Abstract
Topological self-stabilization is an important concept to build robust open distributed systems (such as peer-to-peer systems) where nodes can organize themselves into meaningful network topologies. The goal is to devise distributed algorithms that converge quickly to such a desirable topology, independently of the initial network state. This paper proposes a new model to study the parallel convergence time. Our model sheds light on the achievable parallelism by avoiding bottlenecks of existing models that can yield a distorted picture. As a case study, we consider local graph linearization—i.e., how to build a sorted list of the nodes of a connected graph in a distributed and self-stabilizing manner. We propose two variants of a simple algorithm, and provide an extensive formal analysis of their worst-case and best-case parallel time complexities, as well as their performance under a greedy selection of the actions to be executed.
For a complete technical report, we refer the reader to [8]. Research supported by the DFG project SCHE 1592/1-1, and NSF Award number CCF-0830704.
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Gall, D., Jacob, R., Richa, A., Scheideler, C., Schmid, S., Täubig, H. (2010). Time Complexity of Distributed Topological Self-stabilization: The Case of Graph Linearization. In: López-Ortiz, A. (eds) LATIN 2010: Theoretical Informatics. LATIN 2010. Lecture Notes in Computer Science, vol 6034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12200-2_27
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DOI: https://doi.org/10.1007/978-3-642-12200-2_27
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