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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

A Provider-side View ofWeb Search Response Time

Yingying Chen, Ratul Mahajan,

BaskarSridharan, Zhi-Li Zhang (Univ. of Minnesota)

Microsoft



Web service latency is critical to service providers as well
Web service latency is critical to service providers as well information

revenue

-20%

Latency

+2 sec

Latency

+0.5 sec

revenue

-4.3%

  • Google

  • Bing


Understanding srt behavior is challenging
Understanding SRT behavior is challenging information

200+t

300+t

SRT (ms)

t

t

peak

M

W

Th

off-peak

S

F

Su

T

SRT (ms)


Our work
Our work information

  • Explaining systemic SRT variation

  • Identify SRT anomalies

  • Root cause localization


Client and server side instrumentation
Client- and server-side instrumentation information

query

on-load

HTML header

Brand header

Query results

BoP scripts

Embedded images

Referenced content


Impact factors of srt
Impact Factors of SRT information

network

browser

query

server


Primary factors of srt variation
Primary factors of SRT variation information

  • Apply Analysis of Variance (ANOVA) on the time intervals

ƞ

Unexplained

variance

SRT variance

Variance explained by time interval k


60 information

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



Explaining network variations
Explaining network variations information

  • Residential networks send a higher fraction of queries during off-peak hours than peak hours

  • Residential networks are slower


residential information

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


Variation in query type
Variation in query type information

  • Impact of query on SRT

    • Server processing time

    • Richness of response page

  • Measure: number of image


Explaining query type variation
Explaining query type variation information

Off-peak hours

Peak hours


Browser variations

Javascript information 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


Summarizing systemic srt variation
Summarizing systemic SRT variation information

  • Server: Little impact

  • Network: Poorer during off-peak hours

  • Query: Richer during off-peak hours

  • Browser: Faster during off-peak hours


Detecting anomalous srt variations
Detecting anomalous SRT variations information

  • Challenge: interference from systemic variations


Week over week wow approach
Week-over-Week ( informationWoW) approach

+ Seasonality + Noise



Conclusions
Conclusions variations

  • Understanding SRT is challenging

    • Changes in user demographics lead to systemic variations in SRT

    • Debugging SRT is challenging

      • Must factor out systemic variations


I mplications
I variationsmplications

  • 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


Questions? variations


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