1. Designing a new BitTorrent Client
Improving BitTorrent
Designing a new BitTorrent Client
11/16/2018
Dimitri DeFigueiredo – Computer Science

2. Dimitri DeFigueiredo – Computer Science
Overview
How BitTorrent Works
Experimental Analyses
Modeling: Gittins’ Indices
Our New Algorithms
Proposed Evaluation
HELP!
11/16/2018
Dimitri DeFigueiredo – Computer Science

3. Dimitri DeFigueiredo – Computer Science
File Organization
File 1 2 3 4
Piece 256KB
Block 16KB
Incomplete Piece
11/16/2018
Dimitri DeFigueiredo – Computer Science

4. Dimitri DeFigueiredo – Computer Science
Initialization
HTTP GET MYFILE.torrent
S3F5YHG6FEB
FG5467HGF367
F456JI9N5FF4E …
MYFILE.torrent
webserver
“register”
ID :6881
ID :5692
ID :4545 …
ID :6882
list of peers
user
tracker
Peer 40 …
Peer 1
Peer 2
11/16/2018
Dimitri DeFigueiredo – Computer Science

5. Dimitri DeFigueiredo – Computer Science
“On the Wire” Protocol
(Over TCP)
Non-keepalive messages:
0 – choke
1 – unchoke
2 – interested
3 – not interested
4 – have
5 – bitfield
6 – request
7 – piece
8 – cancel 1
ID/Infohash Handshake
BitField
BitField
Remote Peer
Local Peer
Interested = 0 choked = 1
Interested = 0 choked = 1
11/16/2018
Dimitri DeFigueiredo – Computer Science

6. Dimitri DeFigueiredo – Computer Science
Piece Selection
Pipelining (5 requests)
Strict Priority
3 stages:
Random first piece
Rarest First
Endgame mode
11/16/2018
Dimitri DeFigueiredo – Computer Science

7. Dimitri DeFigueiredo – Computer Science
Peer Selection
Focus on Rate
Upload to at most 4 peers
Random Unchoke
Global rate cap only
11/16/2018
Dimitri DeFigueiredo – Computer Science

8. Dimitri DeFigueiredo – Computer Science
Analyses
N. Liogkas et al., “Exploiting BitTorrent For Fun (But Not Profit)”, IPTPS 2006.
A. Legout et al., “Rarest First and Choke Algorithms Are Enough”, June 2006
A. R. Bharambe et al. “Analyzing and Improving BitTorrent Performance”, Feb 2005.
11/16/2018
Dimitri DeFigueiredo – Computer Science

9. Exploitation – Liogkas et al.
PlanetLab & Real Torrent
Exploits:
Download only from seeds
Median improvement of 7-20%
Download only from fastest peers
Contradictory results:
good in PlanetLab (+22%)
bad in the wild(-1 to -30%)
Advertise false pieces
22% better download rates
11/16/2018
Dimitri DeFigueiredo – Computer Science

10. Piece Selection – Legout et al.
11/16/2018
Dimitri DeFigueiredo – Computer Science

11. Piece Selection – Legout et al.
a = local peer interested in remote peer
b = time peers are connected
c = remote peer interested in local peer
11/16/2018
Dimitri DeFigueiredo – Computer Science

12. Piece Selection – Legout et al.
Limitations:
In 25% of the torrents the local peer is not interested.
Who is interested in me?
11/16/2018
Dimitri DeFigueiredo – Computer Science

13. Dimitri DeFigueiredo – Computer Science
Gittins Indices
The Two-armed bandit problem
Same prize
Unknown winning probabilities
Known discount factor
Limitation:
Infinitely discounted or fixed horizon
Projects remain dormant
One project at a time
P. Whittle 1988
Restless bandits
m projects “on average”
Asymptotically optimal for large m and n
11/16/2018
Dimitri DeFigueiredo – Computer Science

