Abstract The progress made in the field of medicine and the consequent increase in the prospect of life have contributed to rise people's interest towards a healthier lifestyle. Fitness activity is becoming a must for those who aspire to live more and better. However, this should be accompanied by additional good practices to safeguard individuals' life from risks that could undermine their health. Most of these risks are linked to personal, surrounding, and contextual conditions that technology can detect and monitor. Recommender systems can adequately support fitness activity by performing data analyzes aimed at identifying possible risk factors for users, starting from their physiological data and those related to the closest context where they are. This article introduces the architecture of a recommender system called App4Health in the context related to both mobile crowd sensing and wellness. The App4Health architecture consists of a smart application platform, capable of interfacing and managing data from heterogeneous edge sources, such as mobile phones, IoT, and sensors. The analysis result consists of the semantic generation of healthy behavioral conducts to the user as Telegram BOT messages. For evaluating the proposed solution, the article also provides a case study and a testbed. The testbed consists of a comparative stress test of two edge software components of the App4Health's architecture in order to identify the performance degradation threshold of these components, assuming that they can be deployed on edge‐level hardware devices with different technical specifications and configurations.

With the vigorous development of the Internet of Things (IoT), mobile users need to access data from other domains in edge computing. To achieve secure data sharing, mobile users first need to be authenticated by servers from different domains and then negotiate session keys among them. However, traditional schemes cannot solve cross-domain identity authentication and key agreement problems well due to the limited computational resources of IoT devices. In this work, we propose a Blockchain-based Cross-domain Authentication scheme for Edge computing, namely BCAE. First, to achieve secure identity verification, we design a novel cross-domain mutual identity authentication algorithm based on digital certificates and digital signatures. Next, to improve efficiency, we utilize the blockchain to share information among different domains to reduce the computation overhead. To realize quick key agreement, we apply the elliptic curve cryptography technique to design a lightweight key agreement algorithm and obtain secure session keys. Extensive experiments conducted on an actual smart healthcare issue to validate the performance of BCAE and formal security analysis confirmed the potential of the proposed work.

Abstract Trial excessive duration is a common problem in Juridical systems worldwide, even if some countries seem to be more affected by it than others. The European Council has provided metrics and statistics to identify this problem and has pointed out solutions, such as the simplification of norms and the digitization of Juridical procedures. The Italian Telematic Civil Process (TCP) is an example of this digitization effort that has surely positively influenced the duration of Trials, their traceability and general complexity. However, there are still many possible actions that can be taken to simplify the work of Judges and Chancellors, and to support their daily operations in dealing with several Trials at once, and with the consistent number of documents that are involved in them. This paper presents a toolchain and a related methodology for the management of documentation attached to Trials, based on semantic technologies and Natural Language Processing techniques, which will help Judges in faster assessing the situation of each Trial they follow, and will also provide the means to identify potential correlations among different Juridical procedures. The methodology is tested against a case study, i.e. the compensation requests related to road accidents, which has been provided and described by Domain Experts from the Italian Ministry of Justice.

Through the digitalisation of Civil Trials and the implementation of the Telematic Civil Process framework, the Italian Ministry of Justice has amassed a wealth of data covering all facets of modern Trials. While data availability has surged, the focus now lies in actively analysing this data to optimise Trials and curtail their duration. This paper, with a strong emphasis on its outcomes, delves into the analysis of data from the Court of Livorno. It seeks to pinpoint specific events within the Trial workflow that significantly extend Trial durations. Notably, Domain Experts have identified a set of events, intending to validate their pivotal role in recognising critical Trials. Leveraging Machine Learning techniques, the paper evaluates multiple binary classifiers to proactively identify potentially critical Trials, empowering Judges to mitigate future issues. The study has yielded a remarkable 80% accuracy rate in predicting Trials exceeding acceptable duration thresholds.

This paper focuses on exploring and comparing different machine learning algorithms in the context of diabetes management.The aim is to understand their characteristics, mathematical foundations, and practical implications specifically for predicting blood glucose levels.The study provides an overview of the algorithms, with a particular emphasis on deep learning techniques such as Long Short-Term Memory Networks.Efficiency is a crucial factor in practical machine learning applications, especially in the context of diabetes management.Therefore, the paper investigates the tradeoff between accuracy, resource utilisation, time consumption, and computational power requirements, aiming to identify the optimal balance.By analysing these algorithms, the research uncovers their distinct behaviours and highlights their dissimilarities, even when their analytical underpinnings may appear similar.

