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

Content Provider	IEEE Xplore Digital Library
Author	Caglar, M. Ozkasap, O.
Copyright Year	2006
Description	Author affiliation: Department of Computer Engineering, Koç University, Istanbul, Turkey. oozkasap@ku.edu.tr (Ozkasap, O.) \|\| Department of Mathematics, Koç University, Istanbul, Turkey. mcaglar@ku.edu.tr (Caglar, M.)
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.
Starting Page	909
Ending Page	914
File Size	320581
Page Count	6
File Format	PDF
ISBN	1424403553
ISSN	81649547
DOI	10.1109/ICC.2006.254823
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-11
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Peer to peer computing Delay Large-scale systems Scalability Information analysis Context Telecommunication network reliability Protocols Robustness Mathematical model peer-to-peer chain-binomial epidemic algorithms anti-entropy
Content Type	Text
Resource Type	Article

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