Remedy: un Supporto basato su DHT per la Ricerca di Risorse Dinamiche su Griglie Computazionali

Presentazione e relativi risultati sperimentali di un approccio originale per la ricerca e pubblicazione di risorse con attributi dinamici in ambienti distributi, in particolare grglie computazionali, usando approcci basati su dht

A Holistic Approach for High-level Programming of Next-generation Data-intensive Applications Targeting Distributed Heterogeneous Computing Environment

AbstractThe intrinsic richness and heterogeneity of large amount of data is paired with the extreme complexity in its storing and processing, as well as with the heterogeneity of their processing environments, ranging from super computers to federations of Cloud data-centres. This makes the conception, definition and implementation of software tools for programming applications dealing with very large amount of data really challenging from different perspectives, ranging from technological issues to economic concerns. We propose an approach focused on data-intensive applications that goes beyond the state of the art allowing a seamless exploitation of heterogeneous and distributed resources and satisfying users’ needs on data processing providing a dynamically determined set of features, depending on the running environment, the application, the user requirements

Dragon: Multidimensional Range Queries on Distributed Aggregation Trees,

Distributed query processing is of paramount importance in next-generation distribution services, such as Internet of Things (IoT) and cyber-physical systems. Even if several multi-attribute range queries supports have been proposed for peer-to-peer systems, these solutions must be rethought to fully meet the requirements of new computational paradigms for IoT, like fog computing. This paper proposes dragon, an ecient support for distributed multi-dimensional range query processing targeting ecient query resolution on highly dynamic data. In dragon nodes at the edges of the network collect and publish multi-dimensional data. The nodes collectively manage an aggregation tree storing data digests which are then exploited, when resolving queries, to prune the sub-trees containing few or no relevant matches. Multi-attribute queries are managed by linearising the attribute space through space lling curves. We extensively analysed dierent aggregation and query resolution strategies in a wide spectrum of experimental set-ups. We show that dragon manages eciently fast changing data values. Further, we show that dragon resolves queries by contacting a lower number of nodes when compared to a similar approach in the state of the art

On the codimension of permanental varieties

In this article, we study permanental varieties, i.e. varieties defined by the vanishing of permanents of fixed size of a generic matrix. Permanents and their varieties play an important, and sometimes poorly understood, role in combinatorics. However, there are essentially no geometric results about them in the literature, in very sharp contrast to the well-behaved and ubiquitous case of determinants and minors. Motivated by the study of the singular locus of the permanental hypersurface, we focus on the codimension of these varieties. We introduce a $\mathbb C^{*}$-action on matrices and prove a number of results. In particular, we improve a lower bound on the codimension of the aforementioned singular locus established by von zur Gathen in 1987.Comment: 20

Distributed Current Flow Betweeness Centrality

—The computation of nodes centrality is of great importance for the analysis of graphs. The current flow betweenness is an interesting centrality index that is computed by considering how the information travels along all the possible paths of a graph. The current flow betweenness exploits basic results from electrical circuits, i.e. Kirchhoff’s laws, to evaluate the centrality of vertices. The computation of the current flow betweenness may exceed the computational capability of a single machine for very large graphs composed by millions of nodes. In this paper we propose a solution that estimates the current flow betweenness in a distributed setting, by defining a vertex-centric, gossip-based algorithm. Each node, relying on its local information, in a selfadaptive way generates new flows to improve the betweenness of all the nodes of the graph. Our experimental evaluation shows that our proposal achieves high correlation with the exact current flow betweenness, and provides a good centrality measure for large graphs

SmartORC: smart orchestration of resources in the compute continuum

The promise of the compute continuum is to present applications with a flexible and transparent view of the resources in the Internet of Things–Edge–Cloud ecosystem. However, such a promise requires tackling complex challenges to maximize the benefits of both the cloud and the edge. Challenges include managing a highly distributed platform, matching services and resources, harnessing resource heterogeneity, and adapting the deployment of services to the changes in resources and applications. In this study, we present SmartORC, a comprehensive set of components designed to provide a complete framework for managing resources and applications in the Compute Continuum. Along with the description of all the SmartORC subcomponents, we have also provided the results of an evaluation aimed at showcasing the framework's capability

Big Data Research in Italy: A Perspective

The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains