1. Measurements, Analysis, and Modeling of BitTorrent-like Systems
Lei Guo1, Songqing Chen2, Zhen Xiao3,
Enhua Tan1, Xiaoning Ding1, and Xiaodong Zhang1
1College of William and Mary
2George Mason University, 3AT & T Labs - Research

2. Basic Model of P2P Systems
Peers sharing different files self-organize into a P2P network
Exchange files they desire
Limitations
Free riding
Large file downloading ♫
Examples: Gnutella, KaZaa, eDonkey/eMule/Overnet

3. BitTorrent: Fast Delivery with Incentive
A large file is divided into chunks
Peers interested in the same file self-organize into a torrent
Peers exchange file chunks with each other
Incentive is established by tit for tat
Very simple and effective, scale fairly well during flash crowd 4 5
Torrent of Bits
...

4. BitTorrent Traffic Online users Traffic volume
6.8 million in August 2004, 9.6 million in August 2005 (BigChampagne)
Traffic volume
53% of all P2P traffic on the Internet in June 2004 (CacheLogic)
P2P traffic: 60-80% Other traffic: 20-30%
Source: CacheLogic, 2004

5. Limited Understanding of BitTorrent
Existing studies on BitTorrent systems (INFOCOM04, SIGCOMM04)
Unrealistic assumptions in system model: no evolution considered
Single-torrent based: more than 85% BT users join multiple torrents
What we are not clear about BitTorrent systems
Service availability
Service stability
Service fairness
Our objective of this work
Evolution of single-torrent system, and limitations of BT
Multi-torrent model for inter-torrent relation and collaboration
during the entire lifetime

6. Outline BitTorrent mechanism and our methodology
Modeling and characterization of single-torrent system
Modeling and characterization of multi-torrent system
Inter-torrent collaboration
Conclusion

7. How BitTorrent Works: Publishing
seed
foo.torrent
announce: tracker URL for bootstrap
creation date: epoch time
of file creation
length: file size
name: file name
piece length: chunk size
pieces: SHA1 hash key
of each chunk
I am here! 3 4 5
...
foo.torrent
Tracker site
Web site
peer list
The publisher
Create a meta file
Publish on a Web site
Start the tracker site
Start a BT client as the initial seed

8. How BitTorrent Works: Downloading
seed
The downloader
Download the meta file
Start a BT client, connect to the tracker site
Get peer list from tracker
Get first chunk from other peers (seeds) 3 4 5
...
foo.torrent
Tracker site
Web site
peer list
peer list
I am here!
download
foo.torrent

9. How BitTorrent Works: Downloading
seed
The downloader
Download the meta file
Start a BT client, connect to the tracker site
Get peer list from tracker
Get first chunk from other peers (seeds)
Exchange file chunk with other peers
Download complete: become a new seed 3 4 5
...
foo.torrent
Tracker site
Web site
peer list
foo.torrent
foo.torrent

10. How BitTorrent Works: Downloading
seed
Future performance
Depends on the arrival and departure of new downloaders and seeds
The downloader
Download the meta file
Start a BT client, connect to the tracker site
Get peer list from tracker
Get first chunk from other peers (seeds)
Exchange file chunk with other peers
Download complete: become a new seed
Initial seed leaves 3 4 5
...
foo.torrent
Tracker site
Web site
peer list
foo.torrent
foo.torrent
seed 3 4 5
...

11. Our Methodology of this Study
Measurement
BitTorrent traffic pattern
Meta file downloading and tracker statistics
Analysis
BitTorrent user behavior and performance limitations
Curve fitting, parameter estimation and validation of mathematical models
Modeling
Torrent evolution and inter-torrent relation
Fluid model, probability model, and graph model

12. Meta File Downloading
The first HTTP packets of .torrent file downloading
Cable network: 3,000+ downloads, 1,000+ torrent meta files
Server farm: 50 tracker sites host hundreds of torrents
Gigasope: fast Internet traffic monitoring tool by AT&T
What information it contains?
Torrent birth time
Peer arrival time to the torrent (packet capture time of downloading)
About 10 days
announce: tracker URL
creation date: epoch time
of file creation
length: file size
name: file name
piece length: chunk size
pieces: SHA1 hash key
of each chunk
foo.torrent

13. Torrent Statistics on Trackers
Professional/dedicated tracker sites
Each may host thousands of torrents at the same time
and collected by University of Massachusetts, Amherst
Ex: alluvion -- 1,500 torrents, 550 are fully traced
What information it contains?
Torrents: torrent birth time, file size, number of peers/seeds
Peers: request time, downloading/uploading bytes, downloading/uploading bandwidth
Sampled every 0.5 hour for 48 days

