1. Web Overview The birth of Web: 1989 Now Web is about everything – Business (HR systems, e.g. NUHR) – Online Shopping (Amazon), Banking (Citibank, Chase) – Communications (Gmail, Facebook) Become mission-critical – Performance – Security

2. Web 2.0 Web 1.0 – Basic HTML + Images What is Web 2.0? – No one really gives a clear definition – Features AJAX ( Asynchronous JavaScript and XML) DOM (Document Object Model) Flash CSS (Cascading Style Sheets) User involvement: Wiki, Blog, Social Networks

3. Web 2.0 Basics - JavaScript JavaScript – A scripting language with C/C++ like grammar – Dynamic, weakly typed language Eval() No need to claim the object types Web 2.0 websites are JavaScript heavy – Google Maps (510KB) – Google Calendar (152KB) – Facebook (558KB)

4. DOM (Document Object Model) One of the first JavaScript/DOM heavy apps: Gmail DOM Event API: Keyboard and mouse events DOM CSS API Sample Document An HTML Document This is a simple document.

5. AJAX ( Asynchronous JavaScript and XML) Foundation of popular web apps: Google Map, Gmail, Facebook, etc. Can transfer any object between browser  Web server, e.g. XML or JSON (JavaScript Object Notation) req = new XMLHttpRequest(); function callback () { … } function handler () { if (req.readyState == 4 && req.status == 200) { callback(req.responseText); } req.onreadystatechange = handler; req.open(“GET”, url, true); req.send(null); XMLHttpRequest register a callback function to be asynchronous enable JavaScript visit the url directly response can be either plain text or XML

6. 6 WebProphet: Automating Performance Prediction for Web Services Zhichun Li, Ming Zhang, Zhaosheng Zhu, Yan Chen, Albert Greenberg and Yi-min Wang Northwestern University Microsoft Research

7. 7 Large-scale Web Services Most large-scale online services today are web-based – Web search, map, Webmail, calendar, online stores, etc. Provided by Online Service Providers (OSPs) – MSN, Google, Yahoo, Amazon, etc. Hosted by multiple data-centers around the world More and more complex – Yahoo Maps: 110 embedded objects, complex object dependencies and 670KB JavaScript

8. 8 Amazon: 1% sale loss at the cost of 100ms extra delay Google found 500 ms extra delay reduce revenues by up to 20% Performance Is Important OSP A OSP B Revenue SLOW! Need a tool to understand and improve the user perceived performance.

9. 9 Large Web Services Are Complex BrowserBrowser Backend DCs Frontend DCs OSP Internal Network DNS Internet Complex UI  large browser delay Poor object dependency  more RTTs (online map needs 40~60 http objects) Potential Performance Problems Complex DNS redirection  long dns query (CNAME) Different servers  more dns queries RTT Packet loss interact with TCP Overload  Long response time Overload  Long response time for dynamic contents RTT Packet loss Need a tool to diagnose why slow? and where is the bottleneck?

10. Performance Prediction Problem Many ways can be used for performance optimization. However, cannot try them one by one, huge cost! What the performance will be under hypothetical optimization strategies? How to quickly evaluate the predicted performance? Performance ??? Optimization

11. 11 Outline Motivation Design Dependency Extraction Performance Prediction Implementation Evaluation Conclusion

12. Client Side Performance Prediction Provider-based techniques – Hard to consider multiple data sources – Object dependencies – Page rendering time Internet CDN Data Center

13. The Page Load Time Decomposition Page Load time Object Dependency Client DelayNet DelayServer Delay DNS DelayData Transfer RTTPacket loss Load time of Object i TCP 3-WAY

14. System Architecture Measurement Engine Dependency Extractor Performance Predictor New Scenarios Results PDGs

15. 15 Outline Motivation Design Dependency Extraction Performance Prediction Implementation Evaluation Conclusion

16. What are dependencies? The embedded objects in an HTML page Object requests generated by JavaScript depend on the corresponding.JS files External CSS and JavaScript files blocks the other embedded objects in the HTML page Event triggers, such as when image B trigger “onload” event, then image A will be load by JavaScript

17. Dependency Definitions Descendant(X): objects that depend on X Ancestor(X): objects that X depends on Parent(X): The objects that X directly depends on. Direct means can be the last among ancestors Based on parent relationship build PDG (parental dependency graph)

18. Discover Ancestors and Descendants We discover the descendant(X) sets by using time perturbation through HTTP proxy.

19. Extract non-stream parents Stream VS. Non-stream – HTML is stream objects and other types of objects are non-stream Non-stream parent extraction A B C D X YZ Descendant(A)={B,D} Descendant(B)={D}

20. Extract stream parents 1) Load the HTML page very slow 2) Delay other known non-stream parents X YZ Offset(Z) X YZ

21. Extract stream parents 1) Load the HTML page very slow 2) Delay other known non-stream parents X YZ Offset(Z) X YZ Offset2(Z)

22. 22 Outline Motivation Design Dependency Extraction Performance Prediction Implementation Evaluation Conclusion

23. Performance Prediction Procedure Extract Object timing information Annotate client delay Adjust each of object according to new scenario Simulate the page load process Packet trace PDG New Scenario PDG

24. Object Timing Info Basic object timing info Adding client delay info X Parent(X) Client delay DNS TCP HTTP DNS lookup time TCP handshaking time Response time Reply transfer time Request transfer time

25. Adjust Object Timing Info Adjust DNS lookup time directly Server response time: change the response time RTT: m * ΔRTTn * ΔRTT ΔRTT

26. Simulating Page Load Process I Browser behaviors

27. Simulating Page Load Process II Page load process – Find the earliest candidate C from CandidateQueue – Load C according to the conditions in the pervious slide – Find new candidates whose parents are all available – Adjust timings of new candidates – Insert new candidates into CanidateQueue

28. 28 Outline Motivation Design Dependency Extraction Performance Prediction Implementation Evaluation Conclusion

29. 29 WebProphet Framework Browser Control plug-in Web robot Scripting API Application transaction script snippet Pcap trace logger Agent network Results New scenario input PDGs Web Agent Web Proxy Dependency Extractor Annotate object timing info Page simulator Trace Analyzer Performance Predictor Traces The whole system is about 12,000 lines of code

30. Dependency Extraction Results Google and Yahoo Search Validation: manual code analysis Google Yahoo

31. Dependency Extraction Results Google and Yahoo Maps Validation: Create fake pages with the same PDGs and validate the fake pages Google Yahoo

32. Predication Accuracy Evaluate both median and 95-percentile Control experiment – 50% cases with predication error less than 6.1% – 90% cases with predication error less than 16.2% Planetlab experiment – Predication error of median less than 6.1% – Predication error of 95-percentile less than 10.7%

33. Usage Scenarios Analyze how to improve Yahoo Maps – Only want to optimize a small number of objects – Use a greedy based search – Evaluate 2,176 hypothetical scenarios, find that Move 5 objects to CDN: 14.8% Reduce client delays of 14 objects to half: 26.6% Combine both: 40.1%

34. 34 Outline Motivation Design Dependency Extraction Performance Prediction Implementation Evaluation Conclusion

35. Conclusions Develop a novel technique to extract the object dependencies of complex web pages Implement a simple but yet effective model to simulate the page load process Apply Webprophet to Yahoo Map to show that it can be useful for performance optimization