Abstract The adoption of the business process model notation for the description of internal workflows and procedures by both public administrations and private organizations is steadily growing, thanks to the simplicity of the standard and its consistent expressivity. However, the lack of semantic support from BPMN can pose important limitations to its efficient use, as ambiguities in task definitions and communications can lead to misinterpretations. Furthermore, there is the need to validate the BPMNs, to check their adherence to regulations, especially in public administration, and to verify their conformance to security and privacy constraints. In this work, we present SemPrAnn, a semantic annotation tool for BPMN that, exploiting domain ontologies and logical rules, provides the possibility to unambiguously identify concepts in workflows and to run inferential engines against them to enforce the rules. The manuscript presents the methodology applied for the implementation of the tool, the tool itself with its exposed functionalities, and a case study demonstrating its current capabilities.

Abstract Patients afflicted by multiple sclerosis experience a relapsing-remitting course in about 85% of the cases. Furthermore, after a 10/15-year period their situation tends to worse, resulting in what is considered the second phase of multiple sclerosis. While treatments are now available to reduce the symptoms and slow down the progression of the disease, the administration of drugs must be adapted to the course of the disease, and predicting relapsing periods and the worsening of the symptoms can greatly improve the outcome of the treatment. For this reason, indicators such as the patient-reported outcome measures (PROMs) have been largely used to support early diagnosis and prediction of future relapsing periods in patients affected by multiple sclerosis. However, such indicators are insufficient, as the prediction they provide is often not accurate enough. In this paper, machine learning techniques have been applied to data obtained from clinical trial, in order to improve the prediction capabilities and provide doctors with an additional instrument to evaluate the clinical situation of patients. After the application of correlation indicators and the use of principal component analysis for the reduction of the dimensionality of the feature space, classification algorithms have been applied and compared, in order to identify the best suiting one for our purposes. After the application of re-balance algorithms, the accuracy of the machine learning-based prediction system reaches 79%, demonstrating the capability of the framework to correctly predict future progression of disability.

GreenCharge is an European research project that aims at integrating and deploying innovative technologies and associated business models to facilitate the transition towards electro mobility (e-mobility). In this paper the GreenCharge approach for the evaluation of technology innovation is presented. Three Pilots will apply the proposed framework according to their specific requirements and objectives.

To run proper Big Data Analytics, small and medium enterprises (SMEs) need to acquire expertise, hardware and software, which often translates to relevant initial investments for activities not directly connected to the company's business. To reduce such investments, the TOREADOR project proposes a Big Data Analytics framework which supports users in devising their own Big Data solutions by keeping the inherent costs at a minimum, and leveraging pre-existent knowledge and expertise. Among the objectives of the TOREADOR framework is supporting developers in parallelizing and deploying their Big Data algorithms, in order to develop their own analytics solutions. This paper describes the Code-Based approach, adopted within the TOREADOR framework to parallelize users' algorithms and deploy them on distributed platforms, via the annotation of parallelizable code portions with parallelization primitives. The approach, which relies on the guidance of Parallel Patterns to implement the parallelization, and on Skeletons to automatically build execution and deployment templates, is realized through a source-to-source Compiler, also described in the present paper.

The highly heterogeneous environment of IoT (Internet of Things) and Smart cities guarantees that every stakeholder's requirements can be achieved and implemented. However deciding which is the most appropriate sensor to adopt among various similar ones and, even most important, how to use it is not an easy task; a task that, as of today, cannot be done without access to source code or documentation. Moreover heterogeneity in descriptions and definitions complicate sensor interfaces comparison and selection, and may create interoperability and portability problems among multiple providers. In this work we aim at providing a Q&A tool that guides the user in the production of a RESTApi sensor's interfaces according to the OpenAPI specification. The automatically produced ad-hoc specification is used as a base for defining sensors' interfaces equivalence and similarity using natural language processing techniques.

