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https://doi.org/10.1109/sfcs.2...
Article . 2002 . Peer-reviewed
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Conference object . 2024
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Conference object . 2000
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Randomized rumor spreading

Authors: Richard M. Karp; Christian Schindelhauer; Scott Shenker; Berthold Vöcking;

Randomized rumor spreading

Abstract

Investigates the class of epidemic algorithms that are commonly used for the lazy transmission of updates to distributed copies of a database. These algorithms use a simple randomized communication mechanism to ensure robustness. Suppose n players communicate in parallel rounds in each of which every player calls a randomly selected communication partner. In every round, players can generate rumors (updates) that are to be distributed among all players. Whenever communication is established between two players, each one must decide which of the rumors to transmit. The major problem is that players might not know which rumors their partners have already received. For example, a standard algorithm forwarding each rumor form the calling to the called players for /spl Theta/(ln n) rounds needs to transmit the rumor /spl Theta/(n ln n) times in order to ensure that every player finally receives the rumor with high probability. We investigate whether such a large communication overhead is inherent to epidemic algorithms. On the positive side, we show that the communication overhead can be reduced significantly. We give an algorithm using only O(n ln ln n) transmissions and O(ln n) rounds. In addition, we prove the robustness of this algorithm. On the negative side, we show that any address-oblivious algorithm needs to send /spl Omega/(n ln ln n) messages for each rumor, regardless of the number of rounds. Furthermore, we give a general lower bound showing that time and communication optimality cannot be achieved simultaneously using random phone calls, i.e. every algorithm that distributes a rumor in O(ln n) rounds needs /spl omega/(n) transmissions.

Country
Germany
Keywords

004

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    popularity
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
275
Top 1%
Top 0.1%
Top 10%