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        <identifier>oai:drops-oai.dagstuhl.de:2429</identifier>
        <datestamp>2024-03-06T11:08:59Z</datestamp>
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          <dc:title>Stabilizing Consensus with the Power of Two Choices</dc:title>
          <dc:creator>Doerr, Benjamin</dc:creator>
          <dc:creator>Goldberg, Leslie Ann</dc:creator>
          <dc:creator>Minder, Lorenz</dc:creator>
          <dc:creator>Sauerwald, Thomas</dc:creator>
          <dc:creator>Scheideler, Christian</dc:creator>
          <dc:subject>Distributed consensus</dc:subject>
          <dc:description>Consensus problems occur in many contexts and have therefore been intensively studied in the past. In the standard consensus problem there are n processes with possibly different input values and the goal is to eventually reach a point at which all processes commit to exactly one of these values. We are studying a slight variant of the consensus problem called the stabilizing consensus problem. In this problem, we do not require that each process commits to a final value at some point, but that eventually they arrive at a common value without necessarily being aware of that. This should work irrespective of the states in which the processes are starting. Coming up with a self-stabilizing rule is easy without adversarial involvement, but we allow some T-bounded adversary to manipulate any T processes at any time. In this situation, a perfect consensus is impossible to reach, so we only require that there is a time point t and value v so that at any point after t, all but up to O(T) processes agree on v, which we call an almost stable consensus. As we will demonstrate, there is a surprisingly simple rule for the standard message passing model that just needs O(log n loglog n) time for any sqrt{n}-bounded adversary and just O(log n) time without adversarial involvement, with high probability, to reach an (almost) stable consensus from any initial state. A stable consensus is reached, with high probability, in the absence of adversarial involvement.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Benjamin Doerr and Leslie Ann Goldberg and Lorenz Minder and Thomas Sauerwald and Christian Scheideler</dc:contributor>
          <dc:date>2010</dc:date>
          <dc:relation>Is Part Of Dagstuhl Seminar Proceedings, Volume 9371, Algorithmic Methods for Distributed Cooperative Systems (2010)</dc:relation>
          <dc:type>InProceedings</dc:type>
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          <dc:identifier>doi:10.4230/DagSemProc.09371.6</dc:identifier>
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          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09371.6</dc:identifier>
          <dc:language>eng</dc:language>
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