Search

International audience; We consider a stochastic bipartite matching model consisting of multi-class customers and multiclass servers. Compatibility constraints between the customer and server classes are described by a bipartite graph. Each time slot, exactly one customer and one server arrive. The incoming customer (resp. server) is matched with the earliest arrived server (resp. customer) with a class that is compatible with its own class, if there is any, in which case the matched customer-server couple immediately leaves the system; otherwise, the incoming customer (resp. server) waits in the system until it is matched. Contrary to classical queueing models, both customers and servers may have to wait, so that their roles are interchangeable. While (the process underlying) this model was already known to have a product-form stationary distribution, this paper derives a new compact and manageable expression for the normalization constant of this distribution, as well as for the waiting probability and mean waiting time of customers and servers. We also provide a numerical example and make some important observations.

Internet of Things (IoT) is a well established approach used for the implementation of surveillance systems that are suitable for monitoring large portions of territory. Current developments allow the design of battery powered IoT nodes that can communicate over the network with low energy requirements and locally perform some computing and coordination task, besides running sensing and related processing: it is thus possible to implement edge computing oriented solutions on IoT, if the design encompasses both hardware and software elements in terms of sensing, processing, computing, communications and routing energy costs as one of the quality indices of the system. In this paper we propose a modeling approach for edge computing IoT-based monitoring systems energy related characteristics, suitable for the analysis of energy levels of large battery powered monitoring systems with dynamic and reactive computing workloads.

Authorship Attribution (AA) is currently applied in several applications, among which fraud detection and anti-plagiarism checks: this task can leverage stylometry and Natural Language Processing techniques. In this work, we explored some strategies to enhance the performance of an AA task for the automatic detection of false and misleading information (e.g., fake news). We set up a text classification model for AA based on stylometry exploiting recurrent deep neural networks and implemented two learning tasks trained on the same collection of fake and real news, comparing their performances: one is based on Federated Learning architecture, the other on a centralized architecture. The goal was to discriminate potential fake information from true ones when the fake news comes from heterogeneous sources, with different styles. Preliminary experiments show that a distributed approach significantly improves recall with respect to the centralized model. As expected, precision was lower in the distributed model. This aspect, coupled with the statistical heterogeneity of data, represents some open issues that will be further investigated in future work.

The faithful reproduction and accurate prediction of the phe-notypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extreme computational effort required. In this context, model reduction techniques and High-Performance Computing infrastructures could be leveraged to mitigate these issues. In addition, as cellular processes are characterized by multiple scales of temporal and spatial organization, novel hybrid simulators able to harmonize different modeling approaches (e.g., logic-based, constraint-based, continuous deterministic, discrete stochastic, spatial) should be designed. This chapter describes a putative unified approach to tackle these challenging tasks, hopefully paving the way to the definition of large-scale comprehensive models that aim at the comprehension of the cell behavior by means of computational tools.

In this paper we present a modeling approach suitable for practical evaluation of the delays that may affect security monitoring systems in (multitenant) cloud based architecture, and in general to support professionals in planning and evaluating relevant parameters in dealing with new designs or migration projects. The approach is based on modularity and multiformalism techniques to manage complexity and guide designers in an incremental process, to help transferring technical knowledge into modeling practice and to help easing the use of simulation. We present a case study based on a real experience, triggered by a new legal requirement that Italian Public Administration should comply about their datacenters.

Cloud based architectures present many challenges, ranging from poorly dimensioned hardware to lengthy wait times and resource balancing issues. Most of these problems can be mitigated by using proper performance evaluation techniques, depending on the accuracy of the model abstracting the real system. The adoption of a formalism over another to describe the considered infrastructure plays a crucial role in achieving this goal. In this sense, multiformalism proves itself to be a powerful modeling approach describing each system component according to the most suitable representation. This paper presents a novel modeling method oriented to the prediction of cloud architectures performance, suitable for joining the advantages of high level modeling abstractions and of the detail of a specialized simulator. Generalized Stochastic Petri Nets are used to describe the workload and the behavior of users and applications, while Cloudsim, a well known cloud infrastructure simulator, is adopted for the cloud part. A case study of a simplified Edge Computing application is presented to demonstrate the effectiveness of the proposed approach.

