3,715 research outputs found
Three applications for mobile epidemic algorithms
This paper presents a framework for the pervasive sharing of data using wireless networks. 'FarCry' uses the mobility of users to carry files between separated networks. Through a mix of ad-hoc and infrastructure-based wireless networking, files are transferred between users without their direct involvement. As users move to different locations, files are then transmitted on to other users, spreading and sharing information. We examine three applications of this framework. Each of these exploits the physically proximate nature of social gatherings. As people group together in, for example, business meetings and cafés, this can be taken as an indication of similar interests, e.g. in the same presentation or in a type of music. MediaNet affords sharing of media files between strangers or friends, MeetingNet shares business documents in meetings, and NewsNet shares RSS feeds between mobile users. NewsNet also develops the use of pre-emptive caching: collecting information from others not for oneself, but for the predicted later sharing with others. We offer observations on developing this system for a mobile, multi-user, multi-device environment
A stochastic epidemiological model and a deterministic limit for BitTorrent-like peer-to-peer file-sharing networks
In this paper, we propose a stochastic model for a file-sharing peer-to-peer
network which resembles the popular BitTorrent system: large files are split
into chunks and a peer can download or swap from another peer only one chunk at
a time. We prove that the fluid limits of a scaled Markov model of this system
are of the coagulation form, special cases of which are well-known
epidemiological (SIR) models. In addition, Lyapunov stability and settling-time
results are explored. We derive conditions under which the BitTorrent
incentives under consideration result in shorter mean file-acquisition times
for peers compared to client-server (single chunk) systems. Finally, a
diffusion approximation is given and some open questions are discussed.Comment: 25 pages, 6 figure
Diffusion of Digital Products in Peer-to-Peer Networks
Peer-to-peer (P2P) networks are fast emerging as a viable and cost effective alternative for content delivery on the Internet. By offering rebates to users who share content with others, incentives can be provided to address the well-documented problem of free riding. A primary value proposition of P2P networks is their ability to scale well and facilitate fast distribution of digital products. While the fast diffusion of products in P2P networks has generated substantial interest in P2P, rigorous theoretical studies of the diffusion process have been in absence. Our paper provides one of the first analytical studies of the diffusion process in P2P networks. Starting with an analogy between P2P diffusion and epidemic diffusion, we develop a stochastic diffusion model for flat P2P networks. We find that product diffusion, in P2P networks is likely to follow classic S-shaped processes. Next, we develop a deterministic approximation that is computationally efficient. The model allows a content publisher to analyze the diffusion process, evaluate the impact of offering rebates on product diffusion and also determine the optimal rebate to offer by trading off the reduced margins with the faster diffusion of the product. Finally, we expand our study to account for generation of multiple requests and forwarding of requests in P2P networks. The analytical models presented in this paper serve as a starting point for rigorous modeling and study of content diffusion in P2P networks
A Gossip-based optimistic replication for efficient delay-sensitive streaming using an interactive middleware support system
While sharing resources the efficiency is substantially degraded as a result
of the scarceness of availability of the requested resources in a multiclient
support manner. These resources are often aggravated by many factors like the
temporal constraints for availability or node flooding by the requested
replicated file chunks. Thus replicated file chunks should be efficiently
disseminated in order to enable resource availability on-demand by the mobile
users. This work considers a cross layered middleware support system for
efficient delay-sensitive streaming by using each device's connectivity and
social interactions in a cross layered manner. The collaborative streaming is
achieved through the epidemically replicated file chunk policy which uses a
transition-based approach of a chained model of an infectious disease with
susceptible, infected, recovered and death states. The Gossip-based stateful
model enforces the mobile nodes whether to host a file chunk or not or, when no
longer a chunk is needed, to purge it. The proposed model is thoroughly
evaluated through experimental simulation taking measures for the effective
throughput Eff as a function of the packet loss parameter in contrast with the
effectiveness of the replication Gossip-based policy.Comment: IEEE Systems Journal 201
Internet Privacy Information Propagation Model
With the rapid growth of information and communication technology (ICT), the violation of information privacy has increased in recent years. The privacy concerns now re-emerge right because people perceives a threat from new ICT that are equipped with enhanced capabilities for surveillance, storage, retrieval, and diffusion of personal information. With the trend in the prevalence and the easy use of ICT, it is of necessary to pay much attention to the issue how the ICT can threaten the privacy of individuals on the Internet. While the Email and P2P tools are the most popular ICT, this paper aims at understanding their respectively dissemination patterns in spreading of personal private information. To this purpose, this paper using dynamic model technique to simulate the pattern of sensitive or personal private information propagating situation. In this study, an Email propagation model and a Susceptible-Infected-Removed (SIR) model are proposed to simulate the propagation patterns of Email and P2P network respectively. Knowing their dissemination patterns would be helpful for system designers, ICT manager, corporate IT personnel, educators, policy makers, and legislators to incorporate consciousness of social and ethical information issues into the protection of information privacy
Diffusion Models for Peer-to-Peer (P2P) Media Distribution: On the Impact of Decentralized, Constrained Supply
In Peer-to-Peer (P2P) media distribution, users obtain content from other users who already have it. This form of decentralized product distribution demonstrates several unique features. Only a small fraction of users in the network are queried when a potential adopter seeks a file and many of these users may even free-ride i.e. not distribute the content to others. As a result, generated demand may not always be fulfilled immediately. We present mixing models for product diffusion in P2P networks that capture decentralized product distribution by current adopters, incomplete demand fulfillment and other unique aspects of P2P product diffusion. The models serve to demonstrate the important role that P2P search process and distribution referrals – payments made to users that distribute files – play in efficient P2P media distribution. We demonstrate the ability of our diffusion models to derive normative insights for P2P media distributors by studying the effectiveness of distribution referrals in speeding product diffusion and determining optimal referral policies for fully decentralized and hierarchical P2P networks
Reactive immunization on complex networks
Epidemic spreading on complex networks depends on the topological structure
as well as on the dynamical properties of the infection itself. Generally
speaking, highly connected individuals play the role of hubs and are crucial to
channel information across the network. On the other hand, static topological
quantities measuring the connectivity structure are independent on the
dynamical mechanisms of the infection. A natural question is therefore how to
improve the topological analysis by some kind of dynamical information that may
be extracted from the ongoing infection itself. In this spirit, we propose a
novel vaccination scheme that exploits information from the details of the
infection pattern at the moment when the vaccination strategy is applied.
Numerical simulations of the infection process show that the proposed
immunization strategy is effective and robust on a wide class of complex
networks
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