14. Dimitri DeFigueiredo – Computer Science
Piece Selection
Key ideas:
We don’t care about piece selection
Keep download pipes full
Peer Queues
Assume:
We receive pieces at a rate of 1 per second
11/16/2018
Dimitri DeFigueiredo – Computer Science

15. Dimitri DeFigueiredo – Computer Science
Piece Selection 5 6 7 1 2 3 4 – 1 2 3 4 5 6 7
Remote Peer
Local Peer 1 2 3 4 5 6 7 1 3 6 5 6 7 1 2 3 4
11/16/2018
Dimitri DeFigueiredo – Computer Science

16. Dimitri DeFigueiredo – Computer Science
Piece Selection 3 1 Q1
Peer 1 1 3 6 Q2
Remote Peer
Peer 2
Local Peer 4 Q3
Peer 3
11/16/2018
Dimitri DeFigueiredo – Computer Science

17. Dimitri DeFigueiredo – Computer Science
Piece Selection
Cost of empty queue = Ci per unit time
Discount factor d <1
Piece consumption rate li (Poison Process)
Queue has length li(t)
11/16/2018
Dimitri DeFigueiredo – Computer Science

18. Dimitri DeFigueiredo – Computer Science
Piece Selection
Minimize:
where
We can Solve this with Gittins’ Indices!
11/16/2018
Dimitri DeFigueiredo – Computer Science

19. Piece Selection Algorithm (alpha version)
Calculate Gittins’ Index for each queue:
Feed the queue with the highest index.
Two Problems:
1 piece goes into many queues
it takes a while to get the pieces
11/16/2018
Dimitri DeFigueiredo – Computer Science

20. Piece Selection Algorithm (beta version)
Calculate Gittins’ Index for each queue:
Calculate Gittins’ index for each piece.
n(piece) = SA
Get piece with highest index possible.
11/16/2018
Dimitri DeFigueiredo – Computer Science

21. Piece Selection Algorithm
highest
index 7
Peer 1 7 5 2
Peer 2 2
lowest
index 5
Peer 3
time
11/16/2018
Dimitri DeFigueiredo – Computer Science

22. Piece Selection Algorithm (Final Release)
Calculate Gittins’ Index for each queue.
Calculate “Gittins’ index” for each piece.
Extrapolate piece download finishing time.
Order finishing times (earliest first).
Order pieces (highest index first).
Match best piece with earliest finishing time.
11/16/2018
Dimitri DeFigueiredo – Computer Science

23. Dimitri DeFigueiredo – Computer Science
Peer Selection
Key Ideas:
upload rate is a perishable good
Throttle each connection individually
Do not limit number of downloaders to 4
11/16/2018
Dimitri DeFigueiredo – Computer Science

24. Peer Selection – Our Model
Series of rounds
E[download rate] = f(upload rate)
Estimate download rates with “have”.
11/16/2018
Dimitri DeFigueiredo – Computer Science

25. Peer Selection Algorithm
Keep clients with best ratios.
Throttle to estimated safe bound.
Solve Knapsack problem:
Total upload bandwidth = max weight
Item value = download rate we are getting
Item weight = estimated upload bound
If download/upload rate ratio is < 1
Random unchoke
11/16/2018
Dimitri DeFigueiredo – Computer Science

26. Dimitri DeFigueiredo – Computer Science
Evaluation
Simultaneously
Identical machines and network.
Repeat experiment and swap clients.
Different torrents
vs.
11/16/2018
Dimitri DeFigueiredo – Computer Science

27. Dimitri DeFigueiredo – Computer Science
HELP!
What we need:
An awesome python programmer ;0)
What’s in it for you:
We are cool! ;0)
Publications
Open Source Real Estate…
11/16/2018
Dimitri DeFigueiredo – Computer Science

28. Dimitri DeFigueiredo – Computer Science
Thank you!
11/16/2018
Dimitri DeFigueiredo – Computer Science