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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Veröffentlicht in:IEEE International Conference on Communications (2003) Jg. 2; S. 909 - 914
Hauptverfasser: Caglar, Mine, Ozkasap, Oznur
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2006
Schlagworte:
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
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Abstract 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.
AbstractList 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.
Author Caglar, Mine
Ozkasap, Oznur
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  organization: Department of Mathematics, Koç University, Istanbul, Turkey. mcaglar@ku.edu.tr
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  givenname: Oznur
  surname: Ozkasap
  fullname: Ozkasap, Oznur
  organization: Department of Computer Engineering, Koç University, Istanbul, Turkey. oozkasap@ku.edu.tr
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Snippet 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...
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StartPage 909
SubjectTerms 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
Title A Chain-Binomial Model for Pull and Push-Based Information Diffusion
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