1 Achieving high performance throughput in production networks Les Cottrell – SLAC Presented at the Internet 2 HENP Networking Working Group kickoff meeting at Internet 2 Ann Arbor, Michigan, Oct 26 ‘01 Partially funded by DOE/MICS Field Work Proposal on Internet End-to-end Performance Monitoring (IEPM), also supported by IUPAP

2 High Speed Bulk Throughput Driven by: –Data intensive science, e.g. data grids –HENP data rates, e.g. BaBar 300TB/year, collection doubling yearly, i.e. PBytes in couple of years –Data rate from experiment today ~ 20MBytes/s ~ 200GBytes/d –Multiple regional computer centers (e.g. Lyon-FR, RAL-UK, INFN-IT, LBNL-CA, LLNL-CA, Caltech-CA) need copies of data Tier A gets 1/3 data in 1/3 year (full rate), SLAC does not keep copy –Boeing 747 high throughput, BUT poor latency (~ 2 weeks) & very people intensive So need high-speed networks and ability to utilize –High speed today = few hundred GBytes/day (100GB/d ~ 10Mbits/s) Data vol Moore’s law

3 How to measure network throughput Selected about 2 dozen major collaborator sites in US, CA, JP, FR, CH, IT, UK over last year –Of interest to SLAC –Can get logon accounts Use iperf –Choose window size and # parallel streams –Run for 10 seconds together with ping (loaded) –Stop iperf, run ping (unloaded) for 10 seconds –Change window or number of streams & repeat Record # streams, window, throughput (Mbits/s), loaded & unloaded ping responses, cpu utilization, real time Verify window sizes are set properly by using tcpdump can’t believe what application tells you   Note cpu speeds, interface speeds, operating system, path characteristics

4 Solaris Default window size Typical results Today Hi-thru usually = big windows & multiple streams Improves ~ linearly with streams for small windows 8kB 16kB 32kB 100kB 64kB Broke 100Mbps Trans Atlantic Barrier

5 Windows vs Streams Often for fixed streams*window product, streams are more effective than window size, e.g. SLAC>CERN, Jul ‘01: There is an optimum number of streams above which performance flattens out Common for throughputs to be asymmetric –more congestion one way, different routes, host dependencies WindowStreamsMbpsW*S 64KB KB 128KB KB 256KB KB 512KB KB 1024KB KB KB 4096KB KB

6 Windows vs Streams Multi-streams often more effective than windows –more agile in face of congestion Often easier to set up –Need root to configure kernel to set max window –Network components may not support big windows –Some OS’ treat max windows strangely  May be able to take advantage of multiple paths But: – may be considered over-aggressive (RFC 2914)  –can take more cpu cycles –how to know how many streams?

7 Iperf client CPU utilization As expected increases with throughput (mainly kernel)  0.7*MHz/Mbits/s For fixed throughput –Fewer streams take less cpu  –E.g. 1-4 streams take 20% less cpu than 8-16 streams for same throughput (if can get it)

8 Throughput quality improvements TCP BW < MSS/(RTT*sqrt(loss)) Macroscopic Behavior of the TCP Congestion Avoidance Algorithm, Matthis, Semke, Mahdavi, Ott, Computer Communication Review 27(3), July 1997 Note E. Europe keeping up 80% annual improvement ~ factor 10/4yr China

9 Bandwidth changes with time 1/2 Short term competing cross-traffic, other users, factors of 3-5 observed in 1 minute Long term: link, route upgrades, factors 3-16 in 12 months All hosts had 100Mbps NICs. Recently have measured 105Mbps SLAC > IN2P3 and 340Mbps Caltech > SLAC with GE

10 Network Simulator (ns-2) From UCB, simulates network –Choice of stack (Reno, Tahoe, Vegas, SACK…) –RTT, bandwidth, flows, windows, queue lengths … Compare with measured results –Agrees well –Confirms observations (e.g. linear growth in throughput for small window sizes as increase number of flows)

11 Agreement of ns2 with observed

12 Ns-2 thruput & loss predict Indicates on unloaded link can get 70% of available bandwidth without causing noticeable packet loss Can get over 80-90% of available bandwidth Can overdrive: no extra throughput BUT extra loss 90%

13 Simulator benefits No traffic on network (nb throughput can use 90%) Can do what if experiments No need to install iperf servers or have accounts No need to configure host to allow large windows BUT –Need to estimate simulator parameters, e.g. RTT use ping or synack Bandwidth, use pchar, pipechar etc., moderately accurate AND its not the real thing –Need to validate vs. observed data –Need to simulate cross-traffic etc

14 Impact on Others Make ping measurements with & without iperf loading –Loss loaded(unloaded) –RTT Looking at how to avoid impact: e.g. QBSS/LBE, application pacing, control loop on stdev(RTT) reducing streams, want to avoid scheduling

15 File Transfer Used bbcp (written by Andy Hanushevsky) –similar methodology to iperf, except ran for file length rather than time, provides incremental throughput reports, supports /dev/zero, adding duration –looked at /afs/, /tmp/, /dev/null –checked different file sizes Behavior with windows & streams similar to iperf Thru bbcp ~0.8*Thru iperf For modest throughputs (< 50Mbits/s) rates are independent of whether destination is /afs/, /tmp/ or /dev/null. Cpu utilization ~ 1MHz/Mbit/s is ~ 20% > than for iperf

