Distributed control in virtualized networks

The increasing number of the Internet connected devices requires novel solutions to control the next generation network resources. The cooperation between the Software Defined Network (SDN) and the Network Function Virtualization (NFV) seems to be a promising technology paradigm. The bottleneck of current SDN/NFV implementations is the use of a centralized controller. In this paper, different scenarios to identify the pro and cons of a distributed control-plane were investigated. We implemented a prototypal framework to benchmark different centralized and distributed approaches. The test results have been critically analyzed and related considerations and recommendations have been reported. The outcome of our research influenced the control plane design of the following European R&D projects: PLATINO, FI-WARE and T-NOVA

The quality of political information

The article conceptualizes the quality of political information and shows how the concept can be used for empirical research. I distinguish three aspects of quality (intelligibility, relevance, validity) and use them to judge the constituent foundations of political information, that is component claims (statements of alleged facts) and connection claims (argumentative statements created by causally linking two component claims). The resulting conceptual map thus entails six manifestations of information quality (component claimintelligibility, connection claim intelligibility, component claimrelevance, connection claim relevance, component claim validity, and connection claim validity). I explain how the conceptual map can be used to make sense of the eclectic variety of existing research, and how it can advance new empirical research, as a guide for determining variation in information quality, as a conceptual template for the analysis of different types of political messages and their common quality deficiencies, and as a generator of new research questions and theoretical expectations

Business Fluctuations and Bankruptcy Avalanches in an Evolving Network Economy

We analyze the properties of a three-sector network economy characterized by credit relationships connecting downstream and upstream firms (inside credit) and credit relationships connecting firms and banks (outside credit). The network topology changes over time due to an endogenous process of partner selection (the preferred-partner choice rule). The output of simulations shows that a business cycle at the macroeconomic level can develop as a consequence of the complex interaction of the heterogeneous financial conditions of the agents involved. In this paper, we focus on the emergence of bankruptcy crises: the bankruptcy of one agent can bring about the bankruptcy of one or more other agents in a snowball effect of more or less large size, depending on the network structure and the incidence of non-performing loans on balance sheets of agents involved

Credit Chains and Bankruptcy Propagation in Production Networks

We present a simple model of a production network in which firms are linked by supplier-customer relationships involving extension of trade-credit. Our aim is to identify the minimal set of mechanisms which reproduce qualitatively the main stylized facts of industrial demography, such as firms' size distribution, and, at the same time, the correlation, over time and across firms, of output, growth and bankruptcies. The behavior of aggregate variables can be traced back to the direct firm-firm interdependence. In this paper, we assume that the number of firms is constant and the network has a periodic static structure. But the framework allows further extensions to investigate which network structures are more robust against domino effects and, if the network is let to evolve in time, which structures emerge spontaneously, depending on the individual strategies for orders and delivery

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields

Ten years of pluviometric analyses in Italy for civil protection purposes

The concept of climate change has grown in recent decades, influencing the scientific community to conduct research on meteorological parameters and their variabilities. Research on global warming, as well as on its possible economic and environmental consequences, has spread over the last 20 years. Diffused changes in trends have been stated by several authors throughout the world, with different developments observed depending on the continent. Following a period of approximately 40 days of almost continuous rain that occurred from October to November 2019 across the Italian territory and caused several hazards (e.g., floods and landslides), a relevant question for decision-makers and civil protection actors emerged regarding the relative frequencies of given rainfall events in the Warning Hazard Zones (WHZs) of Italy. The derived products of this work could answer this question for both weather and hydrogeological operators thanks to the frequency and spatio-temporal distribution analyses conducted on 10-year daily rainfall data over the entire Italian territory. This work aspires to be an additional tool used to analyse events that have occurred, providing further information for a better understanding of the probability of occurrence and distribution of future events

Economic dynamics with financial fragility and mean-field interaction: a model

Following the statistical mechanics methodology, firstly introduced in macroeconomics by Aoki [1996,2002], we provide some insights to the well known works of Greenwald and Stiglitz [1990, 1993]. Specifically, we reach analytically a closed form solution of their models overcoming the aggregation problem. The key idea is to represent the economy as an evolving complex system, composed by heterogeneous interacting agents, that can partitioned into a space of macroscopic states. This meso level of aggregation permits to adopt mean field interaction modeling and master equation techniques.Comment: APFA6 proceeding