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Network performance measurements

Les Cottrell – SLACPrepared for the ICFA-SCIC, CERN December 8, 2001

Partially funded by DOE/MICS Field Work Proposal on Internet End-to-end Performance Monitoring (IEPM), also supported by IUPAP

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PingER deployment• Measurements from

– 34 monitors in 14 countries• 6 DoE Labs, 4 DoE Uni

– Over 600 remote hosts• 65 DoE funded universities

– Over 72 countries – Over 3300 monitor-remote site pairs– Measurements go back to Jan-95– Reports on RTT, loss, reachability, jitter, reorders, duplicates …

• Countries monitored– Contain 78% of world population– 99% of online users of Internet

• Lightweight (100bps/host pair)– Very useful for inter-regional and poor links, need more intensive for high

performance & Grid sites

• Plan to continue project

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New stuff• 8 metrics, added: duplicates, out of order, jitter, min

RTT, conditional loss probability

• Now have defined ~ 45 affinity groups + 72 countries & 32 monitoring sites

• FNAL re-involved: cleaning up , the database and graphing engine, looking to migrate database, already much more reliable, creating web site

• UCL, Daresbury extending to IperfER

• Besides ESnet, there are data sets for IPv6 6bone, XIWT, NTON when it existed.

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Throughput quality improvementsTCPBW < 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

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Losses: Rest of world by region• <1% = good, 2.5%=acceptable, < 5%=poor, <12%=v. poor,

>12%=bad

• E. Europe still problems

• Middle East, S. & Central America also problem regions

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Rest of world by TLD• Russia poor to bad China poor

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We need to better understand• Closer to applications, e.g. FTP• Understand how to make throughput measurements:

– Duration & frequency (balance impact against granularity needed), – Windows and or vs parallel streams, – OS dependencies, cpu utilization, interface speeds, security (e.g.

ssh)– Impact on others, variability on different time-scales– Can we use QBSS, can/should application self limit?– How well does simulation work, how to improve?– How to relate to simpler measurements– How does file transfer work compared to iperf?– Is compression useful and when?– How useful is it for the application to get feedback from the

network?

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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 (HENP, PPDG, Internet measurement centers)– Can get logon accounts

• Use iperf, bbcp (soon bbftp, gridftp) + traceroute etc.• 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 responsesVerify window sizes are set properly by using tcpdump can’t believe what application tells you

• Compare bandwidth measurement tools plus with iperf, bbcp, bbftp, gridFTP – choose minimum set, automate

9Solaris Default window size

Typical resultsTodayHi-thru usually= big windows & multiple streams

Improves ~ linearlywith streams forsmall windows

8kB

16kB32kB

100kB

64kB

Broke 100MbpsTrans AtlanticBarrier

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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?

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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)

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CPU vs window vs streams vs throughput

•MHz ~ 0.97 * Mbps•Bigger windows = less cpu for fixed throughput

Iperf % cpu vs throughput vs window SLAC to RIKEN Nov 29 '01

0

5

10

15

20

0 5 10 15 20Throughput Mbits/s

CP

U M

Hz

8K 16KB32KB 64KB128KB 256KB512KB 1024KB

Increasing window

Increasing streams

Hooks at end = saturation

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PathologiesDiurnal variation often indicates saturationRouting change

Flat time series due to host NIC limit (100Mbps)

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BBCP vs Iperf

SlopeAvg=0.6Sd = 0.2

bbcp

iperf

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Bbcp memory vs disk (=>/tmp)Bbcp disk vs bbcp mem throughput

y = 0.8725x

R2 = 0.7934

0

20

40

60

80

100

120

140

0 20 40 60 80 100 120 140

BBCP mem to mem throughput Mbits/s

Bb

cp d

isk

to d

isk

thro

ug

hp

ut

Mb

its/

s

Caltech

ORNL

CERN

RAL

Wisconsin

Michigan

Daresbury

Rice

FNAL

JLab

KEK

LANL

LBL

All

Linear (All)

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But …Bbcp disk vs bbcp mem throughput

y = 0.8725x

R2 = 0.7934

0

50

100

150

200

250

300

0 50 100 150 200 250 300 350 400

BBCP mem to mem throughput Mbits/s

Bb

cp d

isk

to d

isk

thro

ug

hp

ut

Mb

its/

sCaltech

ORNL

CERN

RAL

Wisconsin

Michigan

Daresbury

Rice

FNAL

JLab

KEK

LANL

LBL

All

Linear (All)

LANL has Maxtor IDE disks

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CompressionBbcp file throughput from SLAC to CERN

by compression factor, Dec 5 '01

0

10

20

30

40

50

0 2 4 6 8 10Compression factor

Bb

cp

thro

ug

hp

ut

Mb

its/

s

0

2

4

6

8

10

Co

mp

ress

ion

rat

io

•60Mbyte Objectivity file, using zlib, 8 streams, 64KB window•Can improve throughput on this link with these hosts (Sun Ultra Sparcs with 360MHz cpus) by more than a factor of 2.•Want to characterize improvement as function(hosts, link speeds, …)

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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

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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

• Thrubbcp ~0.8*Thruiperf

•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

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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

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Typical QBSS test bed• 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

100Mbps

100Mbps

1Gbps

Cisco 7200s

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Example of effects

•Also tried: 1 stream for all, and priority at 30%, 100 Mbps & 2 Gbps bottlenecks

•2Gbps challenge to saturate: did at SC2001, 3 Linux cpus with 5*1 Gbps NIC cards and 2 Gbps trunk from subnet to floor network, sending to 17 hosts in 5 countries

Kicks in fast (<~ 1 s)

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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

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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

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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, Rolf Reidi (Rice).

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SC2001 Bandwidth Challenge/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

• 2 Dell/Linux cpus + 2 GE NICs each, 1.4Gbps/cpu, plus 1 Dell with 1 GE NIC– Can get 980Mbits/s/cpu with jumbo frames

• Saturate 2Gbps connection to floor network• Got 1.6Mbits/s from booth 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

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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%

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WAN thruput conclusions• High FTP performance across WAN links is possible

–Can do 100s of Mbits/s–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–Need high speed cpus, disks, buses–Careful attention to copies, buffering etc.

• Performance is improving• OS must support big windows selectable by application• Need multiple parallel streams in some cases

• Improvements of 5 to 60 in thruput by using multiple streams & larger windows

• Impacts others users, QBSS looks hopeful

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More Information• IEPM/PingER home site:

– www-iepm.slac.stanford.edu/• Bulk throughput site:

– www-iepm.slac.stanford.edu/monitoring/bulk/• SC2001 & high throughput measurements

– www-iepm.slac.stanford.edu/monitoring/bulk/sc2001/• Transfer tools:

– http://dast.nlanr.net/Projects/Iperf/release.html– http://doc.in2p3.fr/bbftp/– www.slac.stanford.edu/~abh/bbcp/– http://hepwww.rl.ac.uk/Adye/talks/010402-ftp/html/sld015.htm

• TCP Tuning:– www.ncne.nlanr.net/training/presentations/tcp-tutorial.ppt– www-didc.lbl.gov/tcp-wan.html

• QBSS measurements– www-iepm.slac.stanford.edu/monitoring/qbss/measure.html