20,730 research outputs found
Agreement in epidemic information dissemination
Consensus is one of the fundamental problems in multi-agent systems and distributed computing, in which agents or processing nodes are required to reach global agreement on some data value, decision, action, or synchronisation. In the absence of centralised coordination, achieving global consensus is challenging especially in dynamic and large-scale distributed systems with faulty processes. This paper presents a fully decentralised phase transition protocol to achieve global consensus on the convergence of an underlying information dissemination process. The proposed approach is based on Epidemic protocols, which are a randomised communication and computation paradigm and provide excellent scalability and fault-tolerant properties. The experimental analysis is based on simulations of a large-scale information dissemination process and the results show that global agreement can be achieved without deterministic and global communication patterns, such as those based on centralised coordination
Disease and information spreading at different speeds in multiplex networks
Nowadays, one of the challenges we face when carrying out modeling of epidemic spreading is to develop methods to control disease transmission. In this article we study how the spreading of knowledge of a disease affects the propagation of that disease in a population of interacting individuals. For that, we analyze the interaction between two different processes on multiplex networks: the propagation of an epidemic using the susceptible-infected-susceptible dynamics and the dissemination of information about the disease—and its prevention methods—using the unaware-aware-unaware dynamics, so that informed individuals are less likely to be infected. Unlike previous related models where disease and information spread at the same time scale, we introduce here a parameter that controls the relative speed between the propagation of the two processes. We study the behavior of this model using a mean-field approach that gives results in good agreement with Monte Carlo simulations on homogeneous complex networks. We find that increasing the rate of information dissemination reduces the disease prevalence, as one may expect. However, increasing the speed of the information process as compared to that of the epidemic process has the counterintuitive effect of increasing the disease prevalence. This result opens an interesting discussion about the effects of information spreading on disease propagation
Dynamic Resource Management in Clouds: A Probabilistic Approach
Dynamic resource management has become an active area of research in the
Cloud Computing paradigm. Cost of resources varies significantly depending on
configuration for using them. Hence efficient management of resources is of
prime interest to both Cloud Providers and Cloud Users. In this work we suggest
a probabilistic resource provisioning approach that can be exploited as the
input of a dynamic resource management scheme. Using a Video on Demand use case
to justify our claims, we propose an analytical model inspired from standard
models developed for epidemiology spreading, to represent sudden and intense
workload variations. We show that the resulting model verifies a Large
Deviation Principle that statistically characterizes extreme rare events, such
as the ones produced by "buzz/flash crowd effects" that may cause workload
overflow in the VoD context. This analysis provides valuable insight on
expectable abnormal behaviors of systems. We exploit the information obtained
using the Large Deviation Principle for the proposed Video on Demand use-case
for defining policies (Service Level Agreements). We believe these policies for
elastic resource provisioning and usage may be of some interest to all
stakeholders in the emerging context of cloud networkingComment: IEICE Transactions on Communications (2012). arXiv admin note:
substantial text overlap with arXiv:1209.515
Predicting the Impact of Measures Against P2P Networks on the Transient Behaviors
The paper has two objectives. The first is to study rigorously the transient
behavior of some P2P networks whenever information is replicated and
disseminated according to epidemic-like dynamics. The second is to use the
insight gained from the previous analysis in order to predict how efficient are
measures taken against peer-to-peer (P2P) networks. We first introduce a
stochastic model which extends a classical epidemic model and characterize the
P2P swarm behavior in presence of free riding peers. We then study a second
model in which a peer initiates a contact with another peer chosen randomly. In
both cases the network is shown to exhibit a phase transition: a small change
in the parameters causes a large change in the behavior of the network. We
show, in particular, how the phase transition affects measures that content
provider networks may take against P2P networks that distribute non-authorized
music or books, and what is the efficiency of counter-measures.Comment: IEEE Infocom (2011
A dissemination strategy for immunizing scale-free networks
We consider the problem of distributing a vaccine for immunizing a scale-free
network against a given virus or worm. We introduce a new method, based on
vaccine dissemination, that seems to reflect more accurately what is expected
to occur in real-world networks. Also, since the dissemination is performed
using only local information, the method can be easily employed in practice.
Using a random-graph framework, we analyze our method both mathematically and
by means of simulations. We demonstrate its efficacy regarding the trade-off
between the expected number of nodes that receive the vaccine and the network's
resulting vulnerability to develop an epidemic as the virus or worm attempts to
infect one of its nodes. For some scenarios, the new method is seen to render
the network practically invulnerable to attacks while requiring only a small
fraction of the nodes to receive the vaccine
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