Post on 25-Aug-2020
Introduction Mining Second Life Measurement Methodology Results Conclusion
Mining Second Life:Characterizing User Mobility in a Popular
Virtual World
Chi-Anh La - Pietro Michiardi
ACM WOSN 2008Seattle, WA, U.S.
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Introduction Mining Second Life Measurement Methodology Results Conclusion
Outline of the talk
1 Introduction
2 Mining Second Life
3 Measurement Methodology
4 Results
5 Conclusion
Introduction Mining Second Life Measurement Methodology Results Conclusion
Characterizing human mobility:
Objectives of this work:Define a novel methodology to carry out experiments on humanmobility with the following goals:
Affordable experimentsNo logistic organizationWireless technology independentScalability of experiments
Introduction Mining Second Life Measurement Methodology Results Conclusion
Related works
Objectives of prior works:Build mobility models from tracesPerformance evaluation of forwarding strategies in DTNs
Chaintreau et. al.: IEEE Trans. Mobile Computing 2007Karagiannis et. al.: ACM Mobicomm 2007Rhee et. al.: IEEE Infocom 2008
Introduction Mining Second Life Measurement Methodology Results Conclusion
Related works: Experimental Methodology
Select hardware→ exhausting task
Neighbor discovery→ hard for wifi in ad-hoc mode
Prepare / finalize the experiment→ logistic problems
Introduction Mining Second Life Measurement Methodology Results Conclusion
Related works: Restrictions
Available traces are difficult to use (and debug)
Experiments are bound to specific wireless hardware
In general, only “temporal” information is available
GPS-based experiments only for out-door scenarios
Number of participants to experiments is fixed
Introduction Mining Second Life Measurement Methodology Results Conclusion
The idea
Exploit Virtual WorldsNetworked Virtual Environment are a tremendously popular con-cept of on-line communities:
User interaction is synchronousContrast with Social-Networking applications such asFaceBook: asynchronous interaction
In this work we use Second Life and capture user interaction aswell as user spatial distribution.
Introduction Mining Second Life Measurement Methodology Results Conclusion
Our Playground: Second Life
Second Life architecture:Flat, Earth-like world simulated on a large server farmWorld is divided into 256x256 m “lands”, one server perland
→ Limitation on number of concurrent users on each land
Each land has attributes:privatepublicsandbox
→ Limitations on user-generated content deployment
Introduction Mining Second Life Measurement Methodology Results Conclusion
Monitoring Architectures
Measurements in Second Life can be approached underdifferent angles
Use Second Life to build and deploy monitoring probesUse Second Life to mimic real world experimentsSystem approach: connect to Second Life and get data
We built a lightweight client wich crawls a selected land
Input:
Valid Login/passwdTarget LandMeasurement granularityMeasurement duration
Output:Anonymized user ID(x , y , z) of every user on the target land every τ seconds
Introduction Mining Second Life Measurement Methodology Results Conclusion
The Crawler Approach
Observations:The crawler is a user→ should not introduce bias inexperiments
One crawler per land is sufficientAll users concurrently connected to the target land can betracked: we override a method used to build mapsMultiple lands can be tracked using an “army” of crawlers
Limitation: maximum number of concurrent users
Introduction Mining Second Life Measurement Methodology Results Conclusion
Measurement Methodology
We present results for the following lands:Open Spaces:
Apfel Land: a german-speaking arena for newbiesIsland of View: Valentine’s day event
Confined areas:Dance Island: a virtual discotheque
Note:Selecting lands is a tedious manual exerciseAutomate the process
Introduction Mining Second Life Measurement Methodology Results Conclusion
Using SecondLife Traces
How do we use the traces?Using the coordinates of users connected to a target land wecreate several snapshots of radio networks
Given a communication range r , a link between two usersui ,uj exists if their distance d(ui ,uj) ≤ rWe build snapshots every measurement interval τ = 10secr ∈ {rb, rw}, where rb = 10 m (bluetooth) and rw = 80 m(WiFi at 54 Mbps)
Introduction Mining Second Life Measurement Methodology Results Conclusion
Metrics
Temporal:Contact TimeInter Contact Time
Spatial:Node degree distributionNetwork diameterClustering CoefficientZone occupation
Mobility:Cumlative traveled distanceLogin time
Introduction Mining Second Life Measurement Methodology Results Conclusion
Results: Some Numbers
24-hours traces
Apfel Land:Unique visitors: 1568Average concurrent users: 13
Dance Island:Unique visitors: 3347Average concurrent users: 34
Isle of View:Unique visitors: 2656Average concurrent users: 65
Introduction Mining Second Life Measurement Methodology Results Conclusion
Results: Temporal Analysis (1)
101 102 103 104 105
0.1
0.51
Time (s)
1−F(
x)
Contact Time CCDF, r=80m
ApfellandDanceIsle Of View
Contact Time = transferopportunities betweenusersLarge values are good
101 102 103 104 105
0.1
0.51
Time (s)
1−F(
x)
Inter−Contact Time CCDF, r=80m
ApfellandDanceIsle Of View
Inter Contact Time = timeto wait before a pair meetsagainLarge values aresupposedly bad
Introduction Mining Second Life Measurement Methodology Results Conclusion
Results: Trip Characteristics
0 500 1000 1500 2000 250000.10.2
0.40.50.6
0.80.9
1
Length (m)
F(x)
Travel Length CDF
ApfellandDanceIsle Of View
Users do not exercise a lot!Closed vs. open spaces
0 5000 10000 15000 2000000.10.2
0.40.50.6
0.80.9
1
Time (s)
F(x)
Travel Time CDF
ApfellandDanceIsle Of View
Max on-line time ∼ 4 h90-th perc. on-line < 1 h
Our explanation:Quite obvious (and similar to real world): users do not movewhen they chat!
Introduction Mining Second Life Measurement Methodology Results Conclusion
Results: Spatial Distribution
0 5 10 15 20 250.8
0.85
0.9
0.95
1
Number of users per cell
F(x)
Zone Occupation CDF, L=20m
ApfellandDanceIsle Of View
Not a uniform distributionMost of the users aregroupedClosed vs. open spaces
0100
200
0
100
2000
2
4
6
8
X
Zone Distribution, Isle Of View, L=20m
Y
Num
ber o
f use
rs p
er c
ell
0100
200
0
100
2000
2
4
6
8
X
Zone Distribution, Dance, L=20m
Y
Num
ber o
f use
rs p
er c
ell
Introduction Mining Second Life Measurement Methodology Results Conclusion
Concluding remarks
Novel approach to study mobilityDo real people walk like avatars?
Beyond mobility analysisEpidemiologySociologyVirtual playground to test applications
Introduction Mining Second Life Measurement Methodology Results Conclusion
Thank you!
Need traces?Contact: Pietro.Michiardi@eurecom.fr
Web: www.eurecom.fr/∼ michiard