Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Product family design based on a design reuse model

Ph.DDOCTOR OF PHILOSOPH

Hierarchic genetic strategy with maturing as a generic tool for multiobjective optimization

In this paper we introduce the Multiobjective Optimization Hierarchic Genetic Strategy with maturing (MO-mHGS), a meta-algorithm that performs evolutionary optimization in a hierarchy of populations. The maturing mechanism improves growth and reduces redundancy. The performance of MO-mHGS with selected state-of-the-art multiobjective evolutionary algorithms as internal algorithms is analysed on benchmark problems and their modifications for which single fitness evaluation time depends on the solution accuracy. We compare the proposed algorithm with the Island Model Genetic Algorithm as well as with single-deme methods, and discuss the impact of internal algorithms on the MO-mHGS meta-algorithm. © 2016 Elsevier B.V

D5.5 Impacts of future scenarios on the resilience of farming systems across the EU assessed with quantitative and qualitative methods

For improving the sustainability and resilience of EU farming systems, it is important to assess their likely responses to future challenges under future scenarios. In the SURE-Farm project, a five-steps framework was developed to assess the resilience of farming systems. The steps are the following: 1) characterizing the farming system (resilience of what?), 2) identifying the challenges (resilience to what?), 3) identifying the desired functions (resilience for which purpose?), 4) assessing resilience capacities, and 5) assessing resilience attributes. For assessing the resilience of future farming systems, we took the same approach as for current farming systems, with the addition that future challenges were placed in the context of a set of possible future scenarios, (i.e., Eur-Agri-SSP scenarios). We evaluated future resilience in 11 case studies across the EU, using a soft coupling of different qualitative and quantitative approaches. The qualitative approach was FoPIA-SUREFarm 2, a participatory approach in which stakeholders identified critical thresholds for current systems, evaluated expected system performance when these thresholds would be exceeded, envisaged alternative future states of the systems (and their impact on indicators and resilience attributes), as well as strategies to get there. Quantitative approaches included models simulating the behavior of the systems under some specific challenges and scenarios. The models differed in assumptions and aspects of the farming systems described: Ecosystem Service modelling focused on the biophysical level (considering land cover and nitrogen fluxes), AgriPoliS considered, with an agent-based approach, socio-economic processes and interactions within the farming system, and System Dynamics, taking a holistic approach, explored some of the feedback loops mechanisms influencing the systems resilience from both a qualitative and quantitative approach. Each method highlighted different aspects of the farming systems. For each case study, results coming from different methods were discussed and compared. The FoPIA-SURE-Farm 2 assessment highlighted that most farming systems are close to critical thresholds, primarily for system challenges, but also for system indicators and resilience attributes. System indicators related to food production and economic viability were often considered to be close to critical thresholds. The alternative systems proposed by stakeholders are mostly adaptations of the current system and not transformations. In most case studies, both the current and alternative systems are moderately compatible with 'Eur-Agri-SSP1 – Agriculture on sustainable paths’, but little with other Eur-Agri-SSPs’. From the point of view of ecosystem services and nitrogen fluxes, the more resilient case studies are those able to provide multiple services at the same time (e.g., hazelnut cultivations in Italy and vegetable and fruit cultivation in Poland, able to provide good levels of both food production and carbon storage) and those well connected with other neighbouring farming systems (e.g., the Dutch case study receiving manure by the livestock sectors). The System Dynamic simulation (applied quantitatively for the Dutch and French case study) highlighted the need to develop resources that can increase farmers’ flexibility (e.g., access to cheap credit, local research and development, and local market). It also showed that innovation, networks, and cooperation contribute to building resilience against economic disturbances while highlighting the challenges for building resilience to environmental threats. From the application of AgriPoliS to the German case study it was concluded that changes in direct payment schemes not only affect the farm size structure, but also the functions of the farming system itself and therefore its resilience. The report showed complementarity between different methods and, above all, between quantitative and qualitative approaches. Qualitative approaches are needed for interaction with stakeholders, understand perceptions of stakeholders, consider available knowledge on all aspects of the farming system, including social dimensions, and perform a good basis for developing and parameterizing quantitative models. Quantitative methods allow quantifying the consequences of mental models, operationalizing the impact of stresses and strategies to tackle them and help to unveil unintended consequences, but are limited in their reach. Both are needed to assess resilience of farming systems and suggest strategies for improvement and to help stakeholders to wider their views regarding potential challenges and ways to tackle them

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions