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Message-Efficient Self-stabilizing Transformer Using Snap-Stabilizing Quiescence Detection

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Structural Information and Communication Complexity (SIROCCO 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11085))

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Abstract

By presenting a message-efficient snap-stabilizing quiescence detection algorithm, we also facilitate a transformer that converts non self-stabilizing algorithms into self-stabilizing ones. We propose a message-efficient snap-stabilizing ongoing quiescence detection algorithm. (Notice that by definition it is also self-stabilizing and can detect termination.) This algorithm works for diffusing computations. We are not aware of any other self-stabilizing or snap-stabilizing ongoing quiescence or termination detection algorithm.

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Notes

  1. 1.

    Snap-stabilization [8] is a variant of self-stabilization that ensures immediate recovery after transient faults. Notice that a snap-stabilizing algorithm is also self-stabilizing.

  2. 2.

    In a diffusing computation, a unique process, the initiator, can spontaneously send a message to one or more of its neighbors and only once [12]. After receiving their first message, the other processes can freely send messages to their neighbors.

  3. 3.

    In a k-synchronous execution, the difference of speed between any two processes is at most k.

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Acknowledgement

This research was carried with a partial support of the Israel Ministry of Science and Technology.

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Authors and Affiliations

  1. Technion - Israel Institute of Technology, Haifa, Israel

    Anaïs Durand & Shay Kutten

Authors
  1. Anaïs Durand
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  2. Shay Kutten
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Correspondence to Anaïs Durand .

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Editors and Affiliations

  1. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Zvi Lotker

  2. Tel Aviv University, Tel Aviv, Israel

    Boaz Patt-Shamir

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Durand, A., Kutten, S. (2018). Message-Efficient Self-stabilizing Transformer Using Snap-Stabilizing Quiescence Detection. In: Lotker, Z., Patt-Shamir, B. (eds) Structural Information and Communication Complexity. SIROCCO 2018. Lecture Notes in Computer Science(), vol 11085. Springer, Cham. https://doi.org/10.1007/978-3-030-01325-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-01325-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01324-0

  • Online ISBN: 978-3-030-01325-7

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