The churn rate of a peer-to-peer system places direct limitations on the rate at which messages can be effectively communicated to a group of peers. These limitations are independent of the topology and message transmission latency. In this paper we consider a peer-to-peer network, based on the Engset model, where peers arrive and depart independently at random. We show how the arrival and departure rates directly limit the capacity for message streams to be broadcast to all other peers, by deriving mean field models that accurately describe the system behavior. Our models cover the unit and more general k buffer cases, i.e. where a peer can buffer at most k messages at any one time, and we give results for both single and multi-source message streams. We define coverage rate as peer-messages per unit time, i.e. the rate at which a number of peers receive messages, and show that the coverage rate is limited by the churn rate and buffer size. Our theory introduces an Instantaneous Message Exchange (IME) model and provides a template for further analysis of more complicated systems. Using the IME model, and assuming random processes, we have obtained very accurate equations of the system dynamics in a variety of interesting cases, that allow us to tune a peer-to-peer system. It remains to be seen if we can maintain this accuracy for general processes and when applying a non-instantaneous model.
Other closely related work such as in @cite_4 looks at the rate of file transmission in a file sharing system that is based on epidemics. The use of epidemics for large scale communication is also reviewed in @cite_0 . The probabilistic multicast technique in @cite_2 attempts to increase the probability that peers receive messages for which they are interested and to decrease the probability that peers receive messages for which they are not interested. Hence it introduces a notion of membership which is not too different to being online offline. Autonomous Gossiping presented in @cite_5 provides further examples of using epidemics for selective information dissemination.
@cite_0 @cite_5 @cite_4 @cite_2
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Peer-to-peer applications have become highly popular in today's pervasive environments due to the spread of different file sharing platforms. In such a multiclient environment, if users have mobility characteristics, asymmetry in communication causes a degradation of reliability. This work proposes an approach based on the advantages of epidemic selective resource placement through mobile Infostations. Epidemic placement policy combines the strengths of both proactive multicast group establishment and hybrid Infostation concept. With epidemic selective placement we face the flooding problem locally (in geographic region landscape) and enable end to end reliability by forwarding requested packets to epidemically 'selected' mobile users in the network on a recursive basis. The selection of users is performed based on their remaining capacity, weakness of their signal and other explained mobility limitations. Examination through simulation is performed for the response and reliability offered by epidemic placement policy which reveals the robustness and reliability in file sharing among mobile peers. Epidemic algorithms have recently been proposed as an effective solution for disseminating information in large-scale peer-to-peer (P2P) systems and in mobile ad hoc networks (MANET). In this paper, we present a modeling approach for steady-state analysis of epidemic dissemination of information in MANET. As major contribution, the introduced approach explicitly represents the spread of multiple data items, finite buffer capacity at mobile devices and a least recently used buffer replacement scheme. Using the introduced modeling approach, we analyze seven degrees of separation (7DS) as one well-known approach for implementing P2P data sharing in a MANET using epidemic dissemination of information. A validation of results derived from the analytical model against simulation shows excellent agreement. Quantitative performance curves derived from the analytical model yield several insights for optimizing the system design of 7DS. We introduce autonomous gossiping (A G), a new genre epidemic algorithm for selective dissemination of information in contrast to previous usage of epidemic algorithms which flood the whole network. A G is a paradigm which suits well in a mobile ad-hoc networking (MANET) environment because it does not require any infrastructure or middleware like multicast tree and (un)subscription maintenance for publish subscribe, but uses ecological and economic principles in a self-organizing manner in order to achieve any arbitrary selectivity (flexible casting). The trade-off of using a stateless self-organizing mechanism like A G is that it does not guarantee completeness deterministically as is one of the original objectives of alternate selective dissemination schemes like publish subscribe. We argue that such incompleteness is not a problem in many non-critical real-life civilian application scenarios and realistic node mobility patterns, where the overhead of infrastructure maintenance may outweigh the benefits of completeness, more over, at present there exists no mechanism to realize publish subscribe or other paradigms for selective dissemination in MANET environments. For the study of information propagation, one fundamental problem is uncovering universal laws governing the dynamics of information propagation. This problem, from the microscopic perspective, is formulated as estimating the propagation probability that a piece of information propagates from one individual to another. Such a propagation probability generally depends on two major classes of factors: the intrinsic attractiveness of information and the interactions between individuals. Despite the fact that the temporal effect of attractiveness is widely studied, temporal laws underlying individual interactions remain unclear, causing inaccurate prediction of information propagation on evolving social networks. In this report, we empirically study the dynamics of information propagation, using the dataset from a population-scale social media website. We discover a temporal scaling in information propagation: the probability a message propagates between two individuals decays with the length of time latency since their latest interaction, obeying a power-law rule. Leveraging the scaling law, we further propose a temporal model to estimate future propagation probabilities between individuals, reducing the error rate of information propagation prediction from 6.7 to 2.6 and improving viral marketing with 9.7 incremental customers.