On Spreading a Rumor

Pittel, Boris

doi:10.1137/0147013

Cited by 310 publications

(271 citation statements)

References 9 publications

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“…In one of the first papers in this area, Frieze and Grimmett [19] proved that if the underlying graph is a complete graph with n nodes, then the runtime of Push is log 2 n + log n ± o(log n) with high probability 1 , where log n denotes the natural logarithm of n. This result was later strengthened by Pittel [29]. For the standard Push-Pull protocol, Karp et al [24] proved a runtime bound of log 3 n + O(log log n).…”

Section: Introductionmentioning

confidence: 63%

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Faster Rumor Spreading with Multiple Calls

Παναγιώτου

Pourmiri

Sauerwald

2013

Algorithms and Computation

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We consider the random phone call model introduced by Demers et al.,which is a well-studied model for information dissemination in networks. One basic protocol in this model is the so-called Push protocol that proceeds in synchronous rounds. Starting with a single node which knows of a rumor, every informed node calls in each round a random neighbor and informs it of the rumor. The Push-Pull protocol works similarly, but additionally every uninformed node calls a random neighbor and may learn the rumor from it.It is well-known that both protocols need Θ(log n) rounds to spread a rumor on a complete network with n nodes. Here we are interested in how much the spread can be speeded up by enabling nodes to make more than one call in each round. We propose a new model where the number of calls of a node is chosen independently according to a probability distribution R. We provide both lower and upper bounds on the rumor spreading time depending on statistical properties of R such as the mean or the variance (if they exist). In particular, if R follows a power law distribution with exponent β ∈ (2, 3), we show that the Push-Pull protocol spreads a rumor in Θ(log log n) rounds. Moreover, when β = 3, the PushPull protocol spreads a rumor in Θ( log n log log n ) rounds.

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Section: Introductionmentioning

confidence: 63%

“…We point out that the lower bound in Theorem 1.2 is tight up to constant factors, as the results in [19,29] for the standard Push protocol imply an upper bound of O(log n) rounds. We now consider the Push-Pull protocol and extend the lower bound of Ω(log n) from Theorem 1.1.…”

Section: Theorem 12 Assume That R Is Any Distribution With E [R] = mentioning

confidence: 96%

Faster Rumor Spreading with Multiple Calls

Παναγιώτου

Pourmiri

Sauerwald

2013

Algorithms and Computation

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“…The idea of randomized rumor distribution dates back to Frieze and Grimmett [10], and later, Pittel [11] and Feige [12], who showed that one can distribute a single rumor in a complete graph with n vertices in time log n + ln n + o(1), as well as in other types of random graphs. Demers et al [2] showed that rumor spreading can be used for efficient distributed database maintenance.…”

Section: Related Workmentioning

confidence: 99%

How Efficient Can Gossip Be? (On the Cost of Resilient Information Exchange)

Alistarh

Gilbert

Guerraoui

et al. 2010

Automata, Languages and Programming

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Abstract. Gossip, also known as epidemic dissemination, is becoming an increasingly popular technique in distributed systems. Yet, it has remained a partially open question: how robust are such protocols? We consider a natural extension of the random phone-call model (introduced by Karp et al. [1]), and we analyze two different notions of robustness: the ability to tolerate adaptive failures, and the ability to tolerate oblivious failures.For adaptive failures, we present a new gossip protocol, TrickleGossip, which achieves near-optimal O(n log 3 n) message complexity. To the best of our knowledge, this is the first epidemic-style protocol that can tolerate adaptive failures. We also show a direct relation between resilience and message complexity, demonstrating that gossip protocols which tolerate a large number of adaptive failures need to use a super-linear number of messages with high probability.For oblivious failures, we present a new gossip protocol, CoordinatedGossip, that achieves optimal O(n) message complexity. This protocol makes novel use of the universe reduction technique to limit the message complexity.

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“…Distributed Hash Tables (DHTs) [25,13,22] is a structured overlay solution that are leveraging the power of consistent hashing [9]. On the other hand, the gossip-based information sharing protocols [18], also known as epidemic or flooding protocols, process requests from clients in unstructured way. The core implementation relies in flooding search requests to peering neighbors.…”

Section: Control and Signaling Approachesmentioning

confidence: 99%

An Adversarial Evaluation of Network Signaling and Control Mechanisms

Jee

Sidiroglou-Douskos²,

Stavrou³

et al. 2011

Information Security and Cryptology - ICISC 2010

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Abstract. Network signaling and control mechanisms are critical to coordinate such diverse defense capabilities as honeypots and honeynets, host-based defenses, and online patching systems, any one of which might issue an actionable alert and provide security-critical data. Despite considerable work in exploring the trust requirements of such defenses and in addressing the distribution speed of alerts, little work has gone into identifying how the underlying transport systems behave under adversarial scenarios. In this paper, we evaluate the reliability and performance trade-offs for a variety of control channel mechanisms that are suitable for coordinating large-scale collaborative defenses when under attack. Our results show that the performance and reliability characteristics change drastically when one evaluates the systems under attack by a sophisticated and targeted adversary. Based on our evaluation, we explore available design choices to reinforce the reliability of the control channel mechanisms. To that end, we propose ways to construct a control scheme to improve network coverage without imposing additional overhead.

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On Spreading a Rumor

Cited by 310 publications

References 9 publications

Faster Rumor Spreading with Multiple Calls

Faster Rumor Spreading with Multiple Calls

How Efficient Can Gossip Be? (On the Cost of Resilient Information Exchange)

An Adversarial Evaluation of Network Signaling and Control Mechanisms

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