On Reliability of the Extrema Propagation Technique in Random Environment

Authors Jacek Cichoń , Dawid Dworzański , Karol Gotfryd



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Jacek Cichoń
  • Department of Fundamentals of Computer Science, Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Poland
Dawid Dworzański
  • Department of Fundamentals of Computer Science, Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Poland
Karol Gotfryd
  • Department of Fundamentals of Computer Science, Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Poland

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Jacek Cichoń, Dawid Dworzański, and Karol Gotfryd. On Reliability of the Extrema Propagation Technique in Random Environment. In 28th International Conference on Principles of Distributed Systems (OPODIS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 324, pp. 29:1-29:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.OPODIS.2024.29

Abstract

We study the reliability of the following simple mechanism for spreading information in a communication network in the presence of random message loss. Initially, some nodes have information that they want to distribute throughout the network. Each node that has received the information tries to broadcast it to all its neighbors. However, due to interference or communication failures, each transmission between two nodes is broken independently with some fixed probability. 
This transmission mechanism is the basis for the extrema propagation technique, proposed and analyzed in [Carlos Baquero et al., 2012; Baquero et al., 2009; Jacek Cicho{ń} et al., 2012]. Extrema propagation is a simple but powerful method of spreading the extreme values stored by the nodes. In a fully reliable environment, only the number of rounds equal to the network diameter is required for all nodes to be informed. It was shown empirically in [Carlos Baquero et al., 2012] that it also performs well in the presence of link failures and message loss. Extrema propagation is an algorithmic framework that was adopted for designing efficient method for distributed data aggregation, such as estimation of cardinalities, sums, and averages, estimation of data distribution via histograms and random sampling (cf. [Baquero et al., 2009; Karol Gotfryd and Jacek Cichoń, 2023]).
In this paper, we propose a formal network model in which random transmission failures occur and analyze the operation time of the extrema propagation technique for any connected network graph. We provide new general probabilistic upper bounds on the number of rounds needed to spread the extreme values that depend only on the number of nodes, the diameter of the network, and the probability of successful transmission. For some special families of graphs, we also derive specific, more accurate estimates. Our theoretical and experimental results confirm the reliability and efficiency of the extrema propagation technique in faulty or noisy environments.

Subject Classification

ACM Subject Classification
  • Networks → Mobile ad hoc networks
  • Computing methodologies → Distributed algorithms
  • Theory of computation → Random network models
  • Networks → Network reliability
Keywords
  • wireless ad-hoc networks
  • extrema propagation
  • distributed data aggregation
  • fault tolerant aggregation
  • randomly evolving networks

