars/):http://www.es.net/oscars/ For lightweight have a “persistent” capability For more intrusive, must reserve just before make measurement

15. 15 Visualization & Forecasting in Real World

16. 16 Some are seasonal Others are not Events may affect multiple-metrics Misconfigured windows New path Very noisy Examples of real data Seasonal effects –Daily & weekly Caltech : thrulay Nov05 Mar06 0 800 Mbps UToronto: miperf Nov05 Jan06 0 250 Mbps UTDallas Pathchirp thrulay Mar-10-06 Mar-20-06 iperf 0 120 Mbps Events can be caused by host or site congestion Few route changes result in bandwidth changes (~20%) Many significant events are not associated with route changes (~50%)

17. 17 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)

18. 18 However… Elegant graphics are great to understand problems BUT: –Can be thousands of graphs to look at (many site pairs, many devices, many metrics) –Need automated problem recognition AND diagnosis So developing tools to reliably detect significant, persistent changes in performance –Initially using simple plateau algorithm to detect step changes

19. 19 Seasonal Effects on events Change in bandwidth (drops) between 19:00 & 22:00 Pacific Time (7:00-10:00am PK time) Causes more anomalous events around this time

20. 20 Forecasting Over-provisioned paths should have pretty flat time series –Short/local term smoothing –Long term linear trends –Seasonal smoothing But seasonal trends (diurnal, weekly need to be accounted for) on about 10% of our paths Use Holt-Winters triple exponential weighted moving averages

21. 21 Alerting Have false positives down to reasonable level, so sending alerts Experimental Typically few alerts per week. Currently by email to network admins –Adding pointers to extra information to assist admin in further diagnosing the problem, including: Traceroutes, monitoring host parms, time series for RTT, pathchirp, thrulay etc. Plan to add on-demand measurements (excited about perfSONAR) Working on: –Accounting for seasonal effects with Holt-Winters –Using ARMA/ARIMA for forecasting (used by economists) –Automated diagnosing events

22. 22 Passive Active monitoring –Pro: regularly spaced data on known paths, can make on-demand –Con: adds data to network, can interfere with real data and measurements What about Passive?

23. 23 Netflow et. al. 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 intrusive traffic, real: traffic, collaborators, applications No accounts/pwds/certs/keys No reservations etc Characterize traffic: top talkers, applications, flow lengths etc. LHC-OPN requires edge routers to provide Netflow data Internet 2 backbone –http://netflow.internet2.edu/weekly/http://netflow.internet2.edu/weekly/ SLAC: –www.slac.stanford.edu/comp/net/slac-netflow/html/SLAC-netflow.htmlwww.slac.stanford.edu/comp/net/slac-netflow/html/SLAC-netflow.html

24. 24 Typical day’s flows Very much work in progress Look at SLAC border Typical day: –~ 28K flows/day –~ 75 sites with > 100KB bulk-data flows –Few hundred flows > GByte 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 Look at throughput distribution

25. 25 Netflow et. al. Peaks at known capacities and RTTs –RTTs might suggest windows not optimized, peaks at default OS window size(BW=Window/RTT)

26. 26 How many sites have enough flows? In May ’05 found 15 sites at SLAC border with > 1440 (1/30 mins) flows –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

27. 27 Mining data for sites Real application use (bbftp) for 4 months Gives rough idea of throughput (and confidence) for 14 sites seen from SLAC

28. 28 Multi months Bbcp SLAC to Padova Bbcp throughput from SLAC to Padova Fairly stable with time, large variance Many non network related factors

29. 29 Netflow limitations Use of dynamic ports makes harder to detect app. –GridFTP, bbcp, bbftp can use fixed ports (but may not) –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.pdfhttp://www.pam2004.org/papers/166.pdf Aggregation of parallel flows (needs care, but not difficult) Can use for giving performance forecast –Unclear if can use for detecting steps in performance

30. 30 Conclusions Some tools fail at higher speeds Throughputs often depend on non-network factors: –Host: interface speeds (DSL, 10Mbps Enet, wireless), loads, resource congestion –Configurations (window sizes, hosts, number of parallel streams) –Applications (disk/file vs mem-to-mem) Looking at distributions by site, often multi- modal Predictions may have large standard deviations Need automated assist to diagnose events

31. 31 Questions, More information Comparisons of Active Infrastructures: –www.slac.stanford.edu/grp/scs/net/proposals/infra-mon.htmlwww.slac.stanford.edu/grp/scs/net/proposals/infra-mon.html Some active public measurement infrastructures: –www-iepm.slac.stanford.edu/www-iepm.slac.stanford.edu/ –www-iepm.slac.stanford.edu/pinger/www-iepm.slac.stanford.edu/pinger/ –e2epi.internet2.edu/owamp/e2epi.internet2.edu/owamp/ –amp.nlanr.net/amp.nlanr.net/ Monitoring tools –www.slac.stanford.edu/xorg/nmtf/nmtf-tools.htmlwww.slac.stanford.edu/xorg/nmtf/nmtf-tools.html –www.caida.org/tools/www.caida.org/tools/ –Google for iperf, thrulay, bwctl, pathload, pathchirp Event detection –www.slac.stanford.edu/grp/scs/net/papers/noms/noms14224-122705- d.docwww.slac.stanford.edu/grp/scs/net/papers/noms/noms14224-122705- d.doc