An analytical framework for self-organizing peer-to-peer anti-entropy algorithms

Özkasap, Öznur; Çaǧlar, Mine; Yazıcı, Emine Şule; Küçükçifçi, Selda

doi:10.1016/j.peva.2009.09.009

Cited by 9 publications

(12 citation statements)

References 16 publications

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“…One of the key questions about the process (I 0 , I 1 , I 2 , ...), and hence about the push algorithm is this: how many rounds is required so that a given proportion of the network is infected? This is the quantity that all of [19,17,21,8] analyze. The answer to this question is expressed as the following stopping time of I:…”

Section: A Numerical Comparison Of Push and Pullmentioning

confidence: 99%

“…In this subsection it will be easier to use a separate symbol to denote the number of newly infected nodes in a pull round; let us useỸ (n, k) for this purpose. It is well known (see [19]) and simple to see thatỸ (n, k) is Binomial(1 − p n , n − k) with failure probability p n = n−k−1 c / n−1 c . Proposition 1.…”

Section: Comparison Of Push and Pull For Large Networkmentioning

confidence: 99%

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Analysis of push-type epidemic data dissemination in fully connected networks

Çaǧlar

Sezer

2014

Performance Evaluation

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Consider a fully connected network of nodes, some of which have a piece of data to be disseminated to the whole network. We analyze the following push-type epidemic algorithm: in each push round, every node that has the data, i.e., every infected node, randomly chooses c ∈ Z + other nodes in the network and transmits, i.e., pushes, the data to them. We write this round as a random walk whose each step corresponds to a random selection of one of the infected nodes; this gives recursive formulas for the distribution and the moments of the number of newly infected nodes in a push round. We use the formula for the distribution to compute the expected number of rounds so that a given percentage of the network is infected and continue a numerical comparison of the push algorithm and the pull algorithm (where the susceptible nodes randomly choose peers) initiated in an earlier work. We then derive the fluid and diffusion limits of the random walk as the network size goes to ∞ and deduce a number of properties of the push algorithm: 1) the number of newly infected nodes in a push round, and the number of random selections needed so that a given percent of the network is infected, are both asymptotically normal 2) for large networks, starting with a nonzero proportion of infected nodes, a pull round infects slightly more nodes on average 3) the number of rounds 1 until a given proportion λ of the network is infected converges to a constant for almost all λ ∈ (0, 1). Numerical examples for theoretical results are provided.

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Section: A Numerical Comparison Of Push and Pullmentioning

confidence: 99%

Section: Comparison Of Push and Pull For Large Networkmentioning

confidence: 99%

Analysis of push-type epidemic data dissemination in fully connected networks

Çaǧlar

Sezer

2014

Performance Evaluation

Self Cite

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“…Recently, Özkasap et al have presented the exact performance analysis of the data dissemination in P2P network using anti‐entropy algorithm . Their data distribution policy is almost similar to epidemic format of data distribution.…”

Section: Background and Related Workmentioning

confidence: 99%

Modeling epidemic data diffusion for wireless mobile networks

Islam

Akon

Abdrabou

et al. 2012

Wireless Communications

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Data/content dissemination among the mobile devices is the fundamental building block for all the applications in wireless mobile collaborative computing, known as mobile peer‐to‐peer. Different parameters such as node density, scheduling among neighboring nodes, mobility pattern, and node speed have a tremendous impact on data diffusion in a mobile peer‐to‐peer environment. In this paper, we develop analytical models for object diffusion time/delay in a wireless mobile network to apprehend the complex interrelationship among these different parameters. In the analysis, we calculate the probabilities of transmitting a single object from one node to multiple nodes using the epidemic model of spread of disease. We also incorporate the impact of node mobility, radio range, and node density in the networks into the analysis. Utilizing these transition probabilities, we estimate the expected delay for diffusing an object to the entire network both for single object and multiple object scenarios. We then calculate the transmission probabilities of multiple objects among the nodes in the wireless mobile network considering network dynamics. Through extensive simulations, we demonstrate that the proposed scheme is efficient for data diffusion in the wireless mobile network. Copyright © 2012 John Wiley & Sons, Ltd.

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“…Recently, Özkasap et al have presented the exact performance analysis of the data dissemination in P2P network using anti-entropy algorithm [19,20]. Their data distribution policy is almost similar to epidemic format of data distribution.…”

Section: Reed-frostmentioning

confidence: 99%

Modeling Epidemic Data Diffusion for Wireless Mobile Networks

Islam

Akon

Abdrabou

et al. 2011

2011 IEEE Global Telecommunications Conference - GLOBECOM 2011

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We propose an analytical model for data diffusion time/delay in a wireless mobile network using a novel peer-topeer spatial-demand based information dissemination technique. The demand-based technique for data dissemination is beneficial for mobile network since this fully distributed and scalable network system utilizes only local urge for data and provides faster delivery of information. However, due to mobility and chaotic wireless network, it is difficult to predict the object diffusion time/delay among all the interested nodes in a mobile network. Therefore, the development of an analytical model to anticipate the expected time of data distribution among the nodes in a mobile system is an important area of research. In response to this problem, we first find the probabilities of transmitting object from one node to multiple nodes using the epidemic model of disease spreading. Utilizing these transition probabilities, we construct an analytical model based on Markov chain to calculate the expected delay of information diffusion. In addition, we adopt the mobility and scheduling impact on data transition probabilities in our analytical model. Extensive eventbased simulations demonstrate that our analytical model provide near perfect estimation of data diffusion time/delay in wireless mobile networks.

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An analytical framework for self-organizing peer-to-peer anti-entropy algorithms

Cited by 9 publications

References 16 publications

Analysis of push-type epidemic data dissemination in fully connected networks

Analysis of push-type epidemic data dissemination in fully connected networks

Modeling epidemic data diffusion for wireless mobile networks

Modeling Epidemic Data Diffusion for Wireless Mobile Networks

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