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References

  1. Paulo Sérgio Almeida, Carlos Baquero, Martín Farach-Colton, Paulo Jesus, and Miguel A. Mosteiro. Fault-tolerant aggregation: Flow-Updating meets Mass-Distribution. Distributed Computing, 30(4):281-291, August 2017. URL: https://doi.org/10.1007/s00446-016-0288-5.
  2. Carlos Baquero, Paulo Sérgio Almeida, Raquel Menezes, and Paulo Jesus. Extrema Propagation: Fast Distributed Estimation of Sums and Network Sizes. IEEE Trans. Parallel Distrib. Syst., 23:668-675, 2012. URL: https://doi.org/10.1109/TPDS.2011.209.
  3. Carlos Baquero, Paulo Sérgio Almeida, and Raquel Menezes. Fast Estimation of Aggregates in Unstructured Networks. In Fifth International Conference on Autonomic and Autonomous Systems, ICAS 2009, pages 88-93. IEEE Computer Society, 2009. URL: https://doi.org/10.1109/ICAS.2009.31.
  4. Hervé Baumann, Pierluigi Crescenzi, and Pierre Fraigniaud. Parsimonious flooding in dynamic graphs. In Proceedings of the 28th ACM Symposium on Principles of Distributed Computing, PODC '09, pages 260-269, New York, NY, USA, 2009. Association for Computing Machinery. URL: https://doi.org/10.1145/1582716.1582757.
  5. Miguel Borges, Paulo Jesus, Carlos Baquero, and Paulo Sérgio Almeida. Spectra: Robust Estimation of Distribution Functions in Networks. In Karl Michael Göschka and Seif Haridi, editors, Distributed Applications and Interoperable Systems, Proceedings, DAIS 2012, pages 96-103. Springer, Berlin, Heidelberg, 2012. URL: https://doi.org/10.1007/978-3-642-30823-9_8.
  6. Jorge C. S. Cardoso, Carlos Baquero, and Paulo Sérgio Almeida. Probabilistic Estimation of Network Size and Diameter. In 2009 Fourth Latin-American Symposium on Dependable Computing, LADC '09, pages 33-40. IEEE Computer Society, September 2009. URL: https://doi.org/10.1109/LADC.2009.19.
  7. Arnaud Casteigts, Paola Flocchini, Walter Quattrociocchi, and Nicola Santoro. Time-varying graphs and dynamic networks. International Journal of Parallel, Emergent and Distributed Systems, 27(5):387-408, 2012. URL: https://doi.org/10.1080/17445760.2012.668546.
  8. Arnaud Casteigts, Michael Raskin, Malte Renken, and Viktor Zamaraev. Sharp Thresholds in Random Simple Temporal Graphs. In 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS), pages 319-326, 2022. URL: https://doi.org/10.1109/FOCS52979.2021.00040.
  9. Jacek Cichoń and Karol Gotfryd. Average Counting via Approximate Histograms. ACM Trans. Sen. Netw., 14(2):8:1-8:32, March 2018. URL: https://doi.org/10.1145/3177922.
  10. Jacek Cichoń, Jakub Lemiesz, and Marcin Zawada. On Message Complexity of Extrema Propagation Techniques. In Xiang-Yang Li, Symeon Papavassiliou, and Stefan Rührup, editors, Proceedings of the 11th International Conference on Ad-Hoc, Mobile, and Wireless Networks, volume 7363 of ADHOC-NOW 2012, pages 1-13, Berlin, Heidelberg, 2012. Springer. URL: https://doi.org/10.1007/978-3-642-31638-8_1.
  11. Jacek Cichoń, Maciej Gębala, and Marcin Zawada. Fault tolerant protocol for data collecting in wireless sensor networks. In 2017 IEEE Symposium on Computers and Communications (ISCC), pages 483-486, 2017. URL: https://doi.org/10.1109/ISCC.2017.8024575.
  12. Andrea Clementi, Pierluigi Crescenzi, Carola Doerr, Pierre Fraigniaud, Francesco Pasquale, and Riccardo Silvestri. Rumor spreading in random evolving graphs. Random Struct. Algorithms, 48(2):290-312, March 2016. URL: https://doi.org/10.1002/rsa.20586.
  13. Andrea Clementi, Angelo Monti, Francesco Pasquale, and Riccardo Silvestri. Information Spreading in Stationary Markovian Evolving Graphs. IEEE Trans. Parallel Distributed Syst., 22(9):1425-1432, 2011. URL: https://doi.org/10.1109/TPDS.2011.33.
