Brief Announcement: Practical Summation via Gossip

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Brief Announcement: Practical Summation via Gossip. Wesley W. Terpstra, Christof Leng, Alejandro P. Buchmann Databases and Distributed Systems Group Technische Universität Darmstadt Germany. Sum calculation in peer-to-peer. Input: every peer has a value Output: (at least) one peer knows - PowerPoint PPT Presentation

Transcript of Brief Announcement: Practical Summation via Gossip

www.dvs1.informatik.tu-darmstadt.de

Brief Announcement:Practical Summation via Gossip

Wesley W. Terpstra, Christof Leng, Alejandro P. Buchmann

Databases and Distributed Systems Group

Technische Universität Darmstadt

Germany

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Sum calculation in peer-to-peer

Input: every peer has a value

Output: (at least) one peer knows

Useful in computing many global statistics: Network size Average utilization Load balance (standard deviation) Churn (rate of peer replacement) Size of stored data

For our system, BubbleStorm, we compute degi(p)

x pp∈P

x p

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Build on an existing solution

Approaches can be compared by Message rounds (latency) Total messages (bandwidth) Parameters: system size (n), accuracy ()

We improve the Push-Sum algorithm for practical use

Rounds Messages

Push-Sum (2003, FOCS)

Sample&Collide (2006)

Random Tour (2006)

Comp&Spread (2006)

logn + log1

ε

n logn + log1

ε

⎝ ⎜

⎠ ⎟

logn +1

εn

1

εn logn

n +1

ε 2

1

ε 2n

1

ε 2log2 n

1

ε 2 n log2 n

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Analogy: Measuring a lake’s volume

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Push-Sum visualized

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Stationary Distribution (Steady State)

Perturbations of equilibrium do not affect water/fish ratio

Equilibrium: edges carry the same water and fish in both directions peers have water and fish proportional to degree and clock

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Improvement: Big Fish eat smaller fish

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Fish eating in the Network

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Stationary Distribution (Steady State)

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Other improvements

Round switching Once the result is accurate “enough”, restart Provides a running estimate on network statistics

Compensate for message loss

Prevent adding two of the most aggressive fish

Save bandwidth for multiple measurements

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Synchrony

Kempe et al. prove correctness with synchronous model, but conjecture that it works asynchronously We validate this claim by simulation

1 million peers, 5s gossip interval, find network size:

0

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27:00 29:00 31:00 33:00 35:00

Logarithmic size estimate

Time (mm:ss)

MaximumStd dev.

Minimum

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Open Problem

Push-Sum is very vulnerable to attack Any peer can completely change the result This is largely due to the problem statement (sum!)

Simplistic prevention (bounds) easily defeated Introduce too few of the largest fish type too large Switch rounds prematurely too small & unstable

What is a useful adversary model for summation?

www.dvs1.informatik.tu-darmstadt.de

?Questions

Thanks for listening!