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A Provider-side View ofWeb 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
Bing
revenue-20%
Latency+2 sec
revenue-4.3%
Latency+0.5 sec
Understanding SRT behavior is challenging
t
300+tS
RT
(m
s)
M T W Th F S Su
peak off-peak
200+t
t
SR
T (
ms)
Our work
Explaining systemic SRT variation
Identify SRT anomalies
Root cause localization
Client- and server-side instrumentation
HTML header
Brand header
BoP scriptsQuery results
Embedded images
query
𝑇 𝑓𝑠 𝑇 𝑓𝑐
𝑇 h𝑒𝑎𝑑
𝑇 𝑏𝑟𝑎𝑛𝑑
𝑇 h𝑖𝑛𝑡𝑐 𝑘1
𝑇 𝑟𝑒𝑠𝐻𝑇𝑀𝐿
𝑇 𝐵𝑂𝑃
𝑇 h𝑖𝑛𝑡𝑐 𝑘2
𝑇 𝑒𝑚𝑏𝑒𝑑
𝑇 𝑟𝑒𝑓
𝑇 𝑠𝑐𝑟𝑖𝑝𝑡
𝑇 𝑠𝑐
𝑇 𝑡𝑐
on-load
Referenced content
Impact Factors of SRT
𝑇 𝑓𝑠
network browser queryserver
𝑇 h𝑒𝑎𝑑𝑇 𝑏𝑟𝑎𝑛𝑑𝑇 h𝑖𝑛𝑡𝑐 𝑘1𝑇 𝑟𝑒𝑠𝐻𝑇𝑀𝐿𝑇 𝐵𝑂𝑃𝑇 h𝑖𝑛𝑡𝑐 𝑘2𝑇 𝑟𝑒𝑓𝑇 𝑠𝑐𝑟𝑖𝑝𝑡𝑇 𝑛𝑒𝑡𝑇 𝑠𝑐𝑇 𝑓𝑐𝑇 𝑒𝑚𝑏𝑒𝑑 𝑇 𝑡𝑐
Primary factors of SRT variation
Apply Analysis of Variance (ANOVA) on the time intervals
ƞ
SRT variance
Variance explained by time interval k
Unexplainedvariance
Primary factors: network characteristics, browser speed, query type Server-side processing time has a relatively small impact
network browser queryserver
𝑇 h𝑒𝑎𝑑𝑇 𝑟𝑒𝑠𝐻𝑇𝑀𝐿𝑇 𝐵𝑂𝑃𝑇 𝑟𝑒𝑓 𝑇 𝑠𝑐𝑟𝑖𝑝𝑡𝑇 𝑛𝑒𝑡 𝑇 𝑠𝑐𝑇 𝑓𝑐 𝑇 𝑡𝑐
Exp
lain
ed
vari
an
ce (
%) 6
0
40
20
0
Variation in network characteristics
RT
T
Explaining network variations
Residential networks send a higher fraction of queries during off-peak hours than peak hours
Residential networks are slower
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
residential unknownenterprise
Variation in query type
Impact of query on SRT Server processing timeRichness 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
Browser-X Browser-Y
Javascript exec time
82%
1.82t
t
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
Detecting anomalous SRT variations
Challenge: interference from systemic variations
Week-over-Week (WoW) approach
+ Seasonality + Noise
Comparison with approaches that do not account for systemic variations
WoW One Gaussian model of
SRT
Change point
detection
False negative 10% 35% 40%
False positive 7% 17% 19%
Conclusions
Understanding SRT is challengingChanges in user demographics lead to systemic
variations in SRT
Debugging SRT is challenging Must factor out systemic variations
Implications
Performance monitoringShould understand performance-equivalent classes
Performance managementShould consider the impact of network, browser, and
query
Performance debugging End-to-end measures are tainted by user behavior
changes
Questions?