During the past years the Cloud Computing offer has exponentially grown, with new Cloud providers, platforms and services being introduced in the IT market. The extreme variety of services, often providing non uniform and incompatible interfaces, makes it hard for customers to decide how to develop, or even worse to migrate, their own application into the Cloud. This situation can only get worse when customers want to exploit services from different providers, because of the portability and interoperability issues that often arise. In this paper we propose a uniform, integrated, machine-readable, semantic representation of cloud services, patterns, appliances and their compositions. Our approach aims at supporting the development of new applications for the Cloud environment, using semantic models and automatic reasoning to enhance potability and interoperability when multiple platforms are involved. In particular, the proposed reasoning procedure allows to: perform automatic discovery of Cloud services and Appliances; map between agnostic and vendor dependent Cloud Patterns and Services; automatically enrich the semantic knowledge base.

Nowadays, most of the information available on the web is in Natural Language. Extracting such knowledge from Natural Language text is an essential work and a very remarkable research topic in the Semantic Web field. The logic programming language Prolog, based on the definite-clause formalism, is a useful tool for implementing a Natural Language Processing (NLP) systems. However, web-based services for NLP have also been developed recently, and they represent an important alternative to be considered. In this paper we present the comparison between two different approaches in NLP, for the automatic creation of an OWL ontology supporting the semantic annotation of text. The first one is a pure Prolog approach, based on grammar and logic analysis rules. The second one is based on Watson Relationship Extraction service of IBM Cloud platform Bluemix. We evaluate the two approaches in terms of performance, the quality of NLP result, OWL completeness and richness.

The development of a complex software system involves many actors, whose skills and knowledge are very heterogeneous. The requirements representation made using the Business Process notations is far more understandable than the classic UML representation, at least for Business experts, as it allows to investigate the system from other points of view which are not merely connected the development of the software itself. However, it is not possible to completely disregard UML representations, as they catch software design information which would be lost otherwise. In order to use both representations as basic tools through the whole software development life cycle, a key point is to ensure that they can be easily combined together to facilitate the development of the system software assets. In this paper we address this interoperability scope and provide a unified semantic representation, capable of covering every aspect of software development life cycle and of bonding the different Business and Software development points of view, from the requirements definition to the actual implementation of the source code, including the migration of applications to the Cloud.

The potential of patient-centred care and a connected eHealth ecosystem can be developed through socially responsible innovative architectures. The purpose of this paper is to define key innovation needs. This is achieved through conceptual development of an architecture for common information spaces with emergent end-user applications by supporting intelligent processing of measurements, data and services at the Internet of Things (IoT) integration level. The scope is conceptual definition, and results include descriptions of social, legal and ethical requirements, an architecture, services and connectivity infrastructures for consumer-oriented healthcare systems linking co-existing healthcare systems and consumer devices. We conclude with recommendations based on an analysis of research challenges related to how to process the data securely and anonymously and how to interconnect participants and services with different standards and interaction protocols, and devices with heterogeneous hardware and software configurations.

Summary The quota of renewable energy production from decentralized sources is increasing even more, affecting the management of current power grids. The lack of alignment between energy produced by photovoltaic panels and the consumption by user's appliances are the main issues that need to be addressed. The usage of energy storages is not yet feasible because of dimensioning problems and costs. The CoSSMic project is developing an innovative autonomic distributed platform that adopts a collaborative approach and distributed knowledge to schedule the usage of consuming devices according to user's preferences and constraints, monitoring data and predicted photovoltaic production. The main objective is the maximization of self‐consumption in a neighborhood. Multi‐agent systems are a good candidate for modeling and managing such kind of systems. In fact, smart grids are geographically distributed systems composed of autonomous and reactive entities among which some are pro‐active and have social abilities. In this paper, we focus on the design and prototype implementation of the multi‐agent systems, that is one of the pillars of the CoSSMic platform, aiming at implementing a distributed scheduler. Copyright © 2016 John Wiley & Sons, Ltd.

In the present work, we describe a procedure and a prototype implementation for the automatic recognition of design patterns from documentation of software artefacts design and implementation, provided in a machine readable form, namely, the XML Metadata Interchange (XMI) coded representation of UML class diagrams. The procedure exploits a semantic representation of the patterns to be recognized, based on an existing Web Ontology Language (OWL), known as object design ontology layer (ODOL), defined by the University of Massey (New Zealand), which has been augmented with an OWL-S based representation of the patterns' dynamic behaviour. Both the UML set of diagrams related to the analysed software artefacts and the ODOL+OWL-S patterns representation are automatically scanned and translated into a first-order logic representation (namely Prolog). A set of first-order logic rules, independent from the specific pattern to be recognized, has been defined to describe the heuristics and features which trigger the recognition, exploiting the Prolog description of the patterns to be recognized and the base of Prolog facts, which represents the UML documentation. Copyright © 2015 John Wiley & Sons, Ltd.