16 Application rate-limiting Bbcp has transfer rate limiting –Could use network information (e.g. from Web100 or independent pinging) to bbcp to reduce/increase its transfer rate, or change number of parallel streams No rate limiting, 64KB window, 32 streams 15MB/s rate limiting, 64KB window, 32 streams

17 Using bbcp to make QBSS measurements Run bbcp src data /dev/zero, dst=/dev/null, report throughput at 1 second intervals –with TOS=32 (QBSS) –After 20 s. run bbcp with no TOS bits specified (BE) –After 20 s. run bbcp with TOS=40 (priority) –After 20 more secs turn off Priority –After 20 more secs turn off BE

18 QBSS test bed with Cisco 7200s Set up QBSS testbed Configure router interfaces –3 traffic types: QBSS, BE, Priority –Define policy, e.g. QBSS > 1%, priority < 30% –Apply policy to router interface queues 10Mbps 100Mbps 1Gbps Cisco 7200s

19 Example of effects Also tried: 1 stream for all, and priority at 30%

20 QBSS with Cisco s + Policy Feature Card (PFC) –Routing by PFC2, policing on switch interfaces –2 queues, 2 thresholds each –QBSS assigned to own queue with 5% bandwidth – guarantees QBSS gets something –BE & Priority traffic in 2 nd queue with 95% bandwidth –Apply ACL to switch port to police Priority traffic to < 30% 100Mbps 1Gbps Cisco 6500s + MSFC/Sup2 Time 100% BE Priority (30%) QBSS (~5%)

21 Impact on response time (RTT) Run ping with Iperf loading with various QoS settings, iperf ~ 93Mbps –No iperf ping avg RTT ~ 300usec (regardless of QoS) –Iperf = QBSS, ping=BE or Priority: RTT~550usec 70% greater than unloaded –Iperf=Ping QoS (exc. Priority) then RTT~5msec > factor of 10 larger RTT than unloaded –If both ping & iperf have QoS=Priority then ping RTT very variable since iperf limited to 30% RTT quick when iperf limited, long when iperf transmits

22 Possible HEP usage Apply priority to lower volume interactive voice/video-conferencing and real time control Apply QBSS to high volume data replication Leave the rest as Best Effort Since 40-65% of bytes to/from SLAC come from a single application, we have modified to enable setting of TOS bits Need to identify bottlenecks and implement QBSS there Bottlenecks tend to be at edges so hope to try with a few HEP sites

23 Acknowledgements for SC2001 Many people assisted in getting accounts, setting up servers, providing advice, software etc. –Suresh Man Singh, Harvey Newman, Julian Bunn (Caltech), Andy Hanushevsky, Paola Grosso, Gary Buhrmaster, Connie Logg (SLAC), Olivier Martin (CERN), Loric Totay, Jerome Bernier (IN2P3), Dantong Yu (BNL), Robin Tasker, Paul Kummer (DL), John Gordon (RL), Brian Tierney, Bob Jacobsen, (LBL), Stanislav Shalunov (Internet 2), Joe Izen (UT Dallas), Linda Winkler, Bill Allcock (ANL), Ruth Pordes, Frank Nagy (FNAL), Emanuele Leonardi (INFN), Chip Watson (JLab), Yukio Karita (KEK), Tom Dunigan (ORNL), John Gordon (RL), Andrew Daviel (TRIUMF), Paul Avery, Greg Goddard (UFL), Paul Barford, Miron Livny (UWisc), Shane Canon (NERSC), Andy Germain (NASA), Andrew Daviel (TRIUMF), Richard baraniuk, Rold Reidi (Rice).

24 SC2001 demo Send data from SLAC/FNAL booth computers (emulate a tier 0 or 1 HENP site) to over 20 other sites with good connections in about 6 countries –Throughputs from SLAC range from 3Mbps to > 300Mbps Part of bandwidth challenge proposal Saturate 2Gbps connection to floor network Apply QBSS to some sites, priority to a few and rest Best Effort –See how QBSS works at high speeds Competing bulk throughput streams Interactive low throughput streams, look at RTT with ping

25 WAN thruput conclusions High FTP performance across WAN links is possible –Even with 20-30Mbps bottleneck can do > 100Gbytes/day –Can easily saturate a fast Ethernet interface over WAN –Need GE NICs, > OC3 WANs & to improve performance Performance is improving OS must support big windows selectable by application Need multiple parallel streams in some cases Loss is important in particular interval between losses Can get close to max thruput with small (<=32Mbyte) with sufficient (5-10) streams Improvements of 5 to 60 in thruput by using multiple streams & larger windows Impacts others users, QBSS looks hopeful

26 More Information IEPM/PingER home site: –www-iepm.slac.stanford.edu/www-iepm.slac.stanford.edu/ Bulk throughput site: –www-iepm.slac.stanford.edu/monitoring/bulk/www-iepm.slac.stanford.edu/monitoring/bulk/ Transfer tools: – – – – TCP Tuning: – –www-didc.lbl.gov/tcp-wan.htmlwww-didc.lbl.gov/tcp-wan.html QBSS measurements –www-iepm.slac.stanford.edu/monitoring/qbss/measure.htmlwww-iepm.slac.stanford.edu/monitoring/qbss/measure.html