  14. Andrea E.F. Clementi, Claudio Macci, Angelo Monti, Francesco Pasquale, and Riccardo Silvestri. Flooding Time in edge-Markovian Dynamic Graphs. In Proceedings of the Twenty-Seventh ACM Symposium on Principles of Distributed Computing, PODC '08, pages 213-222, New York, NY, USA, 2008. Association for Computing Machinery. URL: https://doi.org/10.1145/1400751.1400781.
  15. Bennett Eisenberg. On the expectation of the maximum of IID geometric random variables. Statistics & Probability Letters, 78(2):135-143, 2008. URL: https://doi.org/10.1016/j.spl.2007.05.011.
  16. Wilfried Gansterer, Gerhard Niederbrucker, Hana Straková, and Stefan Schulze Grotthoff. Scalable and fault tolerant orthogonalization based on randomized distributed data aggregation. Journal of Computational Science, 4:480-488, November 2013. URL: https://doi.org/10.1016/j.jocs.2013.01.006.
  17. Balázs Gerencsér and Julien M. Hendrickx. Push-Sum With Transmission Failures. IEEE Transactions on Automatic Control, 64(3):1019-1033, 2019. URL: https://doi.org/10.1109/TAC.2018.2836861.
  18. George Giakkoupis, Thomas Sauerwald, and Alexandre Stauffer. Randomized Rumor Spreading in Dynamic Graphs. In Javier Esparza, Pierre Fraigniaud, Thore Husfeldt, and Elias Koutsoupias, editors, 41st International Colloquium on Automata, Languages and Programming (ICALP), pages 495-507. Springer Berlin Heidelberg, 2014. URL: https://doi.org/10.1007/978-3-662-43951-7_42.
  19. Karol Gotfryd and Jacek Cichoń. On distributed data aggregation and the precision of approximate histograms. Journal of Parallel and Distributed Computing, 180:104722, 2023. URL: https://doi.org/10.1016/j.jpdc.2023.104722.
  20. Christoforos N. Hadjicostis and Themistoklis Charalambous. Average Consensus in the Presence of Delays in Directed Graph Topologies. IEEE Transactions on Automatic Control, 59(3):763-768, 2014. URL: https://doi.org/10.1109/TAC.2013.2275669.
  21. Paulo Jesus. Robust Distributed Data Aggregation. LAP LAMBERT Academic Publishing, 2016. Google Scholar
  22. Paulo Jesus, Carlos Baquero, and Paulo Sergio Almeida. A Survey of Distributed Data Aggregation Algorithms. Commun. Surveys Tuts., 17(1):381-404, January 2015. URL: https://doi.org/10.1109/COMST.2014.2354398.
  23. Paulo Jesus, Carlos Baquero, and Paulo Sérgio Almeida. Flow updating: Fault-tolerant aggregation for dynamic networks. Journal of Parallel and Distributed Computing, 78:53-64, 2015. URL: https://doi.org/10.1016/j.jpdc.2015.02.003.
  24. Barry H. Margolin and Herbert S. Winokur, Jr. Exact Moments of the Order Statistics of the Geometric Distribution and their Relation to Inverse Sampling and Reliability of Redundant Systems. Journal of the Americam Statistical Assiociation, 62(319):915-925, September 1997. URL: https://doi.org/10.2307/2283679.
  25. Saptadi Nugroho, Alexander Weinmann, Christian Schindelhauer, and Andreas Christ. Averaging Emulated Time-Series Data Using Approximate Histograms in Peer to Peer Networks. In Highlights in Practical Applications of Agents, Multi-Agent Systems, and Trust-worthiness. The PAAMS Collection, pages 339-346, Cham, 2020. Springer International Publishing. URL: https://doi.org/10.1007/978-3-030-51999-5_28.
  26. Ali Pourmiri and Bernard Mans. Tight Analysis of Asynchronous Rumor Spreading in Dynamic Networks. In Proceedings of the 39th Symposium on Principles of Distributed Computing, PODC '20, pages 263-272, New York, NY, USA, 2020. Association for Computing Machinery. URL: https://doi.org/10.1145/3382734.3405720.
  27. Jan Sacha, Jeff Napper, Corina Stratan, and Guillaume Pierre. Adam2: Reliable Distribution Estimation in Decentralised Environments. In Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems, ICDCS '10, pages 697-707, Washington, DC, USA, 2010. IEEE Computer Society. URL: https://doi.org/10.1109/ICDCS.2010.16.
  28. Wojciech Szpankowski and Vernon Rego. Yet another application of a binomial recurrence order statistics. Computing, 43(4):401-410, 1990. URL: https://doi.org/10.1007/BF02241658.
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