One of the biggest advantages introduced by the Smart Grid is the ability to efficiently and simply integrate renewable energy sources that appear to be intermittent because of their dependence on phenomena that are not constant but that are an important energy supply if properly exploited. The realization of a smart grid, however, requires an infrastructure able to handle almost instantaneous bidirectional communication between each node of the network. All this involves the management of huge data flows in terms of analysis and control, requiring a structure capable of handling responses in a very short time and an high level of global analysis to prevent or minimize problems. Successful negotiation and brokering on the Energy Market is an important prerequisite for achieving the advantages of Smart Grid. In this paper we present a Market for the sale of energy using a completely distributed approach based on P2P protocols and server-less technology, providing a greater level of security by exploiting the properties of this type of networks. We propose a scenario animated by three actors: Consumer, Producer and Market Manager.

Cloud computing is driving formidable change in the technology industry and transforming how to do business in Europe and around the world. Despite the obvious advantages of cloud computing there are a number of issues that require solutions for Cloud computing to develop further. In particular challenges include (but are not limited to) lack of support for heterogeneous Cloud providers, lack of meaningful cross-platform Cloud resource descriptions, lack of lifecycle management of Cloud applications. In this paper we propose an overview of two solutions for cloud services description and orchestration, TOSCA (Topology and Orchestration Specification for Cloud Applications) and HOT (Heat Orchestration Template), a comparison among these two solution and examples of how these two solutions correlate with cloud patterns.

The Cloud Computing paradigm has been adopted in countless areas of application and forms the basis of a growing number of business cases. Similar to the situation with service providers in the 1980th, it becomes apparent that different Cloud providers build walled gardens around their offerings. While multiple projects and organizations are working on standards for federating Cloud domains, the scalable exchange of descriptions about heterogeneous resources are often not well considered. Our approach is to adopt both, ideas initially developed for the Internet to define a scalable architecture and concepts from the Semantic Web to define a canonical Intercloud ontology. An initial implementation of the architecture has been developed to form a basis for further refinement of the proposed concepts. As a result, we have defined an initial ontology for Intercloud resources and implemented a catalog for the IEEE Intercloud architecture.

Business Process management has attracted the attention of companies and organizations, which have heavily invested in the research and development of tools and standards for process representation and business requirement formalization. Several tools have been produced for the representation and management of Business processes, and many companies have designed their enterprise models using business process standards. In this paper we analyse different approaches to enrich the existing standards, in particular the Business Process Model Notation (BPMN), with the semantic information provided by OWL ontologies. We also discuss the motivations behind the need of a semantic annotation for Business Processes and provide an overview of existing research efforts and results.

Summary In this paper, we present an engine based on semantic technologies aimed at supporting cloud applications developers, in the tasks of discovering functionalities, APIs, and resources needed for the application development through semantic based agnostic (vendor independent) representations of such application components, and representation of generic programming concepts and patterns, including application domain related ones. It handles, maintains, and exposes to the user in a graphical way the semantic descriptions of application domain concepts, application‐related concepts, general design patterns and programming functionalities, specific API implementations and cloud resources, layering the knowledge base in four layers. The semantic engine is a component of the European project mOSAICs framework. Copyright © 2014 John Wiley & Sons, Ltd.

Virtualization technologies represent the basis of many software architectures and frameworks existing today, Cloud Computing being probably the most influenced paradigm in the current scenery. With the steady growth of new Cloud and Virtualization platforms, which have caused the proliferation of many Cloud Services and Virtual appliances, it seems necessary to put order in the chaos of overlapping functionalities such services and appliances provide. A common representation of both Cloud Services and Virtual Appliances can be the perfect starting point to define a functional classification that could help customers choose the resources which best adhere to their objectives. A semantic based representation, implemented through an OWL [1] ontology, is probably the best candidate for this purpose, thanks to the flexibility of the OWL language and the possibility to navigate the ontology using both visual tools and query languages.

Software certification is one of the major issue in the aeronautical domain. Therefore, in order to support the development of certifiable software components, Text Analysis has been adopted to support the quality assurance team to quick define a map of applicable standards and guidelines to accomplish certification needs. A specific certification tool has been implemented in order to support the search of certification material closer to a certifiable industry product, reducing the development effort of innovative product.

