139,159 research outputs found
Semantic-based policy engineering for autonomic systems
This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise
Category Theory and Model-Driven Engineering: From Formal Semantics to Design Patterns and Beyond
There is a hidden intrigue in the title. CT is one of the most abstract
mathematical disciplines, sometimes nicknamed "abstract nonsense". MDE is a
recent trend in software development, industrially supported by standards,
tools, and the status of a new "silver bullet". Surprisingly, categorical
patterns turn out to be directly applicable to mathematical modeling of
structures appearing in everyday MDE practice. Model merging, transformation,
synchronization, and other important model management scenarios can be seen as
executions of categorical specifications.
Moreover, the paper aims to elucidate a claim that relationships between CT
and MDE are more complex and richer than is normally assumed for "applied
mathematics". CT provides a toolbox of design patterns and structural
principles of real practical value for MDE. We will present examples of how an
elementary categorical arrangement of a model management scenario reveals
deficiencies in the architecture of modern tools automating the scenario.Comment: In Proceedings ACCAT 2012, arXiv:1208.430
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
A Framework for Evaluating Model-Driven Self-adaptive Software Systems
In the last few years, Model Driven Development (MDD), Component-based
Software Development (CBSD), and context-oriented software have become
interesting alternatives for the design and construction of self-adaptive
software systems. In general, the ultimate goal of these technologies is to be
able to reduce development costs and effort, while improving the modularity,
flexibility, adaptability, and reliability of software systems. An analysis of
these technologies shows them all to include the principle of the separation of
concerns, and their further integration is a key factor to obtaining
high-quality and self-adaptable software systems. Each technology identifies
different concerns and deals with them separately in order to specify the
design of the self-adaptive applications, and, at the same time, support
software with adaptability and context-awareness. This research studies the
development methodologies that employ the principles of model-driven
development in building self-adaptive software systems. To this aim, this
article proposes an evaluation framework for analysing and evaluating the
features of model-driven approaches and their ability to support software with
self-adaptability and dependability in highly dynamic contextual environment.
Such evaluation framework can facilitate the software developers on selecting a
development methodology that suits their software requirements and reduces the
development effort of building self-adaptive software systems. This study
highlights the major drawbacks of the propped model-driven approaches in the
related works, and emphasise on considering the volatile aspects of
self-adaptive software in the analysis, design and implementation phases of the
development methodologies. In addition, we argue that the development
methodologies should leave the selection of modelling languages and modelling
tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition,
self-adaptive application, context oriented software developmen
Data integration through service-based mediation for web-enabled information systems
The Web and its underlying platform technologies have often been used to integrate existing software and information systems. Traditional techniques for data representation and transformations between documents are not sufficient to support a flexible and maintainable data integration solution that meets the requirements of modern complex Web-enabled software and information systems. The difficulty
arises from the high degree of complexity of data structures, for example in business and technology applications, and from the constant change of data and its
representation. In the Web context, where the Web platform is used to integrate different organisations or software systems, additionally the problem of heterogeneity
arises. We introduce a specific data integration solution for Web applications such as Web-enabled information systems. Our contribution is an integration technology
framework for Web-enabled information systems comprising, firstly, a data integration technique based on the declarative specification of transformation rules and the construction of connectors that handle the integration and, secondly, a mediator architecture based on information services and the constructed connectors to handle the integration process
Model-driven performance evaluation for service engineering
Service engineering and service-oriented architecture as an
integration and platform technology is a recent approach to software systems integration. Software quality aspects such as performance are of central importance for the integration of heterogeneous, distributed service-based systems. Empirical performance evaluation is a process of
measuring and calculating performance metrics of the implemented software. We present an approach for the empirical, model-based performance evaluation of services and service compositions in the context of model-driven service engineering. Temporal databases theory is utilised
for the empirical performance evaluation of model-driven developed service systems
Collaborative Verification-Driven Engineering of Hybrid Systems
Hybrid systems with both discrete and continuous dynamics are an important
model for real-world cyber-physical systems. The key challenge is to ensure
their correct functioning w.r.t. safety requirements. Promising techniques to
ensure safety seem to be model-driven engineering to develop hybrid systems in
a well-defined and traceable manner, and formal verification to prove their
correctness. Their combination forms the vision of verification-driven
engineering. Often, hybrid systems are rather complex in that they require
expertise from many domains (e.g., robotics, control systems, computer science,
software engineering, and mechanical engineering). Moreover, despite the
remarkable progress in automating formal verification of hybrid systems, the
construction of proofs of complex systems often requires nontrivial human
guidance, since hybrid systems verification tools solve undecidable problems.
It is, thus, not uncommon for development and verification teams to consist of
many players with diverse expertise. This paper introduces a
verification-driven engineering toolset that extends our previous work on
hybrid and arithmetic verification with tools for (i) graphical (UML) and
textual modeling of hybrid systems, (ii) exchanging and comparing models and
proofs, and (iii) managing verification tasks. This toolset makes it easier to
tackle large-scale verification tasks
Taming the interoperability challenges of complex IoT systems
of communication protocols and data formats; hence ensuring diverse devices can interoperate with one another remains a significant challenge. Model-driven development and testing solutions have been proposed as methods to aid software developers achieve interoperability compliance in the face of this increasing complexity. However, current approaches often involve complicated and domain specific models (e.g. web services described by WSDL). In this paper, we explore a lightweight, middleware independent, model-driven development framework to help developers tame the challenges of composing IoT services that interoperate with one another. The framework is based upon two key contributions: i) patterns of interoperability behaviour, and ii) a software framework to monitor and reason about interoperability success or failure. We show using a case-study from the FI-WARE Future Internet Service domain that this interoperability framework can support non-expert developers address interoperability challenges. We also deployed tools built atop the framework and made them available in the XIFI large-scale FI-PPP test environment
Evolutionary model type selection for global surrogate modeling
Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist ( Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm ( heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type
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