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A Provider-side View of Web Search Response Time

A Provider-side View of Web Search Response Time. Yingying Chen, Ratul Mahajan, Baskar Sridharan , Zhi -Li Zhang (Univ. of Minnesota) Microsoft. Web services are the dominant way to find and access information. Web service latency is critical to service providers as well. revenue -20%.

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A Provider-side View of Web Search Response Time

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  1. A Provider-side View ofWeb Search Response Time Yingying Chen, Ratul Mahajan, BaskarSridharan, Zhi-Li Zhang (Univ. of Minnesota) Microsoft

  2. Web services are the dominant way to find and access information

  3. Web service latency is critical to service providers as well revenue -20% Latency +2 sec Latency +0.5 sec revenue -4.3% • Google • Bing

  4. Understanding SRT behavior is challenging 200+t 300+t SRT (ms) t t peak M W Th off-peak S F Su T SRT (ms)

  5. Our work • Explaining systemic SRT variation • Identify SRT anomalies • Root cause localization

  6. Client- and server-side instrumentation query on-load HTML header Brand header Query results BoP scripts Embedded images Referenced content

  7. Impact Factors of SRT network browser query server

  8. Primary factors of SRT variation • Apply Analysis of Variance (ANOVA) on the time intervals ƞ Unexplained variance SRT variance Variance explained by time interval k

  9. 60 40 20 0 Explained variance (%) • Primary factors: network characteristics, browser speed, query type • Server-side processing time has a relatively small impact network browser server query

  10. Variation in network characteristics RTT

  11. Explaining network variations • Residential networks send a higher fraction of queries during off-peak hours than peak hours • Residential networks are slower

  12. residential enterprise unknown 1.25t 25% t RTT (ms) enterprise residential Residential networks are slower Residential networks send a higher fraction of queries during off-peak hours than peak hours

  13. Variation in query type • Impact of query on SRT • Server processing time • Richness of response page • Measure: number of image

  14. Explaining query type variation Off-peak hours Peak hours

  15. Javascript exec time Browser variations 1.82t 82% • Two most popular browsers: X(35%), Y(40%) • Browser-Y sends a higher fraction of queries during off-peak hours • Browser-Y has better performance t Browser-Y Browser-X

  16. Summarizing systemic SRT variation • Server: Little impact • Network: Poorer during off-peak hours • Query: Richer during off-peak hours • Browser: Faster during off-peak hours

  17. Detecting anomalous SRT variations • Challenge: interference from systemic variations

  18. Week-over-Week (WoW) approach + Seasonality + Noise

  19. Comparison with approaches that do not account for systemic variations

  20. Conclusions • Understanding SRT is challenging • Changes in user demographics lead to systemic variations in SRT • Debugging SRT is challenging • Must factor out systemic variations

  21. Implications • Performance monitoring • Should understand performance-equivalent classes • Performance management • Should consider the impact of network, browser, and query • Performance debugging • End-to-end measures are tainted by user behavior changes

  22. Questions?

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