The increasing demand for energy and the availability of several solutions of renewable energy sources has stimulated the formulation of plans aiming at expanding and upgrading existing power grids in several countries. According to NIST, smart grid will be one of the greatest achievements of the 21st century. By linking information technologies with the electric power grid to provide electricity with a brain, the smart grid promises many benefits, including increased energy efficiency, reduced carbon emissions, and improved power reliability. In this paper we present an agent based architecture for supporting collection and processing of information about local energy production and storage resources of neighborhoods of individual houses and to schedule the energy flows using negotiation protocols.

This paper focuses on two aspects related to the widespread of cloud computing: first, the definition of a common formalism, based on a unique and shared model, which could be used to completely describe Cloud patterns, second, the investigation of a methodology to automatize the recognition of similar elements in Design and Cloud patterns, thus defining an automatic mapping among representations of application that follow these patterns. These aspects are handled on one hand by extending an existing semantic based Design pattern language in order to properly represent both Design and Cloud patterns together with its possible related architectural implementations and on the other hand by using these agnostic representations to define a mapping between Design patterns (a single design pattern or a composition of pattern) and Cloud patterns, in order to obtain a clear model of the cloud applications which could satisfy its original requirements and exploit all the benefits of cloud computing at the same time.

The transition to cloud computing requires a certain effort since the development of applications for the Cloud requires programming skills and knowledge about the several programming models, APIs provided by Cloud vendors and cloud patterns to fit the cloud environment. Due to the lack of sharing programming model and interface also the porting of application among clouds requires in most case the rewriting of the whole application according to the new provider interface and programming model. The growing number of cloud providers that deploy their offers led to increase more and more the need to automate some discovery mechanisms and alignment facilities. In this paper a possible solution to the above stated problem is proposed through the application of semantic and matchmaking technologies. In particular we want to describe the Dynamic Discovery and Mapping prototype, developed within the EU funded mOSAIC project (http://www.mosaic-cloud.eu), to provide the discovery of new Cloud services (dynamically) deployed by providers. The Dynamic Discovery and Mapping Service' target is to discover Cloud providers' functionalities and resources, compare and align to other provider or agnostic API, thus supporting agnostic and interoperable access to Cloud providers' offers.

Here we describe a procedure and a prototype implementation for the automatic recognition of Design Patterns from documentation of Software Artefacts' design and implementation, provided in a machine readable form, namely the XMI coded representation of UML diagrams. The procedure exploits a semantic representation of the patterns to be recognized, based on the ODOL ontology defined by the University of Massey (New Zealand) [12], which we have augmented with an OWL-S based representation of the patterns' dynamic behaviour. Both the UML set of diagrams related to the analysed Software Artefacts and the ODOL+OWL-S patterns' representation are automatically scanned and translated into a first order logic representation (namely Prolog). A set of first order logic rules, independent from the specific pattern to be recognized, have been defined to describe the heuristics and features which trigger the recognition, exploiting the Prolog description of the patterns to be recognized and the base of Prolog facts which represents the UML documentation.

This paper describes a semantic based representation for Design Patterns, defined on the base of the formal pattern language known as ODOL, which has been corrected and extended to better represent both structural and behavioural aspects of pattern descriptions. In particular, new concepts have been added to the underlying OWL ontology to enrich the structural description of patterns, while OWL-S has been adopted to describe patterns' behaviour by representing participants' methods as services. Our goal is to define a new, flexible and easily extendible pattern representation, which could be used to describe all aspects of Design Patterns and could be also applied to cloud computing, in particular to the description of Cloud Patterns. We provide details on the defined language and considerations on its ability to describe both Design and Cloud Patterns.

Cultural heritage often fails to represent a successful attraction because it is not able to capture the interest of tourists. Sometimes the presentation of an archaeological site cannot be improved because of legal or environmental constraints. Sometimes it is really difficult to recognize the relevant details or to understand its significance. A new way of exploiting archaeological sites in a more informative and intuitive way is needed. It is necessary to provide people with information and contents, for understanding the world around them, and with the possibility to interact with those places or objects in real time. Image recognition and analysis is a promising technology that can be applied in this field. In this paper we use in indoor, but also in outdoor scenarios, image recognition for the content discovery service. In particular search by sample facilities are speed-up and improved using a position based filtering. Moreover it is used to extract semantic from subjects in order to personalize the discovery and recommendation according to the user's profile.

Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing solutions are currently used in settings where they have been developed without addressing a common programming model, open standard interfaces, adequate service level agreements or portability of applications. Neglecting these issues current Cloud computing offers force people to be stranded into locked, proprietary systems. Developers making an effort in migrate to the cloud their applications cannot port them elsewhere. Cloud application portability is a concept in cloud computing that refers to the ability to move applications between cloud vendors with a minimum level of integration issues. This paper investigates an approach, based on automatic information extraction and semantic matchmaking, to analyze application programming interfaces and support application migration among different cloud solution.

In this paper we propose a Semantic Engine for Cloud applications development: a semantically enabled Search Engine that can be used by cloud applications developers as development support. The Semantic Engine will be a component of the EU project mOSAIC's architecture. The Semantic Engine supports the application developer with semantic discovery of mosaic Cloud API functionalities and Components at development time and resources at deployment time. It manages semantic descriptions of the resources and available mosaic elements. It will be the system supporting the user in selecting APIs components and functionalities needed for building the application, and the list of needed resources to be acquired from the Cloud providers.

Cloud computing represents an opportunity for IT users to reduce costs and increase efficiency providing an alternative way of using IT services. In this scenario value added services for dynamic and elastic provisioning play an important role, by giving the possibility to get the best resources configuration that satisfies the application requirements. Agent technology provides asynchronous mechanisms that could represent the best choice for effective programming of Cloud, due to the unpredictable behaviour of the network. Cloud Agency is a collection of agent based services for provisioning, monitoring and autonomic reconfiguration of Cloud resources at infrastructure level, which go beyond the common offer by commercial providers and by open Cloud technologies. In this work we describe how Cloud Agency extends the Open Cloud Computing Interface (OCCI\cite{OCCICORE}) proposal of standard for the Cloud infrastructures level. We present the Cloud agency design and the implementation of an Restful to/from ACL gateway that allows for the communication between the Cloud world and the agents' one, being compliant with OCCI and extending its model and services.

The development of applications for the Cloud requires programming skills and knowledge about the several programming models, APIs and underlying infrastructures, which are provided by Cloud vendors. The European Project mOSAIC aims at developing an API, Platform and a set of tools to facilitate language and platform agnostic application development and deployment on a variety of Infrastructures as a Service offers. Within the mOSAIC project, appropriate ontologies, a knowledge base and an associated Semantic Engine [10] have been developed to support the Cloud application developer in the tasks of discovering the needed functionalities and resources for application development through vendor independent representations of such application components, and representation of generic programming concepts and patterns, including application domain related ones. In this paper the use of the Semantic Engine, its ontologies and knowledge base is illustrated by following the design and implementation of an application for analysis of structures under static loading that is based on the Finite Element Method.

Mobile devices can be used to improve the exploitation of reality by the user in pervasive environments. In this paper we present a software framework for augmenting the user's perception of reality by innovative applications and services. Mobile applications enable handheld devices to get information from pervasive sensors, as their own perceptors, in order to let the users benefit of context awareness. Furthermore they exploit information from the field to improve delivery of ubiquitous services and presentation of multimedia contents. We implemented tools to support experts in the domain of the Cultural Heritage to augment the archaeological site with these kind of services. Besides we developed a tourist guide that supports user's visit and enhances his cultural experience by an intelligent discovering and delivery of those information, which are relevant to the user's profile and to the environment conditions. In our approach software agents adapt themselves in response to a changing environment and are able to respond differently, creating entirely new plans, and thus changing their own behavior depending on their perceptions.

Many contexts of every-day life are characterized by pervasiveness and ubiquity of embedded systems. These features can be exploited to support people in different scenarios, such as emergency situations and post disaster management. In this paper, we propose an agent-based approach to assist and simulate human critical activities in such scenarios. We propose a rule-based solution, which is implemented as a decentralized solution, which delegates the verification of rules to independent mobile agents. Agents are software components that interact with neighboring objects and react on the basis of the surrounding context. Moreover, a simulation tool is presented. It is used to model the remote scenario by collecting of context information from the field. It supports the on-line monitoring of the ongoing activities and the prediction of future situations. We present an implementation of the proposed solution to support and supervise human activities in real scenarios.