14. Outline BitTorrent mechanism and our methodology
Modeling and characterization of single-torrent system
The evolution of torrent over time
Limitations of current BitTorrent systems
Modeling and characterization of multi-torrent system
Inter-torrent collaboration
Conclusion

15. Torrent Popularity Peer arrivals: decrease with time exponentially
meta file workload
100
101
102
103
CCDF of peer arrival
raw data
linear fit
100
102
104
tracker site workload
raw data
linear fit
relative deviation (%) 10 20 30
individual torrents
torrents ranked by population
(non ascending order)
6% in average
time after torrent birth (day)
Peer arrivals: decrease with time exponentially
Peer arrival rate
derivative of CCDF

16. inter-arrival time > seed service time
Torrent Death
Peer n arrives at time tn :
inter-arrival time:
peer arrival rate:
seed service time:
seed leaving rate:
downloading time:
downloading rate:
When tn  , what will happen?
inter-arrival time > seed service time t
torrent dead
peer n
peer n+1 tn
tn+1

17. Torrent Population and Lifespan
trace
model
100
102
104
torrent population
rank of torrents
trace
model
100
102
104
torrent lifespan (hour)
torrents
Most torrents are small (avg 102)
Most torrents are short live (avg 8 days)

18. Downloading Failure Ratio
download failure
population
10-3
10-1
100
10-2
downloading failure ratio
102
104
torrent population
torrents ranked in non-ascending
order of downloading failure ratio
Define:
Avg downloading failure ratio
about 10%
Different evolution patterns
Small population  large Rfail
Reminder: most torrents have small population!
Altruistic peers make torrents long live

19. Torrent Evolution: Fluid Model
Existing model (SIGCOMM 04)
Constant arrival rate  = const
Torrent reaches equilibrium
The correct model
Exponentially decreasing arrival rate
Torrent dead finally
Verified by our measurements
Two completely different pictures

20. Torrent Evolution: Modeling Results
trace
model
time (hour) 40 80
Flash crowd
Downloader #: exponentially 
Seed #: exponentially 
Peek time
A very short duration
Constant arrival model: flat peak
Attenuation – a long tail
Downloader #: exponentially 
Seed #: exponentially 
Constant arrival model is far from the reality: no attenuation
Torrent death
constant arrival model
# of downloaders
# of seeds
constant arrival model

21. Performance Stability
Evolution over time 2 4 6 8 10
torrents
101
101
103
105
downloader
seed
download speed
Snapshot of torrents at time t
# of peers
model
trace
time (hour)
avg download speed (byte/sec) 5 10 15
104
avg download speed (byte/sec)
Only stable when torrent is large
Fluctuate significantly after peak time
Larger torrents have higher and more table performance

22. Service Unfairness peer contribution ratio Contribution ratio:
ranked peers
102
100
10-2
106
104
download speed (byteps)
peer contribution ratio
+ contribution ratio
–x– download speed
102
100
10-2
101
103
peer contribution ratio
# of torrents
+ contribution ratio
–x– # of torrents
ranked peers
Contribution ratio:
uploaded bytes
downloaded bytes
Unfairness:  download speed,  uploading contribution
Seeds serve high speed downloaders first
Peers not willing to serve after downloading
Not due to new file downloading: selfish

23. Single-torrent Model : Summary
Torrent evolution over time
Exponentially decreasing arrival rate
Flash crowd – short peak – long tailed attenuation
BitTorrent Limitations
Content availability: torrent death
Performance stability
Service fairness

24. Outline BitTorrent mechanism and our methodology
Modeling and characterization of single-torrent system
Modeling and characterization of multi-torrent system
Traffic pattern and user behavior
Graph based model of inter-torrent relation
Inter-torrent collaboration
Conclusion

25. Multi-torrent Environment Dynamics
Torrent birth
Request arrival
Peer birth
CDF of torrents
CDF of requests
CDF of peers
raw data
linear fit
raw data
linear fit
raw data
asymptotic fit
Torrent birth time, request arrival time, and peer birth time (hour)
Considering peers and torrents on the Internet as an open system
Torrent birth rate, torrent request rate, and peer birth rate are constant
Implication:
The lifecycle of a BT peer: downloading, seeding, sleeping, …, dead
avg # of torrents a peer requests
torrent request rate
peer birth rate
= = constant

26. Peer Request Pattern: Request Rate
108
102
Peer request rate:
requests by a peer to different torrents per unit time
104
101
 r (day)
# of torrents
Assume
+  r
–x– # torrents
100
100
Explain 77 years
r  77 years !
peers
Peer request process: seems Poisson-like
Request a new torrent with a probability p: participation probability
Dead with probability 1-p

