An aspect-oriented and model-driven approach for managing dynamic variability

Constructing and executing distributed systems that can adapt to their operating context in order to sustain provided services and the service qualities are complex tasks. Managing adaptation of multiple, interacting services is particularly difficult since these services tend to be distributed across the system, interdependent and sometimes tangled with other services. Furthermore, the exponential growth of the number of potential system configurations derived from the variabilities of each service need to be handled. Current practices of writing low-level reconfiguration scripts as part of the system code to handle run time adaptation are both error prone and time consuming and make adaptive systems difficult to validate and evolve. In this paper, we propose to combine model driven and aspect oriented techniques to better cope with the complexities of adaptive systems construction and execution, and to handle the problem of exponential growth of the number of possible configurations. Combining these techniques allows us to use high level domain abstractions, simplify the representation of variants and limit the problem pertaining to the combinatorial explosion of possible configurations. In our approach we also use models at runtime to generate the adaptation logic by comparing the current configuration of the system to a composed model representing the configuration we want to reach. © 2008 Springer-Verlag Berlin Heidelberg.

Methodology for the model-driven development of self-managing systems

Based on recent advances in autonomic computing, we propose a methodology for the cost-effective development of self-managing systems starting from a model of the resources to be managed and using a general-purpose autonomic architecture.

Model-driven software adaptation:report on the workshop M-ADAPT at ECOOP 2007

This first edition of the workshop Model-driven Software Adaptation (M-ADAPT'07) took place in the Technische Universität Berlin with the International Conference ECOOP'07 in the beautiful and buzzing city of Berlin, on the 30th of July, 2007. The workshop was organized by Gordon Blair, Nelly Bencomo, and Robert France. Participants explored how to develop appropriate model-driven approaches to model, analyze, and validate the volatile properties of the behaviour of adaptive systems and its environments. This report gives an overview of the presentations as well as an account of the fruitful discussions that took place at M-ADAPT'07. © 2008 Springer-Verlag Berlin Heidelberg.

Run-time model evaluation for requirements model-driven self-adaptation

A self-adaptive system adjusts its configuration to tolerate changes in its operating environment. To date, requirements modeling methodologies for self-adaptive systems have necessitated analysis of all potential system configurations, and the circumstances under which each is to be adopted. We argue that, by explicitly capturing and modelling uncertainty in the operating environment, and by verifying and analysing this model at runtime, it is possible for a system to adapt to tolerate some conditions that were not fully considered at design time. We showcase in this paper our tools and research results. © 2012 IEEE.

Form follows function:model-driven engineering for clinical trials

We argue that, for certain constrained domains, elaborate model transformation technologies-implemented from scratch in general-purpose programming languages-are unnecessary for model-driven engineering; instead, lightweight configuration of commercial off-the-shelf productivity tools suffices. In particular, in the CancerGrid project, we have been developing model-driven techniques for the generation of software tools to support clinical trials. A domain metamodel captures the community's best practice in trial design. A scientist authors a trial protocol, modelling their trial by instantiating the metamodel; customized software artifacts to support trial execution are generated automatically from the scientist's model. The metamodel is expressed as an XML Schema, in such a way that it can be instantiated by completing a form to generate a conformant XML document. The same process works at a second level for trial execution: among the artifacts generated from the protocol are models of the data to be collected, and the clinician conducting the trial instantiates such models in reporting observations-again by completing a form to create a conformant XML document, representing the data gathered during that observation. Simple standard form management tools are all that is needed. Our approach is applicable to a wide variety of information-modelling domains: not just clinical trials, but also electronic public sector computing, customer relationship management, document workflow, and so on. © 2012 Springer-Verlag.

Genie:supporting the model driven development of reflective, component-based adaptive systems

Engineering adaptive software is an increasingly complex task. Here, we demonstrate Genie, a tool that supports the modelling, generation, and operation of highly reconfigurable, component-based systems. We showcase how Genie is used in two case-studies: i) the development and operation of an adaptive flood warning system, and ii) a service discovery application. In this context, adaptation is enabled by the Gridkit reflective middleware platform.

