Mobile Video Delivery via Human Movement
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Mobile Video Deliveryvia Human Movement
Gene Moo Lee, Swati Rallapalli, Wei Dong, Yi-Chao Chen, Lili Qiu, Yin Zhang
University of Texas at Austin
SECON 2013, New Orleans LA
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Introduction● Mobile devices have great local connectivities
○ WiFi, NFC, Bluetooth● People bring mobile devices wherever they go
○ Home, workplace, restaurants, subways, bus stops
Can we leverage human movements as a content distribution channel?
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0.65 Mbps (in our LBSN data)
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VideoFountain● VideoFountain is a mobile content distribution system
○ To deploy kiosks (WiFi AP w/ storages) at popular venues○ To let mobile users download/upload contents○ To leverage mobile users to deliver contents between kiosks
● Challenges○ To understand real user mobility○ To bootstrap mobile contents○ To route contents○ To incentivize users○ To protect copyrights○ To preserve integrity of contents○ so on...
source: http://www.redbox.com
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Roadmap
1. User mobility analysisa. Location-based social networksb. Various mobility properties
2. Content distribution via human movementa. Initial content placementb. Routing in VideoFountain
3. Feasibility study with trace-driven simulationsa. Impacts of algorithmsb. Impacts of various factors
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Mobility analysis with LBSN● LBSN: Location-Based Social Networks
○ Mobile users check-in to places where they visit○ Foursquare, Gowalla, Facebook, Google+
● Why LBSN?○ Mobile devices are the targets of VideoFountain○ Dwell time is long enough to do manual check-in○ Massive data to understand human movement
● Limitations○ No check-out time, missing check-ins
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Venue popularity
● Popularity of venues (# of check-ins) follows Zipf-like distribution○ Significant value of placing content
at popular venues
● Venue popularity is stable over time○ We can learn from historical data
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Number of check-ins
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Inter-venue human traffic● Aggregated human traffic between venues
○ T(a,b,t) = # users moving from venue a to venue b in day t■ user u check-ins at a then b
● Human traffic is stable over time○ In two week data, T(a,b,t) doesn't change for 80-90% pairs
● Aggregated human traffic exhibits Zipf-like distribution
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Number of human traffic
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Degree of separation
● How well connected are the venues in a city?○ 80-90% venue pairs are within 2-3 hops○ Routing is relatively easy
● Methodology
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Number of hops
Frac
tion
of v
enue
pai
rs
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Individual mobility prediction
● Given a user is at venue X, can we predict the next check-in venue?○ Somewhat. ○ Paris (55%), Manhattan (41%), Austin (26%)
● Use a very simple algorithm to predict user's next-checkin based on personal history plus movements from general population○ See paper for algorithm details
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Roadmap
1. User mobility analysisa. Location-based social networksb. Various mobility properties
2. Content distribution via human movementa. Initial content placementb. Routing in VideoFountain
3. Feasibility study with trace-driven simulationsa. Impacts of algorithmsb. Impacts of various factors
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Initial content placement
● Given the set of destinations to deliver contents, where do we seed the contents?
● Placement algorithms○ Popularity-based placement
■ Place the contents from the most popular venues○ Utility-based placement
■ Place the contents from the venues that maximize our utility functions● Ex. Minimize the distances to the destinations
■ Use greedy algorithm○ Random placement
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Routing algorithms
How to route initial contents to destination venues?
● Utility-based replication○ When a user visits a venue, calculate the marginal utilities of
uploading (user->venue) or downloading (venue->user) contents
○ Execute download/upload with highest marginal utility
○ Marginal utility: an example with geographic distance■ How much the content is getting closer to the final
destination with this download/upload?
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Utility functions
Let Sc be the set of venues that currently have the content cLet dc be the destination venue of content c
● Expected delay○ What is the expected delay from Sc to dc ?
● Geographic distance○ How far is the Sc to dc ?
● Single-hop traffic○ How many people move from Sc to dc in a day?
