eth zurich – distributed computing group raphael eidenbenz, phd thesis 1eth zurich – distributed...

ETH Zurich – Distributed Computing Group Raphael Eidenbenz, PhD Thesis 1ETH Zurich – Distributed Computing Group

PhD Thesis Raphael Eidenbenz Prof. Roger Wattenhofer, examinerProf. Dov Monderer, co-examiner

Prof. Karl Aberer, co-examiner

Coping with Selfishness in Distributed SystemsMechanism Design in Multi-Core and

Peer-to-Peer Systems

ETH Zurich – Distributed Computing Group Raphael Eidenbenz, PhD Thesis 2

Selfishness in Computer Science

„The Internet is unique among all computer systems in that it is built, operated, and used by a multitude of diverse economic interests, in varying relationships of collaboration and competition with each other.“ C. Papadimitriou, STOC 2001

Game theory attempts to mathematically capture behavior in strategic situations (games), in which an individual's success in making choices depends on the choices of others.


Game Theory & Mechanism Design

• Game Theory explains/predicts behavior

• Mechanism Design Selfishness-aware optimization

ETH Zurich – Distributed Computing Group Raphael Eidenbenz 4ETH Zurich – Distributed Computing Group

Raphael Eidenbenz

Mechanism Design with Payments


Mechanism Design with Payments

silent testify

silent3 0

3 4

testify4 1

0 1

1 2 1 00 0

2 0

+ =

4 2 4 44 1

2 1

cost = 2net gain = 2


Optimal Implementations

20 11 10 10

0 9 10 10

11 20 10 10

9 0 10 1019 10 9 0 10 19 11 20

10 19 0 9

19 10 20 11


20 11 10 10

0+ 9+ 10 10

11 20 10 10

9+ 0+ 10 1019+1 10+1 9+∞ 0+∞

10+1 19+1 11 20

10+1 19+1 0+∞ 9+∞

19+1 10+1 20 11

Optimal Implementations


Complexity

5 0 0 0 0 1 0

0 5 0 0 0 1 0

0 0 5 0 0 1 0

0 0 0 5 0 1 0

0 0 0 0 5 1 0

5 0 0 5 0 0 0

0 5 0 5 0 0 0

0 5 5 0 5 0 0

5 5 5 0 0 0 0

,

, ,

,

, ,

𝒔𝟏

𝒔𝟐

𝒔𝟑

𝒔𝟒

1 2

Optimal exact implementation NP-hard


Worst-Case Leverage

• «maximum worst-case net gain»

𝑋∗max

𝐺

𝑚𝑖𝑛(𝑈−𝑉 ¿)¿

𝑂


Uniform Leverage

• «maximum average net gain»

𝑋∗𝐺

𝑂


Result Overview

Uniform Worst-case

Exact implementation cost NP-hard NP-hard (conjecture)

Implementation cost NP-hard NP-hard (conjecture)

Singleton implementation cost

Polynomial approximation ratio -

(Malicious) exact leverage NP-hard -

(Malicious) leverage NP-hard As hard as cost

Singleton leverage

Polynomial approximation ratio -


Raphael Eidenbenz

Multi-Core Architecture


The Multicore Revolution


Transactional Memory

explicit locks

Which transaction shall I abort??

transactions

Timestamp, Polite, Karma,Polka, Randomized, ...


Transactional Memory is a Game

– Players = programmers– Strategy space = placing of transactions– Their goal: fast execution– Social goal: maximize system throughput

„My thread is the fastest!“


Good Programming

incRingCounters(Node start){ var cur = start; transaction{ repeat{ cur.doSomething(); cur = cur.next; } until(cur==start) }}

incRingCountersGP(Node start){ var cur = start; repeat{ transaction{cur.doSomething();} cur = cur.next; until(cur==start) }}

long transactions vs short transactions

R1

R3

t

R2

Rs

R1

R3

t

R2

Rs


Good Programming Incentives

• A CM is GPI compatible iff it – punishes unnecessary locking – and rewards partitioning.


Quasi Priority Accumulating CM

R1

R3

t

R2

Rs

R1

R3

t

R2

Rs

Thm: Timestamp is not GPI compatible.


• Not priority based– „Choose random winner“

• Proof Intuition– Unnecessary Locks: stupid because only risk conflict (no priority gain)– Partitioning:

Ti2

Randomized CM

Ti

Ti1

Lemma: Randomized CM is GPI compatible.

Ti2

Ti

Ti1


• Not priority based– „Choose random winner“

• Proof Intuition– Unnecessary Locks: stupid because only risk conflict (no priority gain)– Partitioning:

Randomized CM

Lemma: Randomized CM is GPI compatible.

