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The Effects of Wide-Area Conditions on WWW Server Performance

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. switch. server. clients. Problem: real Internet doesn’t work this way!. Motivation: Benchmarking Today.

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The Effects of Wide-Area Conditions on WWW Server Performance

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  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. switch server clients Problem: real Internet doesn’t work this way! Motivation: Benchmarking Today

  3. Internet clients server Evaluate WWW server performance under WAN conditions Motivation: Real Life

  4. Want an environment that is *both* realistic *and* reproducible 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. A testbed for server performance analysis 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: • Doesn’t reproduce a specific web site • Doesn’t reproduce a specific network topology

  7. WAN emulator WAN emulator used to drop & delay packets Centralized Approach 1 Gbps 100 Base-T Gigabit Ethernet switch server clients

  8. User App drop Socket TCP IP DummyNet client Ethernet delay WASP Approach • Each client acts as a ‘WAN emulator’ • Use DummyNet to drop and delay packets

  9. Centralized approach doesn’t scale Scaling with Load

  10. loss event probability (1 - loss event probability) conditional loss probability (1 - conditional loss probability) 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

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

  12. switch server clients Putting it all together Workload generator (WaspClient) Web server software ( Apache, Flash) Fast Ethernet Gigabit 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 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

  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. Throughputs fall with increasing loss Throughput vs. Loss Rate

  16. Utilization falls with increasing loss Utilization vs. Loss Rate

  17. What’s 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 increases with loss rate Latency vs. Loss Rate

  19. Capacity falls with loss rate 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. Throughputs decrease with RTT Throughput vs. RTT

  22. Utilization falls with increasing RTT Utilization vs. RTT

  23. Latency increases with larger RTT’s Latency vs. RTT

  24. Capacity falls slightly with RTT 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 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?

  26. SACK provides lower latency TCP Variants: Latency

  27. Benchmarks must include WAN characteristics 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

  28. WASP provides a general environment for performing all kinds of evaluations Future Directions • HTTP 1.1 • Linux • Bandwidth limitations • Dynamic content • Other workloads: • Proxies • Clients • SSL

  29. Capacity decreases with loss rate Apache Capacity vs. Loss

  30. RTT doesn’t really affect capacity Apache Capacity vs. RTT

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