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The Effects of Wide-Area Conditions on WWW Server Performance

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The Effects of Wide-Area Conditions on WWW Server Performance Erich Nahum, Marcel Rosu, Srini Seshan, Jussara Almeida IBM T.J. Watson Research Center, – PowerPoint PPT presentation

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Title: The Effects of Wide-Area Conditions on WWW Server Performance


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, CMU, Univ.
of Wisconsin
2
Motivation Benchmarking Today
3
Motivation Real Life
4
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.
  • Live Server Analysis
  • California elections, 96 Olympics, WAWM
  • Capture real WAN conditions
  • - But not reproducible

5
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

6
Wide-Area Server Performance
  • What WASP is
  • Realistic emulates the WAN environment
  • Reproducible allows iterative analysis
  • Configurable can vary many parameters
  • Scalable scales to very large workloads
  • What WASP is not
  • Doesnt reproduce a specific web site
  • Doesnt reproduce a specific network topology


7
Centralized Approach
1 Gbps
100 Base-T
Gigabit Ethernet switch
server
clients
8
WASP Approach
  • Each client acts as a WAN emulator
  • Use DummyNet to drop and delay packets

9
Scaling with Load
10
Packet Loss Model
Good
Bad
  • 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

11
Workload Generators
Responses
Requests
  • S-client (from Rice), SURGE (from BU)
  • WaspClient integrates the two

12
Putting it all together
Workload generator (WaspClient)
Web server software ( Apache, Flash)
Gigabit Ethernet
Fast Ethernet
200 MHz PowerPC w/AIX 4.3.3
500 MHz P/3 w/ FreeBSD 3.3 DummyNet
13
Experimental Methodology
  • Performance Metrics
  • Server throughput, utilization, response time,
    capacity
  • Sensitivity Analysis
  • Vary generated load in SURGE UEs
  • 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

14
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

15
Throughput vs. Loss Rate
16
Utilization vs. Loss Rate
17
Whats going on?
Simple model for TCP throughput, where B max
segment size (MSS), R round-trip time, and p
loss rate. More elaborate models available
from Padhye et al. (SigComm98), Cardwell et al.
(Infocom2000)
18
Latency vs. Loss Rate
19
Capacity vs. Loss Rate
20
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

21
Throughput vs. RTT
22
Utilization vs. RTT
23
Latency vs. RTT
24
Capacity vs. RTT
25
Many Variants of TCP
  • Reno (current baseline in the Internet)
  • Coarse-grained timeouts, fast retransmit
  • Recovers 1 lost segment every 3 RTTs
  • 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?
26
TCP Variants Latency
27
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,
  • dont affect capacity
  • Other fallout
  • Used to find bugs in AIX, Flash, AFPA (IBM
    server)
  • Convinced AIX group to deploy SACK New Reno

28
Future Directions
  • HTTP 1.1
  • Linux
  • Bandwidth limitations
  • Dynamic content
  • Other workloads
  • Proxies
  • Clients
  • SSL

29
Apache Capacity vs. Loss
30
Apache Capacity vs. RTT
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