We applied a flexible modeling technique capable of representing dynamics of large populations interacting in space and time, namely Markovian Agents, to study the evolution of COVID-19 in Italy. Our purpose was to show that this modeling approach, that is based on mean field analysis models, provides good performances in describing the diffusion of phenomena, like COVID-19. The paper describes the application of this modeling approach to the Italian scenario and results are validated against real data available about the Italian official documentation of the diffusion of COVID-19. The model of each agent is organized similarly to what largely established in literature in the Susceptible-Infected-Recovered (SIR) family of approaches. Results match the main events taken by the Italian government and their effects.

Simulation of second-order fluid models requires specific techniques due to the continuous randomness of the considered processes. Things become particularly difficult when considering several dimensions, where correlation occurs, and classical concepts like absorption and reflection require specific extensions. In this work, we will focus on three different types of behaviors, with two correlations structures: either independence or total correlation. For the considered scenario, we will describe how to produce suitable traces of the underlying continuous stochastic process.

Blockchains provide a valid and profitable support for the implementation of trustable and secure distributed ledgers, in support to groups of subjects that are potentially competitors in conflict of interest but need to share progressive information recording processes. Blockchains prevent data stored in blocks from being altered or deleted, but there are situations in which stored information must be deleted or made inaccessible on request or periodically, such as the ones in which GDPR is applicable. In this paper we present literature solutions and design an implementation in the context of a traffic management system for the Internet of Vehicles based on the Pseudonymization/Cryptography solution, evaluating its viability, its GDPR compliance and its level of risk.

The COVID-19 emergency suddenly obliged schools and universities around the world to deliver on-line lectures and services. While the urgency of response resulted in a fast and massive adoption of standard, public on-line platforms, generally owned by big players in the digital services market, this does not sufficiently take into account privacy-related and security-related issues and potential legal problems about the legitimate exploitation of the intellectual rights about contents. However, the experience brought to attention a vast set of issues, which have been addressed by implementing these services by means of private platforms. This work presents a modeling and evaluation framework, defined on a set of high-level, management-oriented parameters and based on a Vectorial Auto Regressive Fractional (Integrated) Moving Average based approach, to support the design of distance learning architectures. The purpose of this framework is to help decision makers to evaluate the requirements and the costs of hybrid cloud technology solutions. Furthermore, it aims at providing a coarse grain reference organization integrating low-cost, long-term storage management services to implement a viable and accessible history feature for all materials. The proposed solution has been designed bearing in mind the ecosystem of Italian universities. A realistic case study has been shaped on the needs of an important, generalist, polycentric Italian university, where some of the authors of this paper work.

PurposeIn this paper, we depart from extant conceptualisations of knowledge translation mechanisms to examine projects as a way to achieve effective knowledge transfer. Our empirical analysis focused on a university–industry research project in the automotive industry.Design/methodology/approachThe empirical analysis was based on a qualitative investigation. We analysed material collected within a research project involving a partnership between two universities and Fiat-Chrysler Automotive (FCA), a multi-brand auto manufacturer with a product range covering several different market segments. We used three data collection techniques: internal document analysis, participant observation and semi-structured interviews.FindingsOur findings show that, in a U-I research project, goals represent a key dimension to support knowledge translation. Defining the goal implies an ongoing negotiation process, where researchers and company employees work together, in order to converge towards a shared meaning of the goal. In this sense, goal orientation and goal-based interaction have significant implications for knowledge translation processes.Originality/valueStudies to date have focussed on the concept of knowledge translation as a way to contextualise the transfer from the source of knowledge to the receiver and to interpret the knowledge to be exchanged. This study expands the understanding of knowledge translation mechanisms in university–industry research settings. It investigates the concept of projects as powerful knowledge translation mechanism in a dynamic and longitudinal perspective. Our contribution provides insight, reflecting on how the use of projects may represent a way to facilitate knowledge transfer and build up new ideas and solutions.

