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Introduction Mining Second Life Measurement Methodology Results Conclusion
Introduction Mining Second Life Measurement Methodology Results Conclusion
Mining Second Life: Characterizing User Mobility in a Popular - - PowerPoint PPT Presentation
Mining Second Life: Characterizing User Mobility in a Popular - - PowerPoint PPT Presentation
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 2008 Seattle, WA, U.S. Introduction Mining
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Introduction Mining Second Life Measurement Methodology Results Conclusion
Characterizing human mobility: Objectives of this work: Define a novel methodology to carry out experiments on human mobility with the following goals: Affordable experiments No logistic organization Wireless technology independent Scalability of experiments
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Introduction Mining Second Life Measurement Methodology Results Conclusion
Related works Objectives of prior works: Build mobility models from traces Performance evaluation of forwarding strategies in DTNs Chaintreau et. al.: IEEE Trans. Mobile Computing 2007 Karagiannis et. al.: ACM Mobicomm 2007 Rhee et. al.: IEEE Infocom 2008
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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
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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
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Introduction Mining Second Life Measurement Methodology Results Conclusion
The idea Exploit Virtual Worlds Networked Virtual Environment are a tremendously popular con- cept of on-line communities: User interaction is synchronous Contrast with Social-Networking applications such as FaceBook: asynchronous interaction In this work we use Second Life and capture user interaction as well as user spatial distribution.
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Introduction Mining Second Life Measurement Methodology Results Conclusion
Our Playground: Second Life Second Life architecture:
Flat, Earth-like world simulated on a large server farm World is divided into 256x256 m “lands”, one server per land → Limitation on number of concurrent users on each land
Each land has attributes:
private public sandbox → Limitations on user-generated content deployment
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Introduction Mining Second Life Measurement Methodology Results Conclusion
Monitoring Architectures Measurements in Second Life can be approached under different angles
Use Second Life to build and deploy monitoring probes Use Second Life to mimic real world experiments System approach: connect to Second Life and get data
We built a lightweight client wich crawls a selected land
Input:
Valid Login/passwd Target Land Measurement granularity Measurement duration
Output:
Anonymized user ID (x, y, z) of every user on the target land every τ seconds
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Introduction Mining Second Life Measurement Methodology Results Conclusion
The Crawler Approach Observations: The crawler is a user → should not introduce bias in experiments One crawler per land is sufficient
All users concurrently connected to the target land can be tracked: we override a method used to build maps Multiple lands can be tracked using an “army” of crawlers
Limitation: maximum number of concurrent users
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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 newbies Island of View: Valentine’s day event
Confined areas:
Dance Island: a virtual discotheque
Note: Selecting lands is a tedious manual exercise Automate the process
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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 we create several snapshots of radio networks Given a communication range r, a link between two users ui, uj exists if their distance d(ui, uj) ≤ r We build snapshots every measurement interval τ = 10 sec r ∈ {rb, rw}, where rb = 10 m (bluetooth) and rw = 80 m (WiFi at 54 Mbps)
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Introduction Mining Second Life Measurement Methodology Results Conclusion
Metrics Temporal: Contact Time Inter Contact Time Spatial: Node degree distribution Network diameter Clustering Coefficient Zone occupation Mobility: Cumlative traveled distance Login time
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Introduction Mining Second Life Measurement Methodology Results Conclusion
Results: Some Numbers 24-hours traces Apfel Land: Unique visitors: 1568 Average concurrent users: 13 Dance Island: Unique visitors: 3347 Average concurrent users: 34 Isle of View: Unique visitors: 2656 Average concurrent users: 65
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Introduction Mining Second Life Measurement Methodology Results Conclusion
Results: Temporal Analysis (1)
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0.1 0.5 1 Time (s) 1−F(x) Contact Time CCDF, r=80m Apfelland Dance Isle Of View
Contact Time = transfer
- pportunities between
users Large values are good
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0.1 0.5 1 Time (s) 1−F(x) Inter−Contact Time CCDF, r=80m Apfelland Dance Isle Of View
Inter Contact Time = time to wait before a pair meets again Large values are supposedly bad
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Introduction Mining Second Life Measurement Methodology Results Conclusion
Results: Trip Characteristics
500 1000 1500 2000 2500 0.1 0.2 0.4 0.5 0.6 0.8 0.9 1 Length (m) F(x) Travel Length CDF Apfelland Dance Isle Of View
Users do not exercise a lot! Closed vs. open spaces
5000 10000 15000 20000 0.1 0.2 0.4 0.5 0.6 0.8 0.9 1 Time (s) F(x) Travel Time CDF Apfelland Dance Isle Of View
Max on-line time ∼ 4 h 90-th perc. on-line < 1 h Our explanation: Quite obvious (and similar to real world): users do not move when they chat!
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Introduction Mining Second Life Measurement Methodology Results Conclusion
Results: Spatial Distribution
5 10 15 20 25 0.8 0.85 0.9 0.95 1 Number of users per cell F(x) Zone Occupation CDF, L=20m Apfelland Dance Isle Of View
Not a uniform distribution Most of the users are grouped Closed vs. open spaces
100 200 100 200 2 4 6 8 X Zone Distribution, Isle Of View, L=20m Y Number of users per cell 100 200 100 200 2 4 6 8 X Zone Distribution, Dance, L=20m Y Number of users per cell
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Introduction Mining Second Life Measurement Methodology Results Conclusion
Concluding remarks Novel approach to study mobility Do real people walk like avatars? Beyond mobility analysis Epidemiology Sociology Virtual playground to test applications
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Introduction Mining Second Life Measurement Methodology Results Conclusion