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Parsimonious flooding in dynamic graphs

Published: 10 August 2009 Publication History

Abstract

An edge-Markovian process with birth-rate p and death-rate q generates sequences of graphs (G0,G1,G2,…) with the same node set [n] such that Gt is obtained from Gt−1 as follows: if eE(Gt−1) then eE(Gt) with probability p, and if eE(Gt−1) then eE(Gt) with probability q. Clementi et al. (PODC 2008) analyzed thoroughly information dissemination in such dynamic graphs, by establishing bounds on their flooding time--flooding is the basic mechanism in which every node becoming aware of an information at step t forwards this information to all its neighbors at all forthcoming steps t∦ > t. In this paper, we establish tight bounds on the complexity of flooding for all possible birth rates and death rates, completing the previous results by Clementi et al. Moreover, we note that despite its many advantages in term of simplicity and robustness, flooding suffers from its high bandwidth consumption. Hence we also show that flooding in dynamic graphs can be implemented in a more parsimonious manner, so that to save bandwidth, yet preserving efficiency in term of simplicity and completion time.
For a positive integer k, we say that the flooding protocol is k-active if each node forwards an information only during the k time steps immediately following the step at which the node receives that information for the first time. We define the reachability threshold for the flooding protocol as the smallest integer k such that, for any source s ∈ [n], the k-active flooding protocol from s completes (i.e., reaches all nodes), and we establish tight bounds for this parameter. We show that, for a large spectrum of parameters p and q, the reachability threshold is by several orders of magnitude smaller than the flooding time. In particular, we show that it is even constant whenever the ratio p/(p + q) exceeds log n/n. Moreover, we also show that being active for a number of steps equal to the reachability threshold (up to a multiplicative constant) allows the flooding protocol to complete in optimal time, i.e., in asymptotically the same number of steps as when being perpetually active. These results demonstrate that flooding can be implemented in a practical and efficient manner in dynamic graphs. The main ingredient in the proofs of our results is a reduction lemma enabling to overcome the time dependencies in edge-Markovian dynamic graphs.

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    cover image ACM Conferences
    PODC '09: Proceedings of the 28th ACM symposium on Principles of distributed computing
    August 2009
    356 pages
    ISBN:9781605583969
    DOI:10.1145/1582716
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    Published: 10 August 2009

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    Author Tags

    1. broadcasting
    2. epidemic protocol
    3. evolving graphs
    4. gossip protocol

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    PODC '09 Paper Acceptance Rate 27 of 110 submissions, 25%;
    Overall Acceptance Rate 740 of 2,477 submissions, 30%

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    • (2019)The cost of global broadcast in dynamic radio networksTheoretical Computer Science10.1016/j.tcs.2019.07.013Online publication date: Jul-2019
    • (2018)Cover Time in Edge-Uniform Stochastically-Evolving GraphsAlgorithms10.3390/a1110014911:10(149)Online publication date: 2-Oct-2018
    • (2018)On the ability of mobile sensor networks to diffuse informationProceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks10.1109/IPSN.2018.00011(37-47)Online publication date: 11-Apr-2018
    • (2017)Cover Time in Edge-Uniform Stochastically-Evolving GraphsStabilization, Safety, and Security of Distributed Systems10.1007/978-3-319-69084-1_33(441-455)Online publication date: 7-Oct-2017
    • (2017)Multi-message Broadcast in Dynamic Radio NetworksAlgorithms for Sensor Systems10.1007/978-3-319-53058-1_1(1-15)Online publication date: 24-Jan-2017
    • (2016)Stability of Spreading Processes over Time-Varying Large-Scale NetworksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2016.25163463:1(44-57)Online publication date: 1-Jan-2016
    • (2016)Information Spreading in Dynamic Networks Under Oblivious AdversariesDistributed Computing10.1007/978-3-662-53426-7_29(399-413)Online publication date: 4-Sep-2016
    • (2016)Rumor spreading in random evolving graphsRandom Structures & Algorithms10.1002/rsa.2058648:2(290-312)Online publication date: 1-Mar-2016
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