Slides

Report
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
 Google
Latency
+0.5 sec
revenue
-20%
 Bing
Latency
+2 sec
revenue
-4.3%
Understanding SRT behavior is challenging
SRT (ms)
300+t
t
W
T
Th
F
200+t
SRT (ms)
M
t
peak
off-peak
S
Su
Our work
 Explaining
 Identify
 Root
systemic SRT variation
SRT anomalies
cause localization
Client- and server-side instrumentation

ℎ



ℎ1



ℎ2



Referenced
content
Impact Factors of SRT
server

network
browser
query
ℎ  ℎ1   ℎ2       
Primary factors of SRT variation
 Apply
Analysis of Variance (ANOVA) on the time intervals
  =
SRT
variance
 
 , 
+ ƞ
Variance explained Unexplained
variance
by time interval k
Explained
variance (%)
60
40
20
0

server



network


ℎ
browser


query

Primary factors: network characteristics, browser speed, query type

Server-side processing time has a relatively small impact
RTT
Variation in network characteristics
Explaining network variations
 Residential
networks send a higher fraction of
queries during off-peak hours than peak hours
 Residential
networks are slower
residential
enterprise
unknown
RTT (ms)
1.25t
25%
t
residential enterprise
Residential networks are slower
Residential networks send a higher fraction of
queries during off-peak hours than peak hours
Variation in query type

Impact of query on SRT
 Server
processing time
 Richness

of response page
Measure: number of image
Explaining query type variation
Peak hours
Off-peak hours
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
Javascript
exec time
1.82t
82%
t
Browser-X Browser-Y
Summarizing systemic SRT variation
 Server:
Little impact
 Network:
 Query:
Poorer during off-peak hours
Richer during off-peak hours
 Browser:
Faster during off-peak hours
Detecting anomalous SRT variations
 Challenge:
interference from systemic variations
Week-over-Week (WoW) approach
 = Long term trend + Seasonality + Noise
Comparison with approaches that do not
account for systemic variations
WoW
False negative
10%
False positive
7%
One Gaussian Change point
model of SRT
detection
35%
40%
17%
19%
Conclusions
 Understanding
SRT is challenging
 Changes
in user demographics lead to systemic
variations in SRT
 Debugging
 Must
SRT is challenging
factor out systemic variations
Implications
 Performance
 Should
understand performance-equivalent classes
 Performance
 Should
management
consider the impact of network, browser, and query
 Performance

monitoring
debugging
End-to-end measures are tainted by user behavior changes
Questions?

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