1. System and User Aspects of Web Search Latency
Ioannis Arapakis, Xiao Bai, B. Barla Cambazoglu
Yahoo Labs, Barcelona

2. Web Search Engines

3. Search Engine Result Page (SERP)

4. Actors in Web Search
§ User’s perspective: accessing information
• relevance
• speed
§ Search engine’s perspective: monetization
• attract more users
• increase the ad revenue
• reduce the operational costs
§ Advertiser’s perspective: publicity
• attract more customers
• pay little

5. Components of a Web Search Engine
§ Major components: Web crawling, indexing, and query processing
crawler
Web
indexer
query
processor
document
collection index
query
results user

6. Expectations from a Web Search Engine
§ Crawl and index a large fraction of the Web
§ Maintain most recent copies of the content in the Web
§ Scale to serve hundreds of millions of queries every day
§ Evaluate a typical query under several hundred milliseconds
§ Serve most relevant results for a user query

7. Multi-site Web Search Engines (why?)
§ Better scalability and fault tolerance
§ Faster web crawling due to reduced proximity to web servers
§ Lower response latency due to reduced proximity to users
DC1
DC2
DC3
DC4

8. Search Data Centers
§ Quality and speed requirements
imply large amounts of compute
resources, i.e., very large data
centers
§ High variation in data center sizes
• hundreds of thousands of computers
• a few computers

9. Web Search Engine Architectures
§ Partitioned§ Replicated§ Centralized

10. Energy-Price-Driven Query
Processing Problem

11. Idea
§ Shifting the query workload among the search sites to
decrease the total financial cost involved in query processing
by exploiting the spatio-temporal variation in
• Query workload"
• Energy price"

12. Problem
Query
?
Local site
Remote sites
§ Forward a query to another data center based on
• Query processing capacities
• Estimated workloads
• Estimated response latency
• Current energy prices

13. Example
21
= $3
21 1 1
= $1
2
= $3
2 1 1 1
= $1
A
= $2
B
C A
= $2
1
B
C
Cost = $26 Cost = $22
221
1

14. Spatio-Temporal Variation
0 3 6 9 12 15 18 21
Hour of the day (GMT+0)
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.018
0.020
Queryvolume(normalizedbythetotalvolume)
DC-A (GMT+10)
DC-B (GMT-3)
DC-C (GMT-4)
DC-D (GMT+1)
DC-E (GMT-4)
Country
0
50
100
150
200
250
300
350
400
450
500
Electricprice($/MWh)
Denmark
Italy
Germany
Sweden
France
Australia
Turkey
Singapore
Portugal
Peru
USA
Iceland
Malaysia
Finland
Canada
0 3 6 9 12 15 18 21
Hour of the day (GMT-4)
0
15
30
45
60
75
90
105
120
Electricprice($/MWh)
Most expensive day (Tuesday)
Week average
Cheapest day (Saturday)
§ Search queries" § Electricity price

15. Formal Problem Definition
§ Minimize financial cost"
§ Response time constraint
§ Capacity constraint

16. Solution

17. Generating Probabilities
A
D
C
B
Remote sites
Local site
Query
w.p. pA(A)
w.p. pA(C)

18. Generating Probabilities
§ Cheap
§ Expensive

19. Price Variation Configurations
§ Universal
§ Spatial
§ Temporal
§ Spatio-temporal
30
60
90
120
150
180
210
240
270
300
Price($/MWh)
DC-A, DC-B, DC-C, DC-D, DC-E DC-A
DC-B
DC-C
DC-D
DC-E
0 3 6 9 12 15 18 21
Hour of the day (GMT+0)
30
60
90
120
150
180
210
240
270
300
Price($/MWh)
DC-A
DC-B
DC-C
DC-D
DC-E
0 3 6 9 12 15 18 21
Hour of the day (GMT+0)
DC-A
DC-B
DC-C
DC-D
DC-E
Universal (PC-U) Temporal (PC-T)
Spatial (PC-S) Spatio-temporal (PC-ST)
U T
S ST

20. Impact of Temporal and Spatial Price Variation
44 64 76 81 94 99 139 148 168 176
Network latency between data center pairs (ms)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Fraction
Saving (r=400)
Saving (r=800)
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Forwarded queries (r=400)
Forwarded queries (r=800)
0 3 6 9 12 15 18 21
Hour of the day
0.00
0.01
0.02
0.03
0.04
0.05
0.06
Fraction
Saving
Forwarded queries
Electric price