27. Peer Request Pattern: Participation Probability
––– raw data
––– linear fit 40 20
number of torrents (m)
peer rank (log i)
Probability model
peers request at least m torrents
p =
Another estimation of p
Probability model confirmed

28. Inter-torrent Relation Graph: How Torrents Can Help with Each Other?j i
some peers in torrent i have downloaded j 1 i j 2
some peers in torrent j have downloaded i

29. Inter-torrent Relation Graph: How Torrents Can Help with Each Other?
weighted out-degree
weighted in-degree
torrent size (# of online peers)
trace
model
torr size j i
some peers in torrent i have downloaded j 1 i j 2
some peers in torrent j have downloaded i
Edge weight Wi,j : number of such peers

30. Single-torrent vs. Multi-torrent Model
Single-torrent model
 seed service time,  download failure rate
Limited seed service time , but inter-arrival time  exponentially
Small improvement
Multi-torrent model
Old peers come back multiple times
 peer arrival rate,  peer inter-arrival time
Significant improvement

31. Single-torrent vs. Multi-torrent Model
Single-torrent model
Multi-torrent model
0.1
seeds stay 10 times longer:  * =  /10
torrent death ' (T'life) = 
0.01
110-6 ≈ 0
Inter-torrent collaboration is much more effective than stimulating seeds to serve longer

32. Outline BitTorrent mechanism and our methodology
Modeling and characterization of single-torrent system
Modeling and characterization of multi-torrent system
Inter-torrent collaboration
Tracker site overlay
Instant incentive for collaboration
Conclusion

33. Tracker Site Overlay Self-organized P2P network (a logical structure)B
Neighbor-in
torrents that can serve me B C A
Neighbor-out
torrents that I can serve (peer list) D D C
Self-organized P2P network (a logical structure)
An instance of inter-torrent relation graph
A built-in mechanism for content search, cover 99%+ torrents
Trackerless BitTorrent: uses DHT to store meta file

34. Incentive for Inter-Torrent Collaboration
Jack
file A
file D
Thanks Jack! A C D
Tom
Instant incentive – similar to “tit-for-tat” principle
Neighboring cycle detection
Neighboring cycle construction
Bandwidth trading: get one chunk, serve multiple peers

35. Conclusion
Extensive analysis and modeling to study the behaviors of BT-like systems
Tracker trace and .torrent downloading trace
Mathematical model
BitTorrent system has its limitations due to exponentially decreasing peer arrival rate
Service availability, performance stability, and fairness
Graph based multi-torrent model
System design for inter-torrent collaboration

36. Thank you!

37. Backup for Questions

38. torrent lifespan (hour)
trace
model
100
102
104
torrent lifespan (hour)
torrents
Extract t and t from trace
Get 0 and  using linear regression
Lifespan model verified by measurement

39. (in non-ascending order of modeling results)
Torrent Population
Total population
Model verified by measurement
Observations:
The population of most torrents are small (102 in average)
Downloading failure ratio
Small population  large Rfail
trace
model
100
102
104
torrent population
rank of torrents
(in non-ascending order of modeling results)

40. Torrent Evolution: Fluid Model
Basic equation set
Parameters
x(t)
number of downloaders
y(t)
number of seeds 0
initial peer arrival rate 
attenuation parameter of  
uploading bandwidth c
downloading bandwidth (c >> ) 
seed leaving rate 
file sharing efficiency
1,2
eigen values of the equation set
a,b,c1, c2,d1,d2
constants
Resolution

41. Peer Request Pattern: Summary
Multi-torrent environment: an open model
Torrent birth rate: per hour (nearly a constant)
Peer birth rate: per hour (nearly a constant)
Torrent request rate (for all peers over all torrents): per hour (nearly a constant)
Actually increase slowly according to BigChampagne
Peer request pattern
Lifecycle: downloading, seeding, sleeping, …, next req with prob. p
Peer participation probability: 0.85
Request rate (for different torrents by a peer): Poission-like

42. Tracker Site Overlay Table size Node degree distribution
Similar to unstructured P2P networks
Many content search and msg routing algorithms
Flooding
Random walk …
Trackerless BitTorrent: uses DHT to store meta file

43. Simulation Experiments
without inter-collaboration
with inter-collaboration
content availability
performance stability
service fairness
downloading failure ratio
downloading speed
contribution ratio
Rfail 0
more stable
more balanced
Inter-torrent collaboration can improve BitTorrent performance