2.  Zhichun Li  The Robust and Secure Systems group at NEC Research Labs  Northwestern University  Tsinghua University 2

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5. 5  Almost everything is related to Web  Web search  Web mail  Maps  Online shopping  Online Social network  Calendar

6.  Amazon: 100ms extra delay  1% sale loss  Google search: 500 ms extra delay  reduce display ads revenues by up to 20% 6

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9.  Example of Yahoo Maps  110 embedded objects  Complex object dependencies  670KB JavaScript  multiple data‐centers around the world 9

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11.  A/B test (controlled experiments)  Idea: Measure through experiments by various service setting  Problems: Hard to fully automated Expensive to set up Quite slow: brute force 11

12.  Service provider based techniques (WISE SIGCOMM2008)  Idea: Based on server logs  Problems: Multiple data sources Object dependencies Client side delays, e.g. JavaScript execution time 12

13.  Regression based techniques (INFOCOM2009)  Idea: Link Gradients predict E2E RST  Problems: Independence assumption Only work for small-scale 13

14.  A tool for automated performance prediction Fast prediction on the user perceived performance Timing perturbation based dependency discovery Dependency driven page load simulation 14

16. 16 Parent( ) = Ancestor( ) = Descendant( ) =

17.  Simple HTML parsing/DOM traversal is not enough Object requests generated by JavaScript depend on the corresponding.JS files Event triggers: e.g. image A need image B trigger “onload” event  Extensive browser instrumentation is heavy- weight and browser dependent 17

18.  Goal: Light‐weight black box based approach Browser independent  Solution: Timing Perturbation based technique Inject delay See how delay propagate Dependence Graph 18

19.  Regular Objects Regular objects have to be fully loaded before their descendants  HTML Objects are special HTML is stream objects, allowing incremental rendering 19

20.  Discovering Descendants: Inject delay Descendant of X: delayed together with X  Discovering Parent: Non-stream parent: Idea: Parent of Z is X? No Y between Z and X Algorithm: Stream Parent: Offset 20

22. 22 PLT of Basic Scenario PLT of New Scenarios Predict

23. 23 Change Delay Factors: Client, Server, RTT and DNS lookup Change Delay Factors: Client, Server, RTT and DNS lookup

24. 24 Challenge: Keep the model simple while accuracy Solution:  Right level abstraction  Most fundamental characteristics

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27. 27 Where? What? Packet traces (Easy deployable) Three types of activities: Additional: infer RTT and # of RT Response Time = RTT + server delay

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29. 29 What is Client Delay? Last Parent is Available Browser request X Parsing HTML Executing JAVAscript

30. 30 How to get Client Delay? + Client Delay =

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32. 32 Timing of Basic Scenario Basic Timing from Trace Basic Timing from Trace Client Delay =+ Adjusted Timing of New Scenario Change Delay Factors: (According to New Scenario) Client, Server, RTT and DNS lookup Change Delay Factors: (According to New Scenario) Client, Server, RTT and DNS lookup

33. Other Factors:  Add DNS lookup time based on DNS cache  Add TCP handshaking time for new connections  Add TCP waiting time when all connections are not available 33 One assumption: # of round-trips involved in loading an object not change

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35. 35 Timing of New Scenario Browser Behavior ++

36.  HTTP/1.1  No pipelining Not been widely supported by proxies Head of line blocking  Limit of TCP connections (for host, not IP) 36 Browser Behavior

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49.  Metrics:  Controlled experiments Baseline: high latency New Scenario: low latency Use controlled gateway to inject and remove delays  Planetlab experiments Baseline: International nodes New scenario: US nodes Improve all delay factors to be the same as the US node. 49

50.  Setup: visit Yahoo Maps from Northwestern  Baseline: inject 100ms RTT to one DC  New Scenario: removing the 100ms RTT 50

51.  Baseline: International node (relative poor performance)  New Scenario: US node (good performance) 51

52.  Web service performance prediction is hard – Modern web services are complicated – Object dependencies are very important  An automated tool for performance prediction – Dependency discovery by Timing Perturbation – Dependency (PDG) driven Performance Predication  Evaluation on the accuracy and usefulness 52

53.  Cannot deal with dynamic object dependencies.  Packet loss assumption.  Lack of error warning scheme when it fails.  Mean and Variance of PLT.  Unit Load Time = PLT/PLW.  Ways to reduce simulation times? 53