21. Query Response Time and Cost Saving
>100 >200 >400 >800
Query response time (ms)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
Fractionofquerytrafficvolume
PC-U
PC-T
PC-S
PC-ST
200 400 800 inf.
Query response time limit (ms)
0.00
0.04
0.08
0.12
0.16
0.20
0.24
0.28
0.32
0.36
0.40
Savinginelectriccost
PC-T
PC-S
PC-ST

22. Human Perception of Web Search Latency

23. Background Information
§ The core research in IR has been on improving the quality of
search results with the eventual goal of satisfying the
information needs of users
§ This often requires sophisticated and costly solutions
• more information stored in the inverted index
• machine-learned ranking strategies
• fusing results from multiple resources

24. Trade-off between the speed of a search system and
the quality of its results
Too slow or too fast may result in financial consequences
for the search engine

25. § Web users
• are impatient
• have limited time
• expect sub-second response times
§ High response latency
• can distract users
• results in fewer query submissions
• decreases user engagement over time
Web Search Economics

26. Components of User-Perceived Response Latency
§ network latency: tuf + tfu
§ search engine latency: tpre + tfb + tproc + tbf + tpost
§ browser latency: trender
User
Search
frontend
Search
backend
tpre tproc
tpost
tfb
tbf
tuf
tfu
trender

27. Distribution of Latency Values
0
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.040
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4
0
0.2
0.4
0.6
0.8
1.0
Fractionofqueries
Cumulativefractionofqueries
Latency (normalized by the mean)

28. Contribution of Latency Components
0
20
40
60
80
100
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4
Contributionpercomponent(%)
Latency (normalized by the mean)
search engine latency
network latency
browser latency

29. Experimental Design
User Study 1: User
Sensitivity to Latency
User Study 2: Impact of
Latency on Search Experience

30. Study 1:
User Sensitivity to Latency

31. Experimental Method (Task 1 & 2)
§ Independent variables:
• Search latency (0 – 2750ms)
• Search site speed (slow, fast)
§ 12 participants (female=6, male=6)
• aged from 24 to 41
• Full-time students (33.3%), studying while working (54.3%),
full-time employees (16.6%)

32. Task 1: Procedure
§ Participants submitted 40 navigational queries
§ After submitting each query, they were asked to report if the
response of the search site was “slow” or “normal”
§ For each query we increased latency by a fixed amount (0 –
1750ms), using a step of 250ms
§ Each latency value (e.g., 0, 250, 500) was introduced 5 times,
in a random order

33. Task 1: Results
250 750 1250 1750
Added latency (ms)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Likelihoodoffeelingaddedlatency
Slow SE (base)
Slow SE
Fast SE (base)
Fast SE
250 750 1250 1750
Added latency (ms)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Increaserelativetobaselikelihood
Slow SE
Fast SE
§ Delays <500ms are not
easily noticeable
§ Delays >1000ms are
noticed with high
likelihood

34. Task 2: Procedure
§ Participant submitted 50 navigational queries
§ After each query submission they provided an estimation of
the search latency in milliseconds
§ Search latency was set to a fixed amount (500 – 2750ms),
using a step of 250ms
§ Each latency value (e.g., 0, 250, 500) was introduced 5
times, in a random order
§ A number of training queries was submitted without any
added delay

35. Task 2: Results
Fig. 1: Slow search engine
1750 2000 2250 2500 2750
Actual latency (ms)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Predictedlatency(ms)
Actual
Males
Females
Average
750 1000 1250 1500 1750 2000 2250 2500 2750
Actual latency (ms)
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Predictedlatency(ms)
Actual
Males
Females
Average
Fig. 2: Fast search engine

36. Study 2:
Impact of Latency on Search Experience

37. Experimental Design
§ Two independent variables
• Search latency (0, 750, 1250, 1750)
• Search site speed (slow, fast)
§ Search latency was adjusted by a desired amount using a
custom-made JS deployed through the Greasemonkey extension
§ 20 participants (female=10, male=10)

38. Procedure
§ Participants performed four search tasks
• Evaluate the performance of four different backend search systems
• Submit as many navigational queries from a list of 200 randomly sampled
web domains
• For each query they were asked to locate the target URL among the first
ten results of the SERP
§ Training queries were used to allow participants to familiarize
themselves with the “default” search site speed