● Multi-hop traffic○ Consider multi-hop traffic with decay factor
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Benchmark algorithms
● Flooding○ Whenever a user visits a venue, copy all the contents
● Epidemic routing○ Flooding with hop count limit (2 in the experiments)
● Oracle routing (upperbound)○ Assume that we know the users' next check-ins○ Construct spatio-temporal graph○ Run LP to optimize the throughput achievable in the graph
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Roadmap
1. User mobility analysisa. Location-based social networksb. Various mobility properties
2. Content distribution via human movementa. Initial content placementb. Routing in VideoFountain
3. Feasibility study with trace-driven simulationsa. Impacts of algorithmsb. Impacts of various factors
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Trace-driven simulation
● Foursquare traces○ Train: January 2nd 2012 to January 16th 2012 (2 weeks)○ Test: January 17th 2012 to January 30th 2012 (2 weeks)
● Default settings○ Wireless capacity: 50 Mbps (802.11n)○ Storage: 10 GB (mobile), 1 TB (venue)○ Dwell time: exponential distribution with mean 60 mins○ 50 flows generation (1GB each)
■ Sources: 1% or 5% venues (by placement algorithms)■ Destinations: up to 10% random venues (non-source)
● Evaluation metrics○ Flow delivery rate: how many flows completely delivered○ Traffic delivery rate: consider partial delivery
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Impact of initial placement
● Utility based placement outperforms others○ Random 25%, popular 67%, utility 74%
● Utility based routing outperforms others○ Different utility functions work for different cities
● Utility routing vs Oracle routing○ Paris: 69% vs 81%○ London: 69% vs 84%
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Random placement Utility placement
Flow
del
iver
y (%
)
Flow
del
iver
y (%
)
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Impact of contents
● Results from London○ Consistent in other cities
● Number of flows○ Increasing congestion○ Utility routing degrades
gracefully○ Flooding/Epidemic suffer
● Content sizes○ The system can support 500+
MB contents, which can cover most mobile contents
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Impact of device specs
● Wireless capacity○ The higher bandwidth, the better
system performs○ 50 Mbps is enough
● User storage○ The larger storage, the better
system performs○ 10GB is enough
● Replacement strategy○ Utility-based outperforms others
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Impact of user behaviors
● User dwell time○ Avg 30 mins is enough
● Comparing Oracle vs Prediction○ Difference within 18%~38%
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Conclusion
● Collected and analyzed LBSN data (Foursquare, Gowalla)○ Venue popularity and human traffic exhibit Zipf-like distribution○ Cities are well-connected by human movements
● Proposed VideoFountain○ Mobile content distribution system leveraging human movements○ Designed placement & routing algorithms
● Evaluated the system with trace-driven simulations○ Utility-based placement & routing work well the best
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Thank you!
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Link capacity between venues
● Does inter-venue human movement link make considerable bandwidth?○ Yes, it is.
● A simple back-of-the-envelope calculation:○ Assume WiFi bandwidth L (50 Mbps)○ If user u stays at venue a for ta then venue b for tb,
■ then the user can carry min(ta, tb) X L / 2■ assuming she downloads and uploads equally
○ Latency T: inter-checkin time from a to b
● Avg capacity of all inter-venue movements = 0.65 Mbps○ Avg global internet speed = 2.9 Mbps○ Avg US internet speed = 7.4 Mbps○ source : https://www.cabletechtalk.com/broadband-internet/u-s-moves-up-in-average-worldwide-internet-speed-
rankings/
Back to main slides
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Spatio-Temporal Graph● Construct spatio-temporal graph
○ Two venues are linked if any user checked two venues in that day
○ optimistic vs conservative
Back to main slides
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Venue popularity● Popularity of venues (# of
checkins) follows Zipf-like distribution○ Richer gets richer○ Significant value of placing
content at popular venues
● Venue popularity is stable over time○ We can learn it from historical
data
● Popular venues are spread across the city○ pair-wise distances
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Benchmark algorithms
● Flooding○ Whenever a user visits a venue, copy all the contents
● Epidemic routing○ Flooding with hop count limit (2 in the experiments)
● Oracle routing (upperbound)○ Assume that we know the users' next checkins○ Construct spatio-temporal graph○ Run LP to optimize the throughput achievable in the graph
14/21
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Impact of initial placement
● Seeds: 1% and 5% venues
● 1% seed results:○ Random placement: 23%○ Popular placement: 77%○ Utility placement: 64%
● 5% seed results:○ Random placement: 25%○ Popular placement: 67%○ Utility placement: 74%
● Utility routing vs Oracle routing○ Paris: 69% vs 81%○ London: 69% vs 84%
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Introduction● People bring mobile devices wherever they go
○ Home, workplace, restaurants, subways, bus stops● Mobile devices have great local connectivities
○ WiFi, NFC, Bluetooth
Can we leverage human movements as a content distribution channel?
source: http://www.apple.com/ios/ios7/features/#airdrop
source: http://www.androidcentral.com/hands-s-beam-samsung-galaxy-s-iii
2/21