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Dynamic Networks of Finite State Machines

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Structural Information and Communication Complexity (SIROCCO 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9988))

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Abstract

Like distributed systems, biological multicellular processes are subject to dynamic changes and a biological system will not pass the survival-of-the-fittest test unless it exhibits certain features that enable fast recovery from these changes. In most cases, the types of dynamic changes a biological process may experience and its desired recovery features differ from those traditionally studied in the distributed computing literature. In particular, a question seldomly asked in the context of distributed digital systems and that is crucial in the context of biological cellular networks, is whether the system can keep the changing components confined so that only nodes in their vicinity may be affected by the changes, but nodes sufficiently far away from any changing component remain unaffected.

Based on this notion of confinement, we propose a new metric for measuring the dynamic changes recovery performance in distributed network algorithms operating under the Stone Age model (Emek and Wattenhofer, PODC 2013), where the class of dynamic topology changes we consider includes inserting/deleting an edge, deleting a node together with its incident edges, and inserting a new isolated node. Our main technical contribution is a distributed algorithm for maximal independent set (MIS) in synchronous networks subject to these topology changes that performs well in terms of the aforementioned new metric. Specifically, our algorithm guarantees that nodes which do not experience a topology change in their immediate vicinity are not affected and that all surviving nodes (including the affected ones) perform \(\mathcal {O}((C + 1) \log ^{2} n)\) computationally-meaningful steps, where C is the number of topology changes; in other words, each surviving node performs \(\mathcal {O}(\log ^{2} n)\) steps when amortized over the number of topology changes. This is accompanied by a simple example demonstrating that the linear dependency on C cannot be avoided.

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Notes

  1. 1.

    Throughout, we use w.p. to abbreviate “with probability” and w.h.p. to abbreviate “with high probability”, i.e., with probability \(n^{-c}\) for any constant c.

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Author information

Authors and Affiliations

  1. Technion, Haifa, Israel

    Yuval Emek

  2. Comerge AG, Zürich, Switzerland

    Jara Uitto

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  1. Yuval Emek
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  2. Jara Uitto
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Correspondence to Jara Uitto .

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Editors and Affiliations

  1. Aalto University, Espoo, Finland

    Jukka Suomela

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Emek, Y., Uitto, J. (2016). Dynamic Networks of Finite State Machines. In: Suomela, J. (eds) Structural Information and Communication Complexity. SIROCCO 2016. Lecture Notes in Computer Science(), vol 9988. Springer, Cham. https://doi.org/10.1007/978-3-319-48314-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-48314-6_2

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