Cache-Aware Memory Manager for Optimistic Simulations

Parallel Discrete Event Simulation is a well known technique for executing complex general-purpose simulations where models are described as objects the interaction of which is expressed through the generation of impulsive events. In particular, Optimistic Simulation allows full exploitation of the available computational power, avoiding the need to compute safety properties for the events to be executed. Optimistic Simulation platforms internally rely on several data structures, which are meant to support operations aimed at ensuring correctness, inter-kernel communication and/or event scheduling. These housekeeping and management operations access them according to complex patterns, commonly suffering from misuse of memory caching architectures. In particular, operations like log/restore access data structures on a periodic basis, producing the replacement of in-cache buffers related to the actual working set of the application logic, producing a non-negligible performance drop. In this work we propose generally-applicable design principles for a new memory management subsystem targeted at Optimistic Simulation platforms which can face this issue by wisely allocating memory buffers depending on their actual future access patterns, in order to enhance event-execution memory locality. Additionally, an application-transparent implementation within ROOT-Sim, an open-source generalpurpose optimistic simulation platform, is presented along with experimental results testing our proposal

An Object-Oriented Framework for Designing Reusable and Maintainable DEVS Models using Design Patterns

Design patterns are well practices to share software development experiences. These patterns allow enhancing reusability, readability and maintainability of architecture and code of software applications. As simulation applies computerized models to produce traces in order to obtain results and conclusions, designers of simulation explored design patterns to make the simulation code more reusable, more readable and easy to maintain, in addition to design complex software oriented simulation modeling. In DEVS (Discrete Event System specification), the designers have successfully designed simulations, frameworks, tools, etc. However, some issues remain still open and should be explored like how a piece of code that implements a set of states, events and transitions may be reused to design a new DEVS model? How may a DEVS model be extended to a new formalism? Etc. In this paper, we address these issues and we propose a set of patterns that may serve as guidelines to designers of DEVS models and its extensions and may contribute to the design of an operational simulation framework. These patterns are inspired partly by the available designs of DEVS community and software engineering developers

Discrete Event Systems based Design Patterns for Diagnosability Analysis of Automated Manufacturing Systems

The main goal of this thesis is to facilitate the process of industrial automated systems development applying formal methods to ensure the reliability of systems. A new formulation of distributed diagnosability problem in terms of Discrete Event Systems theory and automata framework is presented, which is then used to enforce the desired property of the system, rather then just verifying it. This approach tackles the state explosion problem with modeling patterns and new algorithms, aimed for verification of diagnosability property in the context of the distributed diagnosability problem. The concepts are validated with a newly developed software tool

To leave or not to leave? Understanding determinants of farmers' choices to remain in or abandon agri-environmental schemes

Effectiveness of Agri-Environmental Schemes (AESs) as tools to enhance the rural environment can be achieved not only by increasing uptake rates, but also by avoiding participating farmers abandoning the scheme once they are in. For this reason, it is important to also consider what affects farmers\u2019 decisions to remain in the scheme rather than leave it at the end of the contractual obligation. However, up to now, there has been very little on this issue in the literature. The paper offers a contribution to this by revealing the role of determinants like the farmer\u2019s and farm structural characteristics, farmer\u2019s learning process, neighbourhood effect and the impact of changes in the policy design on the farmer\u2019s decision to remain in the scheme over a long time scale. This is examined in a long-standing scheme in the case study area, the Veneto Region of Italy. The paper uses duration analysis and is based on longitudinal panel-data of the entire population of 2000-2015 adopters. By using only data available in official regional records, it also provides regional policy-makers with an operational tool that is useful to analyse the impact of their AES design changes. The results of the duration models show that a larger farm size, a younger farmer age, the succession in the family farm, and the farmer\u2019s positive attitude towards the environment, trigger longer durations in AES. Similarly, the impact of the accumulation of the farmer\u2019s experience in the scheme management, as well as the neighbourhood effect increase the probability of remaining. Lastly, the changes in policy tailoring and targeting also have a positive impact on maintaining the farmer in the scheme. The paper concludes by noting that duration analysis can deliver useful results in order to guide policy-makers in the effort to steer higher levels of farmers\u2019 persistence in the scheme and provides some recommendations for a more mature agro-environmental policy design

Modelling Reactive Multimedia: Design and Authoring

Multimedia document authoring is a multifaceted activity, and authoring tools tend to concentrate on a restricted set of the activities involved in the creation of a multimedia artifact. In particular, a distinction may be drawn between the design and the implementation of a multimedia artifact. This paper presents a comparison of three different authoring paradigms, based on the common case study of a simple interactive animation. We present details of its implementation using the three different authoring tools, MCF, Fran and SMIL 2.0, and we discuss the conclusions that may be drawn from our comparison of the three approaches

Impact of imperfect test sensitivity on determining risk factors : the case of bovine tuberculosis

Background Imperfect diagnostic testing reduces the power to detect significant predictors in classical cross-sectional studies. Assuming that the misclassification in diagnosis is random this can be dealt with by increasing the sample size of a study. However, the effects of imperfect tests in longitudinal data analyses are not as straightforward to anticipate, especially if the outcome of the test influences behaviour. The aim of this paper is to investigate the impact of imperfect test sensitivity on the determination of predictor variables in a longitudinal study. Methodology/Principal Findings To deal with imperfect test sensitivity affecting the response variable, we transformed the observed response variable into a set of possible temporal patterns of true disease status, whose prior probability was a function of the test sensitivity. We fitted a Bayesian discrete time survival model using an MCMC algorithm that treats the true response patterns as unknown parameters in the model. We applied our approach to epidemiological data of bovine tuberculosis outbreaks in England and investigated the effect of reduced test sensitivity in the determination of risk factors for the disease. We found that reduced test sensitivity led to changes to the collection of risk factors associated with the probability of an outbreak that were chosen in the ‘best’ model and to an increase in the uncertainty surrounding the parameter estimates for a model with a fixed set of risk factors that were associated with the response variable. Conclusions/Significance We propose a novel algorithm to fit discrete survival models for longitudinal data where values of the response variable are uncertain. When analysing longitudinal data, uncertainty surrounding the response variable will affect the significance of the predictors and should therefore be accounted for either at the design stage by increasing the sample size or at the post analysis stage by conducting appropriate sensitivity analyses