Presentation is loading. Please wait.

Presentation is loading. Please wait.

A Provider-side View of Web Search Response Time YINGYING CHEN, RATUL MAHAJAN, BASKAR SRIDHARAN, ZHI-LI ZHANG (UNIV. OF MINNESOTA) MICROSOFT.

Similar presentations


Presentation on theme: "A Provider-side View of Web Search Response Time YINGYING CHEN, RATUL MAHAJAN, BASKAR SRIDHARAN, ZHI-LI ZHANG (UNIV. OF MINNESOTA) MICROSOFT."— Presentation transcript:

1 A Provider-side View of Web Search Response Time YINGYING CHEN, RATUL MAHAJAN, BASKAR SRIDHARAN, 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 Google Bing revenue -20% Latency +2 sec revenue -4.3% Latency +0.5 sec

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

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

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

7 Impact Factors of SRT networkbrowserquery server

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

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

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

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 Peak hours Off-peak hours

15 Browser variations 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 Browser-X Browser-Y Javascript exec time 82% 1.82t t

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

19

20

21

22 Comparison with approaches that do not account for systemic variations WoWOne Gaussian model of SRT Change point detection False negative10%35%40% False positive7%17%19%

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

24 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

25 Questions?


Download ppt "A Provider-side View of Web Search Response Time YINGYING CHEN, RATUL MAHAJAN, BASKAR SRIDHARAN, ZHI-LI ZHANG (UNIV. OF MINNESOTA) MICROSOFT."

Similar presentations


Ads by Google