The role of remote resources, such as the ones provided by Cloud infrastructures, is of paramount importance for the implementation of cost effective, yet reliable software systems to provide services to third parties. Cost effectiveness is a direct consequence of a correct estimation of resource usage, to be able to define a budget and estimate the right price to put own services on the market. Attacks that overload resources with non legitimate requests, being them explicit attacks or just malicious, non harmful resource engagements, may push the use of Cloud resources beyond estimation, causing additional costs, or unexpected energy usage, or a lower overall quality of services, so intrusion detection devices or firewalls are set to avoid undesired accesses. We propose the use of Generative Adversarial Neural Networks (GANs) to setup a method for shaping request based attacks capable of reaching resources beyond defenses. The approach is studied by using a publicly available traffic data set, to test the concept and demonstrate its potential applications.

Complexity of current computer networks, including e.g., local networks, large structured networks, wireless sensor networks, datacenter backbones, requires a thorough study to perform analysis and support design. Simulation is a tool of paramount importance to encompass all the different aspects that contribute to design quality and network performance (including as well energy issues, security management overheads, dependability), due to the fact that such complexity produces several interactions at all network layers that is not easily modellable with analytic approaches. In this systematic literature review we aim to analyze, basing our investigation on available literature, the adoption of a popular network simulator, namely ns-3, and its use in the scientific community. More in detail, we are interested in understanding what are the impacted application domains in which authors prefer ns-3 to other similar tools and how extensible it is in practice according to the experience of authors. The results of our analysis, which has been conducted by especially focusing on 128 papers published between 2009 to 2019, reveals that 10% of the evaluated papers were discarded because they represented informal literature, most of the studies presented comparisons among different network simulators, beyond ns-3 and conceptual studies related to performance assessment and validation and routing protocols. Only about 30% of considered studies present extensions of ns-3 in terms of new modules and only about 10% present effective case studies demonstrating the effectiveness of employing network simulator in real application, except conceptual and modeling studies.

Different paradigms have been used to model the behaviour of pedestrians in a crowd. While some approaches analyze the modelization of crowd motion from a physical perspective, other techniques prefer to focus on similar abstractions following classic paradigms, such as mobile agents or cellular automata, or even from the point of view of social forces interaction. Particular interest has emerged around the topic of crowd behaviour under condition of stress caused by a dramatic event, such as an earthquake, a terrorist attack or a fire. This paper presents a novel approach including a GSPN to describe the behaviour of an individual, which is subsequently translated into a set of Markovian Agents. The model under study reproduces a scenario where a fire, developed in a closed environment, triggers erratic crowd behaviour. It is possible to perceive that a panic situation can be mitigated by enforcing certain measures involving human leaders or small embedded systems (such as Internet-of-things enabled devices), reducing the level of risk related to safety.

Autonomic routers are the main component on which autonomic networking is founded. Our goal is to provide a first approach performance modeling method that can be usable by networking professionals that are not part of the Performance Evaluation community.

Additive Manufacturing (AM) is a modern manufacturing approach and it is considered one of the key enabling technologies of Industry 4.0 paradigm. The attractive feature of this technology concerns, mainly, the possibility to build geometrically complex parts in a single step avoiding all the stages of a traditional manufacturing process and, therefore, reducing the production lead time. Originally, this technology was intended for the rapid prototyping. Nowadays the integration of AM in the Traditional Practical Context (TPC) continues to expand and it is emerging as a valid alternative for the production of low size batches that consist of customized products. This work points out the open issues about the spread of AM technologies, especially, in the production of metal parts. Simulation is a strategic method to evaluate the changes caused by the inclusion of this disruptive technology in a conventional shop floor. The Analytic Hierarchy Process (AHP) is used as decision making approach to prioritize the simulation techniques.

During recent years, industrial world faced transformations that led companies to introduce the concepts and the technologies of a new industrial paradigm, named Industry 4.0 (I4.0), in order to improve their products and their production processes. In the context of I4.0, factories are becoming smart, more flexible and collaborative, satisfying the current demands of increasingly competitive markets and of products closer to the real needs of customers. Within this framework, the approach to product and process design is changing too, supported by the use of complex numerical analyses for testing and validating the performance of both products and production processes. This is made possible thanks to the Digital Manufacturing (DM) approach, that allows to reduce the design times and to validate the design solution in virtual environments, without setting up detailed and expensive experimental sessions. This paper aims to propose a possible methodological framework to better carry out the design of new products and production systems, according to the DM principles.