Ti

Ti1 Ti2

Ti

Ti2

throughput collaborators (updates/s)

Karma Polka

Timestampthroughput collaborators (updates/s)


Randomized


thro

ughp

ut s

elfis

h (u

pdat

es/s

)

thro

ughp

ut s

elfis

h (u

pdat

es/s

)

thro

ughp

ut s

elfis

h (u

pdat

es/s

)

thro

ughp

ut s

elfis

h (u

pdat

es/s

)


Raphael Eidenbenz

Peer-to-Peer Computing


Peer-to-Peer File Sharing

• History


𝑆2

𝑆1

𝑝1

𝑆3

𝑝3 𝑝2

𝑝4

𝑝5𝑝6

Cyclic Tit-for-Tat Trading

𝑝5𝑆2

𝑝1

𝑝6𝑆3

𝑝4

𝑝5

𝑆1

𝑝3 𝑝2

𝑝4𝑝1


Cyclic Tit-for-Tat Trading

𝑆2

𝑆1

𝑝1

𝑆3

𝑝3 𝑝2

𝑝4

𝑝5𝑝6


BitTorrent: Downloads per Peer

1 2 3 4 5 6-10 11-20 >20

52.2%

18.0%

9.2%5.4%

3.5%6.9%

3.0% 1.8%

# downloads

# pe

ers

in %


Cross-Swarm Cycles in BitTorrent

2-cycles 3-cycles 4-cycles1E+1

1E+3

1E+5

1E+7

1E+9

1E+11

KATBTJTPB

aver

age

# cy

cles

per

pee

r


Evaluation

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 320

20000

40000

60000

80000

100000

Intra-swarmCycle(2)Cycle(3)Cycle(4)

time (h)

tota

l thr

ough

put

(KB/

s)

10

100

1000Intra-swarm

Cycle(2)

Cycle(3)

Cycle(4)

aver

age

spee

d (K

B/s)


Evaluation

• Results

IS ISas* C2 C2as*

C3 C3as*

C4 C4as*

0

50

100

150

200a

ve

rag

e s

pe

ed

(K

B/s

)


𝑝

Cyclic Trading Protocol CYCT4T

source via dist

…

inTableid

…

outTable

Update:SELECT DISTINCT source, distFROM inTableWHERE distance < k − 1 AND via q;

Out-neighbors on cycles from :SELECT idFROM outTable INNER JOIN inTableON outTable.id = inTable.sourceWHERE via = r;

𝑟 𝑞

𝑘− 2

𝑥


𝑝

Cyclic Trading Protocol CYCT4T

𝑟 𝑞

𝑥

…


Established Equilibrium

utility

n

?

T4T


BitThief: Smooth Transition

utility

n

?


Steganographic Handshake in BitThief

• BitThief is a BitTorrent client that – Free rides with BitTorrent clients [1], and– Trades tit-for-tat (T4T) with other BitThiefs [2]

• Block request sequence• Hybrid approach using PEX

– Order of peer addresses– Forged peer address

BitTorrent

T 4T

[1] Locher et al., Free Riding in Bittorrent is Cheap, HotNets 2006[2] Locher et al., Rescuing Tit-for-Tat with Source Coding, P2P 2007


?

Steganographic Handshake

Π

Π

Π


?

Steganographic Handshake

Π

Π


Steganographic Channels

• P2P File sharing– Block request sequence– Block subset selection

• Timing

• Bandwidth

• Ports


Encoding Bits Into a Permutation

• Encode a message M in a permutation– Represent M as a factorial number– because – M is encoded into as the Lehmer Code of is 2210.

• Lehmer Code– Counts the # swaps to get to – (1,2,3,4) ..2 swaps.. – (3,1,2,4) ..2 swaps..– (3,4,1,2) ..1 swap..– (3,4,2,1) ..0 swaps..– (3,4,2,1)


Hidden Broadcast

• Send a secret message to all conspirers ncn-c


Steganographic Broadcast

• Send a message to all conspirers

• Bittorrent-like p2p file sharing system

k


Efficient Broadcast

8𝑛𝑐

ln (𝑛𝑐 )

If each conspirer randomly connects to peers, then the subnetwork induced by the conspirers is connected w.h.p.𝑐

Lemma


Efficient Broadcast

• Space complexity • Communication complexity w.h.p.

Get peer addressesAcquire blocksReveal types of connected peersBroadcast message in conspirer subnetwork

If each conspirer randomly connects to peers, then the subnetwork induced by the conspirers is connected w.h.p.𝑐

Lemma

Algorithm


Proof of Theorem 10.1

• If each conspirer randomly connects to peers, then the subnetwork induced by the conspirers is connected w.h.p.

• Proof:• For each conspirer , it holds that .• (Chernoff)• • If each edge of a graph G with c nodes is present with probability then G

is connected with probability (Corollary from [Hofstad 2007])

• In such a graph G, all nodes have less than neighbors w.h.p.• Each conspirer implicitly chooses random neighbors in the conspirer

subgraph.


Stronger Authority Models

• Individual Monitoring– Authority monitors individual communication links, no correlation– where m is the # of blocks

• Complete Monitoring– Authority monitors complete network

• Stochastic Monitoring– Trade-off: Hidden communication vs. False positives


Broadcast under Stochastic Monitoring

• Regular peers choose their request order permutation according to a distribution C

• Authority classifies a peer as a conspirer if it uses a request order permutation with

• Trade-off in the choice of threshold

– Amount of hidden communication vs. False positives


Broadcast under Stochastic Monitoring

• Regular peers choose their request order permutation according to a distribution C

• Authority classifies a peer as a conspirer if it uses a request order permutation with

• is a deterministic PRG


Conclusion

t t

Thank You!Questions & Comments?

eth zurich – distributed computing group raphael eidenbenz, phd thesis 1eth zurich – distributed...

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