The structure of a network dramatically affects the spreading phenomena unfolding upon it. The contact distribution of the nodes has long been recognized as the key ingredient in influencing the outbreak events. Howev...
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The structure of a network dramatically affects the spreading phenomena unfolding upon it. The contact distribution of the nodes has long been recognized as the key ingredient in influencing the outbreak events. However, limited knowledge is currently available on the role of the weight of the edges on the persistence of a pathogen. At the same time, recent works showed a strong influence of temporal network dynamics on disease spreading. In this work we provide an analytical understanding, corroborated by numerical simulations, about the conditions for infected stable state in weighted networks. In particular, we reveal the role of heterogeneity of edge weights and of the dynamic assignment of weights on the ties in the network in driving the spread of the epidemic. In this context we show that when weights are dynamically assigned to ties in the network, a heterogeneous distribution is able to hamper the diffusion of the disease, contrary to what happens when weights are fixed in time.
In recent years,two exciting approaches Software-defined Networking(SDN)and Post-IP architecture(or Future Internet Architecture)to future Internet have been studied all over the world both in industry and *** refacto...
In recent years,two exciting approaches Software-defined Networking(SDN)and Post-IP architecture(or Future Internet Architecture)to future Internet have been studied all over the world both in industry and *** refactors the relationship between network devices and the software that controls them to separate data plane and control plane and it is regarded as an incremental approach toward the future Internet *** the
This paper explores the Internet Background Radiation (IBR) observed across five distinct network telescopes over a 15 month period. These network telescopes consisting of a /24 netblock each and are deployed in IP sp...
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This paper describes an architecture for real-time environmental modeling. It consists of a wireless mesh network equipped with sensors and a cloud-based infrastructure to perform real-time environmental simulations u...
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This paper describes an architecture for real-time environmental modeling. It consists of a wireless mesh network equipped with sensors and a cloud-based infrastructure to perform real-time environmental simulations using a physics-based model combined with an Ensemble Kalman Filter. The purpose of the system is to optimize groundwater abstraction close to a river. These initial studies demonstrate that the cloud infrastructure can simultaneously compute a large number of simulations, thus allowing for the implementation of Ensemble Kalman Filters in real-time.
A number of approaches for endowing systems with self-Adaptive behavior have been proposed over the past years. Among such efforts, architecture-centric solutions with explicit representation of feedback loops have cu...
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Ecosystem functioning, climate change, and multiple interactions among biogeochemical cycles, climate system, site conditions and land use options are leading-edge topics in recent environmental modelling. Terrestrial...
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Ecosystem functioning, climate change, and multiple interactions among biogeochemical cycles, climate system, site conditions and land use options are leading-edge topics in recent environmental modelling. Terrestrial ecosystem models are widely used to support carbon sequestration and ecosystem studies under various ecological circumstances. Our team uses the Biome-BGC model (Numerical Terradynamic Simulation group, University of Montana), and develops an improved model version of it, called Biome-BGC MuSo. Both the original and the improved model estimate the ecosystem scale storage and fluxes of energy, carbon, nitrogen and water, controlled by various physical and biological processes on a daily time-scale. Web services were also developed and integrated with parallel processing desktop grid technology. Taverna workflow management system was used to build up and carry out elaborated workflows like seamless data flow to model simulation, Monte Carlo experiment, model sensitivity analysis, model-data fusion, estimation of ecosystem service indicators or extensive spatial modelling. Straightforward management of complex data analysis tasks, organized into appropriately documented, shared and reusable scientific workflows enables researchers to carry out detailed and scientifically challenging 'in silico' experiments and applications that could open new directions in ecosystem research and in a broader sense it supports progress in environmental modelling. The workflow approach built upon these web services allows even the most complicated computations to be initiated without the need of programming skills and deep understanding of model structure and initialization. The developments enable a wider array of scientists to perform ecosystem scale simulations, and to perform analyses not previously possible due to high complexity and computational demand.
A dynamic optimization algorithm is proposed for the joint allocation of subframes, resource blocks, and power in the Type 1 inband relaying scheme mandatory in the LTE-Advanced standard. Following the general framewo...
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This paper presents a generalized methodology and implementation aimed at surrogating the extended period hydraulic simulation with Artificial Neural networks (ANNs) for a large water distribution system. Each snapsho...
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This paper proposes a high-level architecture to support efficient, massively parallel packet classification, filtering and analysis using commodity Graphics Processing Unit (GPU) hardware. The proposed architecture a...
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This paper proposes a high-level architecture to support efficient, massively parallel packet classification, filtering and analysis using commodity Graphics Processing Unit (GPU) hardware. The proposed architecture aims to provide a flexible and efficient parallel packet processing and analysis framework, supporting complex programmable filtering, data mining operations, statistical analysis functions and traffic visualisation, with minimal CPU overhead. In particular, this framework aims to provide a robust set of high-speed analysis functionality, in order to dramatically reduce the time required to process and analyse extremely large network traces. This architecture derives from initial research, which has shown GPU co-processors to be effective in accelerating packet classification to up to tera-bit speeds with minimal CPU overhead, far exceeding the bandwidth capacity between standard long term storage and the GPU device. This paper provides a high-level overview of the proposed architecture and its primary components, motivated by the results of prior research in the field.
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