The cloud paradigm appeared on the computing scene in 2005 with the Amazon Elastic Compute Cloud (EC2). After this date, a large set of related technologies has been developed. In the academic world, and especially in the HPC area, cloud computing is in some way in competition with the GRID model, which offers a middleware based approach. One of the solutions proposed is the integration of the two paradigms, in order to use the enormous potential of the existent computational GRIDs in new ways. One of the most diffused problems on this systems is the choice of the correct programming paradigm: many different approaches exist and it is difficult to define which is the approach that best fit with the cloud paradigm. In this paper we propose the integration of a Cloud on GRID architecture with a mobile agent platform. The architecture we propose offer Virtual clusters with full administrative control to final users, adopting an existent GRID architecture and especially its security infrastructure. The mobile agent platform is able to dynamically add and configure services on the virtual clusters. The experience here presented shows that the mobile agent paradigm well fulfills the dynamic properties of the Cloud paradigm and could be a good choice to simply develop application and services able to dynamically adapt themselves to the virtualized environment.

Emerging computing architectures exploit parallel execution units for performances improvements in programs executions. Recent advances in microprocessors and multicomputers stressed the need of methodologies able to take advantage of parallelism. Source code analysis techniques exist which are used to identify chunks of code for parallelization purposes, but they are not designed to identify complex algorithmic patterns. In this work a methodology for identification of complex patterns is described. It is based on semantic description of patterns, which allows not only for reasoning, but also for providing an easy way to produce and extend algorithmic patterns definitions. A comparison of this approach with an attribute grammar language for patterns description is also reported.

The resource allocation problem is a classical problem in the scientific literature. Here, authors present an architectural model to allow optimum resource allocation in a grid environment. They are investigating a decentralized approach to address this problem into an inter--grid nodes context; instead they argue in favor of a centralized approach in an intra--grid node context (i.e., a cluster architecture). Furthermore, authors aim to implement integration between multi-agent and grid systems, in order to deal with the execution of agent-based applications in a grid environment. The paper focuses on a condor--based solution for resource to agent allocation in an intra--grid node context. The prototypal implementation is described, which has been integrated in grid middleware as grid service.

Welcome to the 10th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC-09), held on 29 May 2009 in Rome, Italy, in conjunction with the 23rd IEEE Int. Parallel and Distributed Processing Symposium (IPDPS 2009).

Web 2.0 changes the role of Web users from information Web consumers to Web participants. In fact, when information is delivered, users' interactions modify data related information, making users authors themselves. Data acquire a new identity, each time they are used or modified according one's goal. They do not depend anymore on their producer and on the context where they have been published, but they are self-contained. We present here an integrated architecture, that exploits data related information, provided as XML metadata for storing, retrieval and delivery of multimedia contents. Metadata describe semantic, data organization, geographic reference, ... . A prototype has been implemented to experiment the integration of an original service for content adaptation to client profile. We focus here on platform design, implementation and on service architecture. We realized a significant case study for semantic and location based information retrieval to demonstrate the effectiveness of the proposed approach.

In this paper we investigate the feasibility of integration of two different programming models in order to exploit the advantages of each one and to overcome their weakness in a heterogeneous environment made of desktop networks and Virtual Organizations sharing powerful Grid platforms. We present a possible approach that will be exploited to integrate two already available frameworks (DG-ADAJ and MAgDA), which adopt respectively the Distributed Components and the Mobile Agents models to support distributed programming.

Mobile agents programming paradigm is an emerging approach for distributed computing, extremely suitable for mobile systems because of its adaptability to exploit the available resources. Optimization of mobile agents applications and system configuration are relevant above all when we deal handheld devices with limited capabilities. Classical approaches are hard to apply because new kinds of interaction facilities provided by agent platforms, such as cloning and migration, represent an additional software layer that affects the system performances. In this context identification and estimation of performance indexes are necessary to evaluate and foresee system dependability. In fact performance evaluation is exploited to address different issues according to which, different measurements are required. In practice, there is a need for tools and techniques for evaluation of the performances of the adopted mobile agent platforms. Related work proposes a set of performance indexes, slightly different one from the other; and measured using different approaches. We present here a framework that aims at supporting the development of ad-hoc solutions based on measurement agents. Framework architecture, prototype implementation and case studies and preliminary performance figures are presented in the following.

