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        <identifier>oai:drops-oai.dagstuhl.de:9830</identifier>
        <datestamp>2024-03-06T10:45:02Z</datestamp>
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          <dc:title>Lattice Agreement in Message Passing Systems</dc:title>
          <dc:creator>Zheng, Xiong</dc:creator>
          <dc:creator>Hu, Changyong</dc:creator>
          <dc:creator>Garg, Vijay K.</dc:creator>
          <dc:subject>Lattice Agreement</dc:subject>
          <dc:subject>Replicated State Machine</dc:subject>
          <dc:subject>Consensus</dc:subject>
          <dc:description>This paper studies the lattice agreement problem and the generalized lattice agreement problem in distributed message passing systems. In the lattice agreement problem, given input values from a lattice, processes have to non-trivially decide output values that lie on a chain. We consider the lattice agreement problem in both synchronous and asynchronous systems. For synchronous lattice agreement, we present two algorithms which run in log(f) and min{O(log^2 h(L)), O(log^2 f)} rounds, respectively, where h(L) denotes the height of the input sublattice L, f &lt; n is the number of crash failures the system can tolerate, and n is the number of processes in the system. These algorithms have significant better round complexity than previously known algorithms. The algorithm by Attiya et al. [Attiya et al. DISC, 1995] takes log(n) synchronous rounds, and the algorithm by Mavronicolasa [Mavronicolasa, 2018] takes min{O(h(L)), O(sqrt(f))} rounds. For asynchronous lattice agreement, we propose an algorithm which has time complexity of 2*min{h(L), f + 1} message delays which improves on the previously known time complexity of O(n) message delays.
The generalized lattice agreement problem defined by Faleiro et al in [Faleiro et al. PODC, 2012] is a generalization of the lattice agreement problem where it is applied for the replicated state machine. We propose an algorithm which guarantees liveness when a majority of the processes are correct in asynchronous systems. Our algorithm requires min{O(h(L)), O(f)} units of time in the worst case which is better than O(n) units of time required by the algorithm in [Faleiro et al. PODC, 2012].</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Xiong Zheng and Changyong Hu and Vijay K. Garg</dc:contributor>
          <dc:date>2018</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 121, 32nd International Symposium on Distributed Computing (DISC 2018)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.DISC.2018.41</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-98301</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2018.41</dc:identifier>
          <dc:language>eng</dc:language>
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