Analysis and simulation of complex systems are facilitated by the availability of appropriate modeling formalisms and tools. In many cases, no single analysis and modeling method can successfully cope with all aspects of a complex system: a multi-formalism multi-solution approach is very appealing, since it offers the possibility of applying the most suitable formalisms and solution techniques to model and analyze different components or aspects of a system. Another important feature that a successfull modeling approach should include is the possibility of reusing (sub)models: by composing parameterized submodels and then instantiating the parameters, complete models of different scenarios can be obtained and analyzed. This paper introduces an innovative approach to multi-formalism modeling of systems that is part of the OsMoSys (Object-based multi-formaliSm MOdeling of SYStems) framework. OsMoSys uses the proposed modeling approach to build multi-formalism models, and workflow management to achieve multi-solution. Our modeling approach is based on meta-modeling, allowing to easily define and integrate different formalisms, and on some concepts from object orientation. Its main objectives are the interoperability of different formalisms and the definition of mechanisms to guarantee the flexibility and the scalability of the modeling framework.

This chapter focuses on the reciprocal relationship between information systems and organizational control. It introduces the volume, describing both the theoretical frameworks and the topics analyzed and discussed. In particular, the volume is divided into three sections, each one focusing on a specific topic: organizational control, accounting and information systems; organizational change, innovation and ICT; and information, knowledge and project management practices.

The fundamental need for models in every field of design stems from the absolute and essential necessity for complexity domination. The prediction and verification competence is a must-have ability to allow an efficient and effective design cycle, as much as the complexity of the requirements, in specification, extension or criticalness of the design object, grows. Researchers and practitioners have developed different modeling languages to suit the needs of special classes of problems, such as general formalism like Fault Trees for dependability and availability models, several Petri Nets variants for performance and correctness evaluation, or dedicated modeling languages that map model elements onto domain entities. Richness and variety of formalisms, each of which tailored on a specific problem, empowers the modeling process, but also results in the explosion of many different submodels when coping with a complex and articulated system. These models are logically correlated and are used to take local decisions about single aspects of the overall problem. In practice they might not be aligned to each other and the mutual influences connecting them could be lost, compromising the general results. Multiformalism is an approach to modeling that aims to enable modelers to exploit together different formalisms for different aspects of the same system, while keeping the coherence and the mutual influences of the different parts of the overall model. Different concepts and interpretation of multiformalism modeling (and related problems, such multiparadigm and multisolution) have been experimented and are documented in literature. In this chapter, the main ideas and results in the field are introduced, together with an analysis and comparison.

The availability of modeling languages that best suit the needs of modelers from case to case can enhance the applicability and immediateness of a model-based approach in the design and evaluation of systems, and can meet the modelers’ expertise and customs. The goal of SIMTHESys is to enable the design and implementation of user-defined modeling languages (dubbed formalisms) and their native integration in multiformalism models, so that it is possible to develop language enhancements for existing formalisms, domain-oriented languages, extensions for existing languages, and abstraction tools to empower the modeling process. The key of this feature relies on the SIMTHESys metamodel and the description framework that is founded onto it. This chapter presents the foundation of SIMTHESys formalisms and demonstrates the possibilities it offers by examining a complete modeling example of a classical problem and a number of different formalisms, chosen to suggest how to exploit the framework.

Technological development and market expansion offer an increased availability of resources and computing power on IoT nodes at affordable cost. The edge computing paradigm allows keeping locally on the edge of the network a part of computing, while keeping all advantages of the cloud and adding support for privacy, real-time and network resilience. This can be further improved in IoT applications by exibly harvesting resources on IoT nodes, by moving part of the computing tasks related to data from the edge server to the nodes, raising the abstraction level of the data aspects of the architecture and potentially enabling larger IoT networks to be efficiently deployed and managed, in a stand-alone logic or as a component of edge architecture. Anyway, an e_cient energy management mechanism is needed for battery powered IoT networks, the most exible implementations, that dynamically balances task allocation and execution in order to In this paper we present a fuzzy logic based power management strategy for IoT subsystem that aims at maximizing the duration of the network by locally migrating part of the computing tasks between nodes. As our goal is to enable the deployment of semi-autonomic large IoT networks, our proposal does not rely on external resources for migration control and operates on a local basis to ensure scalability: at the best of our knowledge, this differentiates our proposal with respect to similar solutions available in literature.