Abstract This paper describes a simulation‐based technique for the performance prediction of message‐passing applications on cluster systems by means of benchmark data. Given data measuring the performance of a target cluster in the form of standard benchmark results, along with the details of the chosen computing configuration, it is possible to build and to validate automatically a detailed simulation model. This makes it possible to predict off‐line, i.e. without resorting to the real hardware, the performance of fully developed or even of skeletal code. An XML‐based language (MetaPL) is adopted to describe the application behavior in the development stage. After a description of the approach and the illustration of the construction and validation of the simulation model, the paper presents a case study. Copyright © 2006 John Wiley & Sons, Ltd.

The integration of Web services and software agents brings new opportunities and helps in defining new kinds of services too. A very interesting approach comes from the adoption of "mobile agents" to design and develop a service oriented architecture that supports the access to distributed services by heterogeneous handheld devices. This paper describes the functional model and the architecture design of a platform for services composition and delivery. Mobile agents are able to access and compose services accessing them by standard interfaces. User authentication, service discovery and publication, personalization are other provided facilities. Technological interoperability is provided by Web services technology. Code mobility is exploited to reconfigure the server platform moving agents to the available nodes, or to reconfigure the user devices providing it with the needed client software

Parallel Skeletons have been proposed as a possible programming model for parallel architectures. One of the problems with this approach is the choice of the skeleton which is best suited to the characteristics of the algorithm/program to be developed/parallelized, and of the target architecture, in terms of performance of the parallel implementation. Another problem arising with parallelization of legacy codes is the attempt to minimize the effort needed for program comprehension, and thus to achieve the minimum restructuring of the sequential code when producing the parallel version. In this paper we propose automated Program Comprehension at the algorithmic level as a driving feature in the task of selection of the proper Parallel Skeleton, best suited to the characteristics of the algorithm/program and of the target architecture. Algorithmic concept recognition can automate or support the generation of parallel code through instantiation of the selected Parallel Skeleton(s) with template based transformations of recognized code segments.

In the past few years, increasing interest has been shown in using Java as a language for performance-oriented distributed and parallel computing. Most Java-based systems that support portable parallel and distributed computing either require the programmer to deal with intricate low level details of Java which can be a tedious, time-consuming and error-prone task, or prevent the programmer from controlling locality of data. In contrast to most existing systems, JavaSymphony - a class library written entirely in Java - allows to control parallelism, load balancing and locality at a high level. Objects can be explicitly distributed and migrated based on virtual architectures which impose a virtual hierarchy on a distributed/parallel system of physical computing nodes. The concept of blocking/nonblocking remote method invocation is used to exchange data among distributed objects and to process work by remote objects. We evaluate the JavaSymphony programming API for a variety of distributed/parallel algorithms which comprises backtracking, N-body, encryption/decryption algorithms and asynchronous nested optimization algorithms. Performance results are presented for both homogeneous and heterogeneous cluster architectures. Moreover, we compare JavaSymphony with an alternative well-known semi-automatic system.

Abstract In this paper we address the issue of workload decomposition in programming hierarchical distributed‐shared memory parallel systems. The workload decomposition we have devised consists of a two‐stage procedure: a higher‐level decomposition among the computational nodes; and a lower‐level one among the processors of each computational node. By focusing on porting of a case study particle‐in‐cell application, we have implemented the described work decomposition without large programming effort by using and integrating the high‐level language extensions High‐Performance Fortran and OpenMP. Copyright © 2002 John Wiley & Sons, Ltd.

A crucial issue in programming hierarchical distributed-shared memory systems is the workload decomposition. In this paper we address this issue in the framework of porting typical particle in cell (PIC) applications on hierarchical distributed-shared memory parallel systems. The workload decomposition we have devised consists in a two-stage procedure: a higher-level decomposition among the computational nodes, and a lower-level one among the processors of each computational node. We have implemented the described work decomposition without large programming effort by using and integrating the high-level languages High Performance Fortran and OpenMP.

The main features of gridless finite-size-particle codes are examined and compared with those of particle-in-cell ones, from the point of view of the performances that can be obtained, with respect to both the spatial-resolution level and the efficiency of parallel particle simulations. The particle-decomposition parallelization strategy is discussed, and its implementation in the framework of the High Performance Fortran paradigm is presented. It is shown that such codes are particularly suited for particle-decomposition parallelization on distributed-memory architectures, as they present a strong reduction, in comparison with particle-in-cell codes, of the memory and computational offsets related to storing and updating the replicated fluctuating-field arrays.