131,147 research outputs found
Run-time Support to Manage Architectural Variability Speci ed with CVL
The execution context in which pervasive systems or mobile
computing run changes continuously. Hence, applications for these systems
should be adapted at run-time according to the current context.
In order to implement a context-aware dynamic reconfiguration service,
most approaches usually require to model at design-time both the list of
all possible configurations and the plans to switch among them. In this
paper we present an alternative approach for the automatic run-time generation
of application configurations and the reconfiguration plans. The
generated configurations are optimal regarding di erent criteria, such as
functionality or resource consumption (e.g. battery or memory). This is
achieved by: (1) modelling architectural variability at design-time using
Common Variability Language (CVL), and (2) using a genetic algorithm
that finds at run-time nearly-optimal configurations using the information
provided by the variability model. We also specify a case study
and we use it to evaluate our approach, showing that it is efficient and
suitable for devices with scarce resources.Campus de Excelencia Internacional Andalucia Tech y proyectos de investigaciĂłn TIN2008-01942, P09-TIC-5231 and INTER-TRUST FP7-317731
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
Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations
The large number of possible configurations of modern software-based systems,
combined with the large number of possible environmental situations of such
systems, prohibits enumerating all adaptation options at design time and
necessitates planning at run time to dynamically identify an appropriate
configuration for a situation. While numerous planning techniques exist, they
typically assume a detailed state-based model of the system and that the
situations that warrant adaptations are known. Both of these assumptions can be
violated in complex, real-world systems. As a result, adaptation planning must
rely on simple models that capture what can be changed (input parameters) and
observed in the system and environment (output and context parameters). We
therefore propose planning as optimization: the use of optimization strategies
to discover optimal system configurations at runtime for each distinct
situation that is also dynamically identified at runtime. We apply our approach
to CrowdNav, an open-source traffic routing system with the characteristics of
a real-world system. We identify situations via clustering and conduct an
empirical study that compares Bayesian optimization and two types of
evolutionary optimization (NSGA-II and novelty search) in CrowdNav
ASCR/HEP Exascale Requirements Review Report
This draft report summarizes and details the findings, results, and
recommendations derived from the ASCR/HEP Exascale Requirements Review meeting
held in June, 2015. The main conclusions are as follows. 1) Larger, more
capable computing and data facilities are needed to support HEP science goals
in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of
the demand at the 2025 timescale is at least two orders of magnitude -- and in
some cases greater -- than that available currently. 2) The growth rate of data
produced by simulations is overwhelming the current ability, of both facilities
and researchers, to store and analyze it. Additional resources and new
techniques for data analysis are urgently needed. 3) Data rates and volumes
from HEP experimental facilities are also straining the ability to store and
analyze large and complex data volumes. Appropriately configured
leadership-class facilities can play a transformational role in enabling
scientific discovery from these datasets. 4) A close integration of HPC
simulation and data analysis will aid greatly in interpreting results from HEP
experiments. Such an integration will minimize data movement and facilitate
interdependent workflows. 5) Long-range planning between HEP and ASCR will be
required to meet HEP's research needs. To best use ASCR HPC resources the
experimental HEP program needs a) an established long-term plan for access to
ASCR computational and data resources, b) an ability to map workflows onto HPC
resources, c) the ability for ASCR facilities to accommodate workflows run by
collaborations that can have thousands of individual members, d) to transition
codes to the next-generation HPC platforms that will be available at ASCR
facilities, e) to build up and train a workforce capable of developing and
using simulations and analysis to support HEP scientific research on
next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio
Automatic instantiation of abstract tests on specific configurations for large critical control systems
Computer-based control systems have grown in size, complexity, distribution
and criticality. In this paper a methodology is presented to perform an
abstract testing of such large control systems in an efficient way: an abstract
test is specified directly from system functional requirements and has to be
instantiated in more test runs to cover a specific configuration, comprising
any number of control entities (sensors, actuators and logic processes). Such a
process is usually performed by hand for each installation of the control
system, requiring a considerable time effort and being an error prone
verification activity. To automate a safe passage from abstract tests, related
to the so called generic software application, to any specific installation, an
algorithm is provided, starting from a reference architecture and a state-based
behavioural model of the control software. The presented approach has been
applied to a railway interlocking system, demonstrating its feasibility and
effectiveness in several years of testing experience
Feedback driven adaptive combinatorial testing
The configuration spaces of modern software systems are too large to test exhaustively. Combinatorial interaction testing (CIT) approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches is that they can cost-effectively exercise all system behaviors caused by the settings of t or fewer options. We conjecture, however, that in practice many such behaviors are not actually tested because of masking effects â failures that perturb execution so as to prevent some behaviors from being exercised. In this work we present a feedback-driven, adaptive, combinatorial testing approach aimed at detecting and working around masking effects. At each iteration we detect potential masking effects, heuristically isolate their likely causes, and then generate new covering arrays that allow previously masked combinations to be tested in the subsequent iteration. We empirically assess the effectiveness of the proposed approach on two large widely used open source software systems. Our results suggest that masking effects do exist and that our approach provides a promising and efficient way to work around them
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