The Effects of Wide-Area Conditions on WWW Server Performance Erich Nahum, Marcel Rosu, Srini...
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![Page 1: The Effects of Wide-Area Conditions on WWW Server Performance Erich Nahum, Marcel Rosu, Srini Seshan, Jussara Almeida IBM T.J. Watson Research Center,](https://reader036.fdocuments.us/reader036/viewer/2022062421/56649c735503460f94924b55/html5/thumbnails/1.jpg)
The Effects of Wide-Area Conditions on WWW Server
Performance
Erich Nahum, Marcel Rosu, Srini Seshan, Jussara Almeida
IBM T.J. Watson Research Center, CMU, Univ. of Wisconsin
![Page 2: The Effects of Wide-Area Conditions on WWW Server Performance Erich Nahum, Marcel Rosu, Srini Seshan, Jussara Almeida IBM T.J. Watson Research Center,](https://reader036.fdocuments.us/reader036/viewer/2022062421/56649c735503460f94924b55/html5/thumbnails/2.jpg)
Motivation: Benchmarking Today
clients
switch
server
Problem: real Internet doesn’t work this way!
![Page 3: The Effects of Wide-Area Conditions on WWW Server Performance Erich Nahum, Marcel Rosu, Srini Seshan, Jussara Almeida IBM T.J. Watson Research Center,](https://reader036.fdocuments.us/reader036/viewer/2022062421/56649c735503460f94924b55/html5/thumbnails/3.jpg)
Motivation: Real Life
clients
server
Internet
Evaluate WWW server performance under WAN conditions
![Page 4: The Effects of Wide-Area Conditions on WWW Server Performance Erich Nahum, Marcel Rosu, Srini Seshan, Jussara Almeida IBM T.J. Watson Research Center,](https://reader036.fdocuments.us/reader036/viewer/2022062421/56649c735503460f94924b55/html5/thumbnails/4.jpg)
Web Server Performance
• Workload Generators Webstone, SpecWeb, SURGE, s-client, httperf, etc.+ Based on measured traffic behavior+ Reproducible- WAN case is ignored: no drops, delays, etc.
Want an environment that is *both* realistic *and* reproducible
• Live Server Analysis California elections, 96 Olympics, WAWM+ Capture real WAN conditions- But not reproducible
![Page 5: The Effects of Wide-Area Conditions on WWW Server Performance Erich Nahum, Marcel Rosu, Srini Seshan, Jussara Almeida IBM T.J. Watson Research Center,](https://reader036.fdocuments.us/reader036/viewer/2022062421/56649c735503460f94924b55/html5/thumbnails/5.jpg)
Outline
• Motivation and Background• The WASP Environment
– Hardware and software– Workload generators
• Results– Effects of packet loss– Effects of packet delay– Effects of TCP variants
• Summary and Conclusions
![Page 6: The Effects of Wide-Area Conditions on WWW Server Performance Erich Nahum, Marcel Rosu, Srini Seshan, Jussara Almeida IBM T.J. Watson Research Center,](https://reader036.fdocuments.us/reader036/viewer/2022062421/56649c735503460f94924b55/html5/thumbnails/6.jpg)
Wide-Area Server Performance
• What WASP is not:– Doesn’t reproduce a specific web site– Doesn’t reproduce a specific network topology
• What WASP is:– Realistic: emulates the WAN environment– Reproducible: allows iterative analysis– Configurable: can vary many parameters– Scalable: scales to very large workloads
A testbed for server performance analysis
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Centralized Approach
GigabitEthernetswitch
server
clients
WAN emulator used to drop & delay packets
WANemulator
100 Base-T 1 Gbps
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WASP Approach
• Each client acts as a ‘WAN emulator’• Use DummyNet to drop and delay packets
User AppSocket
TCP
IPDummyNet
Ethernetclientdelay
drop
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Scaling with Load
Centralized approach doesn’t scale
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Packet Loss Model
• Two-state loss model based on work by Bolot 93, Paxson 97, Rubenstein et al. 2000
• Packets forwarded in good state, dropped in bad• Transitions based on desired loss rate
Good Bad
loss event probability
(1 - loss event probability)
conditional lossprobability
(1 - conditional loss probability)
![Page 11: The Effects of Wide-Area Conditions on WWW Server Performance Erich Nahum, Marcel Rosu, Srini Seshan, Jussara Almeida IBM T.J. Watson Research Center,](https://reader036.fdocuments.us/reader036/viewer/2022062421/56649c735503460f94924b55/html5/thumbnails/11.jpg)
Workload Generators
• S-client (from Rice), SURGE (from BU) • WaspClient integrates the two
Responses Requests
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Putting it all together
clients
