Mapping AADL models to a repository of multiple schedulability analysis techniques

To fill the gap between the modeling of real-time systems and the scheduling analysis, we propose a framework that supports seamlessly the two aspects: 1) modeling a system using a methodology, in our case study, the Architecture Analysis and Design Language (AADL), and 2) helping to easily check temporal requirements (schedulability analysis, worst-case response time, sensitivity analysis, etc.). We introduce an intermediate framework called MoSaRT, which supports a rich semantic concerning temporal analysis. We show with a case study how the input model is transformed into a MoSaRT model, and how our framework is able to generate the proper models as inputs to several classic temporal analysis tools

Governance for sustainability: learning from VSM practice

Purpose – While there is some agreement on the usefulness of systems and complexity approaches to tackle the sustainability challenges facing the organisations and governments in the twenty-first century, less is clear regarding the way such approaches can inspire new ways of governance for sustainability. The purpose of this paper is to progress ongoing research using the Viable System Model (VSM) as a meta-language to facilitate long-term sustainability in business, communities and societies, using the “Methodology to support self-transformation”, by focusing on ways of learning about governance for sustainability. Design/methodology/approach – It summarises core self-governance challenges for long-term sustainability, and the organisational capabilities required to face them, at the “Framework for Assessing Sustainable Governance”. This tool is then used to analyse capabilities for governance for sustainability at three real situations where the mentioned Methodology inspired bottom up processes of self-organisation. It analyses the transformations decided from each organisation, in terms of capabilities for sustainable governance, using the suggested Framework. Findings – Core technical lessons learned from using the framework are discussed, include the usefulness of using a unified language and tool when studying governance for sustainability in differing types and scales of case study organisations. Research limitations/implications – As with other exploratory research, it reckons the convenience for further development and testing of the proposed tools to improve their reliability and robustness. Practical implications – A final conclusion suggests that the suggested tools offer a useful heuristic path to learn about governance for sustainability, from a VSM perspective; the learning from each organisational self-transformation regarding governance for sustainability is insightful for policy and strategy design and evaluation; in particular the possibility of comparing situations from different scales and types of organisations. Originality/value – There is very little coherence in the governance literature and the field of governance for sustainability is an emerging field. This piece of exploratory research is valuable as it presents an effective tool to learn about governance for sustainability, based in the “Methodology for Self-Transformation”; and offers reflexions on applications of the methodology and the tool, that contribute to clarify the meaning of governance for sustainability in practice, in organisations from different scales and types

A Modeling Approach based on UML/MARTE for GPU Architecture

Nowadays, the High Performance Computing is part of the context of embedded systems. Graphics Processing Units (GPUs) are more and more used in acceleration of the most part of algorithms and applications. Over the past years, not many efforts have been done to describe abstractions of applications in relation to their target architectures. Thus, when developers need to associate applications and GPUs, for example, they find difficulty and prefer using API for these architectures. This paper presents a metamodel extension for MARTE profile and a model for GPU architectures. The main goal is to specify the task and data allocation in the memory hierarchy of these architectures. The results show that this approach will help to generate code for GPUs based on model transformations using Model Driven Engineering (MDE).Comment: Symposium en Architectures nouvelles de machines (SympA'14) (2011

Context-aware adaptation in DySCAS

DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met

E-Learning and microformats: a learning object harvesting model and a sample application

In order to support interoperability of learning tools and reusability of resources, this paper introduces a framework for harvesting learning objects from web-based content. Therefore, commonly-known web technologies are examined with respect to their suitability for harvesting embedded meta-data. Then, a lightweight application profile and a microformat for learning objects are proposed based on well-known learning object metadata standards. Additionally, we describe a web service which utilizes XSL transformation (GRDDL) to extract learning objects from different web pages, and provide a SQI target as a retrieval facility using a more complex query language called SPARQL. Finally, we outline the applicability of our framework on the basis of a search client employing the new SQI service for searching and retrieving learning objects

Weaving Rules into [email protected] for Embedded Smart Systems

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related conditions. The challenge at hand is that smart systems usually need to process thousands of updates to detect which rules need to be triggered, often even on restricted hardware like a Raspberry Pi. Despite various approaches have been investigated to efficiently check conditions on data models, they either assume to fit into main memory or rely on high latency persistence storage systems that severely damage the reactivity of smart systems. To tackle this challenge, we propose a novel composition process, which weaves executable rules into a data model with lazy loading abilities. We quantitatively show, on a smart building case study, that our approach can handle, at low latency, big sets of rules on top of large-scale data models on restricted hardware.Comment: pre-print version, published in the proceedings of MOMO-17 Worksho