Ultra-Fast Asynchronous Randomized Rumor Spreading (Brief Announcement)

Pourmiri, Ali; Ramezani, Fahimeh

doi:10.1145/3323165.3323167

Cited by 6 publications

(10 citation statements)

References 13 publications

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“…In large scale and open networks, assuming that all nodes act in synchrony is a very strong assumption since it requires that all the nodes have access to some global synchronization mechanism and that message delays are upper bounded. Several authors, including Giakkoupis et al (2016); Mocquard et al (2016); Acan et al (2017); Doerr et al (2012b); Pourmiri and Ramezani (2019), suppose that nodes asynchronously trigger operations with randomly chosen nodes. In Pourmiri and Ramezani (2019), the authors model a multiple call by tuning the clock rate of each node with a given probability distribution.…”

Section: Introductionmentioning

confidence: 99%

“…Several authors, including Giakkoupis et al (2016); Mocquard et al (2016); Acan et al (2017); Doerr et al (2012b); Pourmiri and Ramezani (2019), suppose that nodes asynchronously trigger operations with randomly chosen nodes. In Pourmiri and Ramezani (2019), the authors model a multiple call by tuning the clock rate of each node with a given probability distribution. Some authors have focused on the message complexity by optimizing the network structure Moreno et al (2004); Doerr et al (2012a); Doerr and Kostrygin (2017); Giakkoupis et al (2016).…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Analysis of Rumor Spreading with Multiple Pull Operations

Robin

Séricola

Anceaume

et al. 2021

Methodol Comput Appl Probab

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We propose and analyze a new asynchronous rumor spreading protocol to deliver a rumor to all the nodes of a large-scale distributed network. This spreading protocol relies on what we call a k-pull operation, with k ≥ 2 . Specifically a k-pull operation consists, for an uninformed node s, in contacting k − 1 other nodes at random in the network, and if at least one of them knows the rumor, then node s learns it. We perform a thorough study of the total number T k,n of k-pull operations needed for all the n nodes to learn the rumor. We compute the expected value and the variance of T k,n , together with their limiting values when n tends to infinity. We also analyze the limiting distribution of (T k,n − E(T k,n ))∕n and prove that it has a double exponential distribution when n tends to infinity. Finally, we show that when k > 2 , our new protocol requires less operations than the traditional 2-push-pull and 2-push protocols by using stochastic dominance arguments. All these results generalize the standard case k = 2.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic Analysis of Rumor Spreading with Multiple Pull Operations

Robin

Séricola

Anceaume

et al. 2021

Methodol Comput Appl Probab

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“…In large scale and open networks, assuming that all nodes act synchronously is a very strong assumption. Several authors, including [1], [10], [18], [22], [27]), suppose that nodes asynchronously trigger operations with randomly chosen nodes. In [27], the authors model a multiple call by tuning the clock rate of each node with a given probability distribution.…”

Section: Introductionmentioning

confidence: 99%

“…Several authors, including [1], [10], [18], [22], [27]), suppose that nodes asynchronously trigger operations with randomly chosen nodes. In [27], the authors model a multiple call by tuning the clock rate of each node with a given probability distribution. Some authors have focused on the message complexity by optimizing the network structure [9], [11], [18], [23].…”

Section: Introductionmentioning

confidence: 99%

“…Another way of limiting the number of interactions is by finely tuning the push and pull operations to take advantage of both of them as achieved for example in [8], [12], or by relying on a central authority to coordinate the work (e.g, [5]). The asynchronous gossip protocol is usually modeled by a time-continuous stochastic (Markovian) process [1], [10], [18], [22], [27]. This type of stochastic processes belongs to the death process category, which has many applications in demography, queuing theory, performance engineering, epidemiology, biology and many other distributed applications.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Rumor Spreading with 2-pull or 3-pull Operations

Mocquard

Séricola

Anceaume

2021

2021 IEEE 20th International Symposium on Network Computing and Applications (NCA)

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In this paper, we analyze a new asynchronous rumor spreading protocol to deliver a rumor to all the nodes of a largescale distributed network. This protocol relies on successive pull operations involving k different nodes, with k = 2 or k = 3, and called k-pull operations. Specifically during a k-pull operation, an uninformed node a contacts k − 1 other nodes at random in the network, and if at least one of them knows the rumor, then node a learns it. We perform a detailed study in continuous-time of Θ k,n , the total time needed for all the n nodes to learn the rumor.We obtain, for k ∈ {2, 3}, the mean value, the variance and the distribution of Θ k,n together with their asymptotic behavior when the number of nodes n tends to infinity.

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Stochastic Analysis of Rumor Spreading with Multiple Pull Operations in Presence of Non-cooperative Nodes

Kilian,

Anceaume,

Sericola

2023

Lecture Notes in Computer Science

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The recent rise of interest in distributed applications has highlighted the importance of effective information dissemination. The challenge lies in the fact that nodes in a distributed system are not necessarily synchronized, and may fail at any time. This has led to the emergence of randomized rumor spreading protocols, such as push and pull protocols, which have been studied extensively. The k-pull operation, which allows an uninformed node to ask for the rumor from a fixed number of other nodes in parallel, has been proposed to improve the pull algorithm's effectiveness. This paper presents and studies the performance of the k-pull operation in the presence of a certain fraction f of non-cooperative nodes. Our goal is to understand the impact of k on the propagation of the rumor despite the presence of a fraction f of non-collaborative nodes.

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Ultra-Fast Asynchronous Randomized Rumor Spreading (Brief Announcement)

Cited by 6 publications

References 13 publications

Stochastic Analysis of Rumor Spreading with Multiple Pull Operations

Stochastic Analysis of Rumor Spreading with Multiple Pull Operations

Analysis of Rumor Spreading with 2-pull or 3-pull Operations

Stochastic Analysis of Rumor Spreading with Multiple Pull Operations in Presence of Non-cooperative Nodes

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