1,721,022 research outputs found
A model-driven methodology to evaluate performability of metro systems
No full textMetro systems are required to continuously achieve acceptable levels of reliability, availability, maintainability, and performance (performability) in order to comply with the target values reported in operation and maintenance contracts. These requirements are regulated by several international standards that control the lifecycle defining both processes, documentation flows, and enabling techniques, aiming at controlling disturbances on service performed by the system. This chapter focuses on a complete modeldriven methodology with the aim to support the performability evaluation of a metro system during design and In-Service phases, as well as requirements assessment. In detail, the methodology allows the automatic generation of those formal models required for performability analysis, specialized according to the specific track layout and the defined operational strategies. The proposed methodology is perfectly coherent with the European Standard CENELEC EN 50126 and it allows the generation of all the technical reports needed in the related documentation
Automatic Resource Allocation for High Availability Cloud Services
No full textAbstractThis paper proposes an approach to support cloud brokers finding optimal configurations in the deployment of dependability and security sensitive cloud applications. The approach is based on model-driven principles and uses both UML and Bayesian Networks to capture, analyse and optimise cloud deployment configurations. While the paper is most focused on the initial allocation phase, the approach is extensible to the operational phases of the life-cycle. In such a way, a continuous improvement of cloud applications may be realised by monitoring, enforcing and re-negotiating cloud resources following detected anomalies and failures
Software Verification and Validation of Safe Autonomous Cars : A Systematic Literature Review
No full textAutonomous, or self-driving, cars are emerging as the solution to several problems primarily caused by humans on roads, such as accidents and traffic congestion. However, those benefits come with great challenges in the verification and validation (V&V) for safety assessment. In fact, due to the possibly unpredictable nature of Artificial Intelligence (AI), its use in autonomous cars creates concerns that need to be addressed using appropriate V&V processes that can address trustworthy AI and safe autonomy. In this study, the relevant research literature in recent years has been systematically reviewed and classified in order to investigate the state-of-the-art in the software V&V of autonomous cars. By appropriate criteria, a subset of primary studies has been selected for more in-depth analysis. The first part of the review addresses certification issues against reference standards, challenges in assessing machine learning, as well as general V&V methodologies. The second part investigates more specific approaches, including simulation environments and mutation testing, corner cases and adversarial examples, fault injection, software safety cages, techniques for cyber-physical systems, and formal methods. Relevant approaches and related tools have been discussed and compared in order to highlight open issues and opportunities
μGRIMOIRE: A Tool for Smart Micro Grids Modelling and Energy Profiling
No full textThe construction of a usable, formal, and extensible modeller and simulator for Smart Energy Grids is of a paramount importance in the industrial settings. Final users are interested in deploying effective smart-home configurations able to satisfy energy requests in the most economical way. Hence, a tool able to forecast both energy consumption and related costs of a smart-home configuration is needed. In this paper, the μGRIMOIRE (micro GRId MOdelling envIRonmEnt) toolset is presented. This tool is based on the well-known model-driven paradigm and its successful applications in the generation of formal/quantitative models for complex systems. By using a Domain Specific Modelling Language, a final user can define a smart-home system configuration and energy saving logics. Then, the tool offers the possibility of evaluating the desired user metrics by translating the model into a Fluid Stochastic Petri Net model representing both discrete and continuous variables
Petri Net based Evaluation of Energy Consumption in Wireless Sensor Nodes
No full textWireless Sensor Networks have proven their capability to deal with problems where wide and hardly accessible areas need to be monitored. Among the other systems there are also sensor networks in which nodes can (voluntarily) modify their positions to better adapt to changes of monitored phenomenon. One of the major issues arising in these situations is the energy consumption: as all the movements affect the batteries lifetime, the life of a sensor can be extended by equipping the device with power generators that exploit renewable sources, albeit this solution does not always avoid a full battery discharge. In this paper a Fluid Stochastic Petri Net modelling framework is presented to provide a wide evaluation of all the factors that contribute to the energy dissipation in mobile wireless sensor nodes. The framework allows the generation of extensible and composable models capable to evaluate the energy consumption due to sensing, communication and movement functions as well as the impact of power saving mechanisms on the energetical balance of the node. The approach is applied to a marine sensing problem and is validated by comparing the model analysis results with experimental results achieved through an existing off-the-shelf sensor network simulator
Using Bayesian Networks to evaluate the trustworthiness of '2 out of 3' decision fusion mechanisms in multi-sensor applications
No full textThe use of smart-sensors to recognize automatically complex situations (anomalous behaviors, physical security threats, etc.) requires 'intelligent' methods to improve the trustworthiness of automatic decisions. Voting and consensus mechanisms can be employed whether supported by probabilistic formalisms to correlate event occurrence, to merge local events and to estimate the likelihood of overall decisions. This paper presents the results of a quantitative comparison of three different voting schemes based on Bayesian Networks. These models present a growing complexity and they are able to provide a trustworthiness estimation based on single nodes detection reliability in terms of false alarm probabilities
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