39. Questionnaires
§ User Engagement Scale (UES)
• Positive affect (PAS)
• Negative affect (NAS)
• Perceived usability
• Felt involvement and focused attention
§ IBM’s Computer System Usability Questionnaire (CSUQ)
• System usefulness (SYSUSE)
§ Custom statements

40. Descriptive Statistics (M) for UE and SYSUSE
§ Positive bias towards SEfast
§ SEfast participants were more deeply engaged
§ SEfast participants’ usability perception was more tolerant to delays
SEslow latency SEfast latency
0 750 1250 1750 0 750 1250 1750
Post-Task Positive Affect 16.20 14.50 15.50 15.20 20.50 19.00 20.80 19.30
Post-Task Negative Affect 7.00 6.80 7.60 6.90 6.80 7.40 7.40 7.20
Frustration 3.20 3.10 2.90 3.30 2.80 3.00 3.50 2.60
Focused Attention 22.80 22.90 19.90 22.20 27.90 26.60 23.90 29.50
SYSUS 32.80 28.90 29.80 27.90 35.20 31.30 29.80 33.20

41. Correlation Analysis of Beliefs and Reported Scales
Correlation Matrix
Beliefs postPAS postNAS FA
CSUQ-
SYSUS
custom-1 custom-2 custom-3
SEslow will respond fast to my queries .455** .041 0.702** .267 .177 .177 .082
SEslow will provide relevant results .262 -.083 .720** .411** .278 .263 .232
SEfast will respond fast to my queries -.051** .245 .341* .591** .330* .443** .624**
SEfast will provide relevant results -.272 .133 -.133 .378* .212 .259 .390*
*. Correlation is significant at the .05 level (2-tailed). **. Correlation is significant at the .01 level (2-tailed)

42. Query Log Analysis

43. Query Log and Engagement Metric
§ Random sample of 30m web search queries obtained from Yahoo
§ We use the end-to-end (user perceived) latency values
§ To control for differences due to geolocation or device, we select
queries issued:
• Within the US
• To a particular search data center
• From desktop computers
§ We quantify engagement using the clicked page ratio metric

44. 0.0
0.2
0.4
0.6
0.8
1.0
0.4 0.6 0.8 1.0 1.2 1.4
Clickedpageratio(normalizedbythemax)
Latency (normalized by the mean)
Variation of Clicked Page Ratio Metric

45. 0.02
0.04
0.06
0.08
0.10
0.12
0 250 500 750 100012501500 17502000
0.8
0.9
1.0
1.1
1.2
1.3
Fractionofquerypairs
Click-on-fast/Click-on-slow
Latency difference (in milliseconds)
Click-on-fast
Click-on-slow
Ratio
Eliminating the Effect of Content
§ q1 = q2 & SERP1 = SERP2
§ 500ms of latency difference is
the critical point beyond
which users are more likely to
click on a result retrieved with
lower latency

46. Eliminating the Effect of Content
0.02
0.04
0.06
0.08
0.10
0.12
0 250 500 750 100012501500 17502000
0.8
0.9
1.0
1.1
1.2
1.3
Fractionofquerypairs
Click-more-on-fast/Click-more-on-slow
Latency difference (in milliseconds)
Click-more-on-fast
Click-more-on-slow
Ratio
§ q1 = q2 & SERP1 = SERP2
§ Clicking on more results
becomes preferable to
submitting new queries when
the latency difference exceeds
a certain threshold (1250ms)

47. Conclusions

48. Conclusions
§ Up to a point (500ms) added response time delays are not
noticeable by the users
§ After a certain threshold (1000ms) the users can feel the added
delay with very high likelihood
§ Perception of search latency varies considerably across the
population!

49. Conclusions
§ The tendency to overestimate or underestimate system
performance biases users’ interpretations of search interactions
and system usability
§ Participants of the SE_fast:
• were generally more deeply engaged
• their usability perception was more tolerant to delays

50. Conclusions
§ Given two content-wise identical result pages, users are more
likely to click on the result page that is served with lower latency
§ 500ms of latency difference is the critical point beyond which
users are more likely to click on a result retrieved with lower
latency
§ Clicking on more results becomes preferable to submitting new
queries when the latency difference exceeds a certain threshold
(1250ms)

51. Thank you for your attention!
arapakis@yahoo-inc.com
iarapakis