switch
server
Web server software( Apache, Flash)
200 MHz PowerPC w/AIX 4.3.3
Workload generator
(WaspClient)
500 MHz P/3w/ FreeBSD
3.3 & DummyNet
GigabitEthernet
FastEthernet
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Experimental Methodology
• Performance Metrics:– Server throughput, utilization, response time,
capacity
• Sensitivity Analysis:– Vary generated load in SURGE UE’s– Vary loss rate from 0 to 9 %– Vary RTT from 0 to 400 msec– Parameters taken from Paxson97, Allman2000
• Methodology:– Average of 10 runs– Each run lasts 10 minutes– 90 % confidence intervals
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Outline
• Motivation and Background• The WASP Environment
– Hardware and software– Workload generators
• Results– Effects of packet loss– Effects of packet delay– Effects of TCP variants
• Summary and Conclusions
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Throughput vs. Loss Rate
Throughputs fall with increasing loss
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Utilization vs. Loss Rate
Utilization falls with increasing loss
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What’s going on?
pR
BT
*
*3/25.1
Simple model for TCP throughput, where:B = max segment size (MSS), R = round-trip time, andp = loss rate.
More elaborate models available from:Padhye et al. (SigComm98), Cardwell et al. (Infocom2000)
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Latency vs. Loss Rate
Latency increases with loss rate
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Capacity vs. Loss Rate
Capacity falls with loss rate
![Page 20: The Effects of Wide-Area Conditions on WWW Server Performance Erich Nahum, Marcel Rosu, Srini Seshan, Jussara Almeida IBM T.J. Watson Research Center,](https://reader036.fdocuments.us/reader036/viewer/2022062421/56649c735503460f94924b55/html5/thumbnails/20.jpg)
Outline
• Motivation and Background• The WASP Environment
– Hardware and software– Workload generators
• Results– Effects of packet loss– Effects of packet delay– Effects of TCP variants
• Summary and Conclusions
![Page 21: The Effects of Wide-Area Conditions on WWW Server Performance Erich Nahum, Marcel Rosu, Srini Seshan, Jussara Almeida IBM T.J. Watson Research Center,](https://reader036.fdocuments.us/reader036/viewer/2022062421/56649c735503460f94924b55/html5/thumbnails/21.jpg)
Throughput vs. RTT
Throughputs decrease with RTT
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Utilization vs. RTT
Utilization falls with increasing RTT
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Latency vs. RTT
Latency increases with larger RTT’s
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Capacity vs. RTT
Capacity falls slightly with RTT
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Many Variants of TCP:
• Reno (current baseline in the Internet):– Coarse-grained timeouts, fast retransmit– Recovers 1 lost segment every 3 RTT’s
• New Reno:– Uses partial acknowledgement to improve loss recovery– Recovers 1 lost segment every RTT– Sender-side only modification
• Selective Acknowledgements (SACK):– Uses SACK option bit field to improve loss recovery– Recovers up to 3 segments per RTT– Requires modifications to both sender and receiver
• Other schemes exist (e.g., Vegas)
How do variants affect server performance?
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TCP Variants: Latency
SACK provides lower latency
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Summary• Presented the WASP environment
– Emulates WAN conditions in a controlled setting– Scalable, reproducible, configurable
• Several results:– Delays and losses affect performance– Loss reduces capacity, increases latency– Delays increase latency but not capacity– SACK, New Reno can reduce response time, don’t affect capacity
• Other fallout:– Used to find bugs in AIX, Flash, AFPA (IBM server)– Convinced AIX group to deploy SACK & New Reno
Benchmarks must include WAN characteristics
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Future Directions
• HTTP 1.1• Linux• Bandwidth limitations• Dynamic content• Other workloads:
– Proxies– Clients– SSL
WASP provides a general environment for performing all kinds of evaluations
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Apache Capacity vs. Loss
Capacity decreases with loss rate
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Apache Capacity vs. RTT
RTT doesn’t really affect capacity