Models in the Cloud: Exploring Next Generation Environmental Software Systems

There is growing interest in the application of the latest trends in computing and data science methods to improve environmental science. However we found the penetration of best practice from computing domains such as software engineering and cloud computing into supporting every day environmental science to be poor. We take from this work a real need to re-evaluate the complexity of software tools and bring these to the right level of abstraction for environmental scientists to be able to leverage the latest developments in computing. In the Models in the Cloud project, we look at the role of model driven engineering, software frameworks and cloud computing in achieving this abstraction. As a case study we deployed a complex weather model to the cloud and developed a collaborative notebook interface for orchestrating the deployment and analysis of results. We navigate relatively poor support for complex high performance computing in the cloud to develop abstractions from complexity in cloud deployment and model configuration. We found great potential in cloud computing to transform science by enabling models to leverage elastic, flexible computing infrastructure and support new ways to deliver collaborative and open science

Teaching MDE through the Formal Verification of Process Models

International audienceModel Driven Engineering (MDE) and formal methods (FM) play a key role in the development of Safety Critical Systems (SCS). They promote user oriented abstraction and formal specification using Domain Specific Modeling Languages (DSML), early Validation and formal Verification (V&V) using efficient dedicated technologies and Automatic Code and Documentation Generation. Their combined use allow to improve system qualities and reduce development costs. However, in most computer science curriculae, both domains are usually taught independently. MDE is associated to practical software engineering and FM to theoretical computer science. This contribution relates a course about MDE for SCS development that bridges the gap between these domains. It describes the content of the course and provides the lessons learned from its teaching. It focuses on early formal verification using model checking of a DSML for development process modeling. MDE technologies are illustrated both on language engineering for CASE tool development and on development process modeling. The case study also highlights the unification power of MDE as it does not target traditional executable software

Detection of Fog Network Data Telemetry Using Data Plane Programming

Fog computing has been introduced to deliver Cloud-based services to the Internet of Things (IoT) devices. It locates geographically closer to IoT devices than Cloud networks and aims at offering latency-critical computation and storage to end-user applications. To leverage Fog computing for computational offloading from end-users, it is important to optimize resources in the Fog nodes dynamically. Provisioning requires knowledge of the current network state, thus, monitoring mechanisms play a significant role to conduct resource management in the network. To keep track of the state of devices, we use P4, a data-plane programming language, to describe data-plane abstraction of Fog network devices and collect telemetry without the intervention of the control plane or adding a big amount of overhead. In this paper, we propose a software-defined architecture with a programmable data plane for data telemetry detection that can be integrated into Fog network resource management. After the implementation of detecting data telemetry based on In-Band Network Telemetry (INT) within a Mininet simulation, we show the available features and preliminary Fog resource management based on the collected data telemetry and future telemetry-based traffic engineering possibilities

On the role of computer languages in scientific computing

International audienceScientific software are complex software systems. Their engineering involves various stakeholders using specific computer languages for defining artifacts at different abstraction levels and for different purposes. In this paper, we review the overall process leading to the development of scientific software, and discuss the role of computer languages in the definition of the different artifacts. We then provide guidelines to make informed decisions when the time comes to choose the computer languages to use when developing scientific software

A Health-care Application of Goal-driven Software Design

In this paper we focus on goal engineering by addressing issues such as goal elicitation, specification, structuring and operationalisation. Specification of business goals is regarded as a means to raise the level of abstraction (and automation) at which business logic is incorporated in model driven software design in the context of service oriented architectures. More specifically, the proposed goal modelling approach consists of an abstract syntax (metamodel) and a concrete syntax (graphical notation) for the specification of business goals. We also proposed a framework for the goal-driven design of service-oriented software applications. In particular, we illustrate our approach by means of a case study carried out in the healthcare sector and we explain the role business goals (operationalised in the form of business rules) can play in software design. This research also outlines a number of areas that have significant research potential

Type Safe Extensible Programming

Software products evolve over time. Sometimes they evolve by adding new features, and sometimes by either fixing bugs or replacing outdated implementations with new ones. When software engineers fail to anticipate such evolution during development, they will eventually be forced to re-architect or re-build from scratch. Therefore, it has been common practice to prepare for changes so that software products are extensible over their lifetimes. However, making software extensible is challenging because it is difficult to anticipate successive changes and to provide adequate abstraction mechanisms over potential changes. Such extensibility mechanisms, furthermore, should not compromise any existing functionality during extension. Software engineers would benefit from a tool that provides a way to add extensions in a reliable way. It is natural to expect programming languages to serve this role. Extensible programming is one effort to address these issues. In this thesis, we present type safe extensible programming using the MLPolyR language. MLPolyR is an ML-like functional language whose type system provides type-safe extensibility mechanisms at several levels. After presenting the language, we will show how these extensibility mechanisms can be put to good use in the context of product line engineering. Product line engineering is an emerging software engineering paradigm that aims to manage variations, which originate from successive changes in software.Comment: PhD Thesis submitted October, 200

Research Questions for Validation and Verification in the Context of Model-Based Engineering

International audienceIn model-based engineering (MBE), the abstraction power of models is used to deal with the ever increasing complexity of modern software systems. As models play a central role in MBE-based development processes, for the adoption of MBE in practical projects it becomes indispensable to introduce rigorous methods for ensuring the correctness of the models. Consequently, much effort has been spent on developing and applying validation and verification (V&V) techniques for models. However, there are still many open challenges. In this paper, we shortly review the status quo of V&V techniques in MBE and derive a catalogue of open questions whose answers would contribute to successfully putting MBE into practice

A High-Level Programming Language for Modelling the Earth

Computational models based on the solution of partial differential equations (PDEs) play a key role in Earth systems simulations. The software implementing these models depends on the discretisation method, data structures and the computer architecture. For this reason, it is difficult for scientists to implement new models without strong software engineering skills. In this paper, we present a computational modeling language (escript) based on the object-oriented scripting language (Python). This language, is designed to implement PDE-based models with a high degree of abstraction from the underlying discretization techniques and their implementation. The main components of escript are the Data class objects which handle data with a spatial distribution and the linearPDE class which define linear PDEs to be solved in each step of a time integration or non-linear iteration scheme. As an example we will discuss the solution of the Lame equation and the implementation of a quasi-static model for crustal fault systems