A Chain-Binomial Model for Pull and Push-Based Information Diffusion

We compare pull and push-based epidemic paradigms for information diffusion in large scale networks. Key benefits of these approaches are that they are fully distributed, utilize local information only via pair-wise interactions, and provide eventual consistency, scalability and communication topolo...

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Bibliographic Details
Published in:IEEE International Conference on Communications (2003) Vol. 2; pp. 909 - 914
Main Authors: Caglar, Mine, Ozkasap, Oznur
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2006
Subjects:
anti-entropy
chain-binomial
Context
Delay
epidemic algorithms
Information analysis
Large-scale systems
Mathematical model
Peer to peer computing
peer-to-peer
Protocols
Robustness
Scalability
Telecommunication network reliability
ISSN:1550-3607
Online Access:Get full text
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Summary:We compare pull and push-based epidemic paradigms for information diffusion in large scale networks. Key benefits of these approaches are that they are fully distributed, utilize local information only via pair-wise interactions, and provide eventual consistency, scalability and communication topology-independence, which make them suitable for peer-to-peer distributed systems. We develop a chain-Binomial epidemic probability model for these algorithms. Our main contribution is the exact computation of message delivery latency observed by each peer, which corresponds to a first passage time of the underlying Markov chain. Such an analytical tool facilitates the comparison of pull and push-based spread for different group sizes, initial number of infectious peers and fan-out values which are also accomplished in this study. Via our analytical stochastic model, we show that push-based approach is expected to facilitate faster information spread both for the whole group and as experienced by each member.
ISSN:1550-3607
DOI:10.1109/ICC.2006.254823