A provider-side view of web search response time

Computer Communication Review

Volumn 43, Issue 4, 2013, Pages 243-254

  Chen, Yingying ^a   Mahajan, Ratul ^a   Sridharan, Baskar ^a   Zhang, Zhi Li ^b  

^a MICROSOFT   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

Author keywords

anomaly detection and diagnosis; performance monitoring; search response time; web services

Indexed keywords

ANALYSIS FRAMEWORKS; ANOMALY DETECTION; DENIAL OF SERVICE ATTACKS; NETWORK CHARACTERISTICS; PERFORMANCE ANOMALY; PERFORMANCE MONITORING; PERIODIC CHANGES; SERVICE USAGE;

COMPUTER ARCHITECTURE; INFORMATION RETRIEVAL; NETWORK ARCHITECTURE; WEB SERVICES;

WEBSITES;

EID: 84891593576     PISSN: 01464833     EISSN: 19435819     Source Type: Conference Proceeding    
DOI: 10.1145/2534169.2486035     Document Type: Conference Paper

References (34)

1. Central limit theorem. http://en.wikipedia.org/wiki/Central-limit- theorem.
2. Measure page load time. https://developers.google.com/speed/docs/pss/ LatencyMeasure.
3. Pagespeed tools family. https://developers.google.com/speed/pagespeed/.
4. WebPageTest - website performance and optimization test. http://www.webpagetest.org/.
5. Yslow. http://yslow.org/.
6. T. Abdelzaher, K. Shin, and N. Bhatti. Performance guarantees for Web server end-systems: A control-theoretical approach. IEEE Transactions on Parallel and Distributed Systems, 2002.
7. E. Agichtein, E. Brill, S. Dumais, and R. Ragno. Learning user interaction models for predicting Web search result preferences. In ACM SIGIR, 2006.
8. M. Al-Fares, K. Elmeleegy, B. Reed, and I. Gashinsky. Overclocking the Yahoo! CDN for faster Web page loads. In IMC, 2011.
9. P. Bahl, R. Chandra, A. Greenberg, S. Kandula, D. Maltz, and M. Zhang. Towards highly reliable enterprise network services via inference of multi-level dependencies. In ACM SIGCOMM, 2007.
10. L. Barroso, J. Dean, and U. Holzle. Web search for a planet: The Google cluster architecture. IEEE Micro, 2003.
11. M. Butkiewicz, H. Madhyastha, and V. Sekar. Understanding website complexity: Measurements, metrics, and implications. In IMC, 2011.
12. Y. Chen, S. Jain, V. Adhikari, and Z. Zhang. Characterizing roles of front-end servers in end-to-end performance of dynamic content distribution. In IMC, 2011.
13. J. Dean. Achieving rapid response times in large online services. In Berkeley AMPLab Cloud Seminar, 2012.
14. M. Freedman, E. Freudenthal, and D. Mazieres. Democratizing content publication with coral. In USENIX NSDI, 2004.
15. A. Gelman. Analysis of variance, 2005. http://www.stat.columbia.edu/ ∼gelman/research/unpublished/econanova.pdf.
16. C. Gkantsidis and P. Rodriguez. Network coding for large scale content distribution. In IEEE INFOCOM, 2005.
17. C. Huang, A. Wang, J. Li, and K. Ross. Measuring and evaluating large-scale cdns. In IMC, 2008.
18. H. Jiang, J. Li, Z. Li, and X. Bai. Performance evaluation of content distribution in hybrid CDN-P2P network. In IEEE Future Generation Communication and Networking, 2008.
19. K. Johnson, J. Carr, M. Day, and M. Kaashoek. The measured performance of content distribution networks. Elsevier, 2001.
20. S. Kandula, R. Mahajan, P. Verkaik, S. Agarwal, J. Padhye, and P. Bahl. Detailed diagnosis in enterprise networks. In ACM SIGCOMM, 2009.
21. J. Kangasharju, K. Ross, and J. Roberts. Performance evaluation of redirection schemes in content distribution networks. Elsevier, 2001.
22. R. Kohavi, R. M. Henne, and D. Sommerfield. Practical guide to controlled experiments on the Web: Listen to your customers not to the HiPPO. In SIGKDD, 2007.
23. R. Krishnan, H. Madhyastha, S. Srinivasan, S. Jain, A. Krishnamurthy, T. Anderson, and J. Gao. Moving beyond end-to-end path information to optimize CDN performance. In IMC, 2009.
24. A. Pinto. How to measure page load time with google analytics. http://www.yottaa.com/blog/bid/215491/How-to-Measure-Page-Load-Time-With-Google- Analytics.
25. D. G. Rees. Foundations of statistics, volume 214. Chapman & Hall, 1987.
26. M. Roughan, A. Greenberg, C. Kalmanek, M. Rumsewicz, J. Yates, and Y. Zhang. Experience in measuring backbone traffic variability: Models, metrics, measurements and meaning. In ACM IMW, 2002.
27. A. Singhal and M. Cutts. Official Google webmaster central blog: Using site speed in Web search ranking, 2009. http://googlewebmastercentral.blogspot. com/2010/04/using-site-speed-in-web-search-ranking.html.
28. Y. Song, V. Ramasubramanian, and E. Sirer. Optimal resource utilization in content distribution networks. Technical Report TR2005-2004, Cornell University, 2005.
29. S. Stefanov. Progressive rendering via multiple flushes. http://www.phpied.com/progressive-rendering-via-multiple-flushes/.
30. W. Taylor. Change-point analysis: a powerful new tool for detecting changes, 2000. http://www.variation.com/cpa/tech/changepoint.html.
31. S. Triukose, Z. Wen, and M. Rabinovich. Measuring a commercial content delivery network. In ACM WWW, 2011.
32. X. S. Wang, A. Balasubramanian, A. Krishnamurthy, and D. Wetherall. Demystify page load performance with WProf. In USENIX NSDI, 2013.
33. R. White and S. Dumais. Characterizing and predicting search engine switching behavior. In ACM conference on Information and knowledge management, 2009.
34. S. Zander, L. Andrew, G. Armitage, G. Huston, and G. Michaelson. Mitigating sampling error when measuring Internet client IPv6 capabilities. In IMC, 2012.