6,050 research outputs found
Open Source Software: All You Do Is Put It Together
The authors propose an infrastructure for rapidly prototyping applications from open source software components. The Adaptable Multi-Interface Communicator infrastructure (AMICO) is based on ideas of middleware platforms for component integration, but it focuses on pragmatic aspects of OSS integration, often absent from many existing integration platforms. The authors also identify the key requirements of middleware for rapid prototyping with OSS components and illustrate their approach through two examples in complex scenarios
Pattern-based software architecture for service-oriented software systems
Service-oriented architecture is a recent conceptual framework for service-oriented software platforms. Architectures are of great importance for the evolution of
software systems. We present a modelling and transformation technique for service-centric distributed software systems. Architectural configurations, expressed through hierarchical architectural patterns, form the core of a specification and transformation technique. Patterns on different levels of abstraction form transformation invariants that structure and constrain the transformation
process. We explore the role that patterns can play in architecture transformations in terms of functional properties, but also non-functional quality aspects
Quality-aware model-driven service engineering
Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects
ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box
character of services
Processing Structured Hypermedia : A Matter of Style
With the introduction of the World Wide Web in the early nineties, hypermedia has become the uniform interface to the wide variety of information sources available over the Internet. The full potential of the Web, however, can only be realized by building on the strengths of its underlying research fields. This book describes the areas of hypertext, multimedia, electronic publishing and the World Wide Web and points out fundamental similarities and differences in approaches towards the processing of information. It gives an overview of the dominant models and tools developed in these fields and describes the key interrelationships and mutual incompatibilities. In addition to a formal specification of a selection of these models, the book discusses the impact of the models described on the software architectures that have been developed for processing hypermedia documents. Two example hypermedia architectures are described in more detail: the DejaVu object-oriented hypermedia framework, developed at the VU, and CWI's Berlage environment for time-based hypermedia document transformations
Adaptivity in High-Performance Embedded Systems: a Reactive Control Model for Reliable and Flexible Design
International audienceSystem adaptivity is increasingly demanded in high-performance embedded systems, particularly in multimedia System-on-Chip (SoC), due to growing Quality of Service requirements. This paper presents a reactive control model that has been introduced in Gaspard, our framework dedicated to SoC hardware/software co-design. This model aims at expressing adaptivity as well as reconfigurability in systems performing data-intensive computations. It is generic enough to be used for description in the different parts of an embedded system, e.g. specification of how different data-intensive algorithms can be chosen according to some computation modes at the functional level; expression of how hardware components can be selected via the usage of a library of Intellectual Properties (IPs) according to execution performances. The transformation of this model towards synchronous languages is also presented, in order to allow an automatic code generation usable for formal verification, based of techniques such as model checking and controller synthesis as illustrated in the paper. This work, based on Model-Driven Engineering and the standard UML MARTE profile, has been implemented in Gaspard
Coarse-grained reconfigurable array architectures
Coarse-Grained Reconfigurable Array (CGRA) architectures accelerate the same inner loops that benefit from the high ILP support in VLIW architectures. By executing non-loop code on other cores, however, CGRAs can focus on such loops to execute them more efficiently. This chapter discusses the basic principles of CGRAs, and the wide range of design options available to a CGRA designer, covering a large number of existing CGRA designs. The impact of different options on flexibility, performance, and power-efficiency is discussed, as well as the need for compiler support. The ADRES CGRA design template is studied in more detail as a use case to illustrate the need for design space exploration, for compiler support and for the manual fine-tuning of source code
Automatic Environmental Sound Recognition: Performance versus Computational Cost
In the context of the Internet of Things (IoT), sound sensing applications
are required to run on embedded platforms where notions of product pricing and
form factor impose hard constraints on the available computing power. Whereas
Automatic Environmental Sound Recognition (AESR) algorithms are most often
developed with limited consideration for computational cost, this article seeks
which AESR algorithm can make the most of a limited amount of computing power
by comparing the sound classification performance em as a function of its
computational cost. Results suggest that Deep Neural Networks yield the best
ratio of sound classification accuracy across a range of computational costs,
while Gaussian Mixture Models offer a reasonable accuracy at a consistently
small cost, and Support Vector Machines stand between both in terms of
compromise between accuracy and computational cost
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