Market-driven strategies and the design of flexible production systems: evidence from the electronics industry

Links the concept of market-driven business strategies with the design of production systems. It draws upon the case of a firm which, during the last decade, changed its strategy from being “technology led” to “market driven”. The research, based on interdisciplinary fieldwork involving long-term participant observation, investigated the factors which contribute to the successful design and implementation of flexible production systems in electronics assembly. These investigations were conducted in collaboration with a major computer manufacturer, with other electronics firms being studied for comparison. The research identified a number of strategies and actions seen as crucial to the development of efficient flexible production systems, namely: effective integration of subsystems, development of appropriate controls and performance measures, compatibility between production system design and organization structure, and the development of a climate conducive to organizational change. Overall, the analysis suggests that in the electronics industry there exists an extremely high degree of environmental complexity and turbulence. This serves to shape the strategic, technical and social structures that are developed to match this complexity, examples of which are niche marketing, flexible manufacturing and employee harmonization.

Practically oriented design projects in mechatronics engineering:a case study of design experiences and outcomes

This paper discusses and presents a case study of a practically oriented design project together with a few examples of implemented design projects recently incorporated into an undergraduate system course at the mechatronics engineering department in Ah-Balqa’ Applied University. These projects have had a positive impact on both the department and its graduates. The focus of these projects is the design and implementation of processor-based system. This helps graduate students cross the border between hardware design and software design. Our case study discusses the research methodology adopted for the physical development of the project, the technology used in the project, and the design experiences and outcomes.

Revisiting goal-oriented models for self-aware systems-of-systems

Systems-of-systems (SoS) are systems resulted from the interaction among other independent constituent systems that collaborate to offer new functionalities towards accomplishing global missions. Each of these constituent systems accomplishes its individual missions and is able to contribute to the achievement of the global missions of the SoS, both being viewed as a set of associated goals. In the perspective of self-aware systems, SoS need to exhibit goal-awareness, i.e., They need to be aware of their own goals and of how their constituent systems contribute to their accomplishment. In this paper, we revisit goal-oriented concepts aiming at identifying and modeling goals at both SoS level and the constituent systems level. Moreover, we take advantage of such goal-oriented models to express the relationship among goals at these levels as well as to define how each constituent system can contribute to the accomplishment of global goals of an SoS. In addition, we shed light on important issues related to goal modeling in self-aware SoS to be addressed in future research.

Reconfigurable service-oriented architecture for autonomic computing

Almost a decade has passed since the objectives and benefits of autonomic computing were stated, yet even the latest system designs and deployments exhibit only limited and isolated elements of autonomic functionality. In previous work, we identified several of the key challenges behind this delay in the adoption of autonomic solutions, and proposed a generic framework for the development of autonomic computing systems that overcomes these challenges. In this article, we describe how existing technologies and standards can be used to realise our autonomic computing framework, and present its implementation as a service-oriented architecture. We show how this implementation employs a combination of automated code generation, model-based and object-oriented development techniques to ensure that the framework can be used to add autonomic capabilities to systems whose characteristics are unknown until runtime. We then use our framework to develop two autonomic solutions for the allocation of server capacity to services of different priorities and variable workloads, thus illustrating its application in the context of a typical data-centre resource management problem.

Using architecture models to support the generation and operation of component-based adaptive systems

Modelling architectural information is particularly important because of the acknowledged crucial role of software architecture in raising the level of abstraction during development. In the MDE area, the level of abstraction of models has frequently been related to low-level design concepts. However, model-driven techniques can be further exploited to model software artefacts that take into account the architecture of the system and its changes according to variations of the environment. In this paper, we propose model-driven techniques and dynamic variability as concepts useful for modelling the dynamic fluctuation of the environment and its impact on the architecture. Using the mappings from the models to implementation, generative techniques allow the (semi) automatic generation of artefacts making the process more efficient and promoting software reuse. The automatic generation of configurations and reconfigurations from models provides the basis for safer execution. The architectural perspective offered by the models shift focus away from implementation details to the whole view of the system and its runtime change promoting high-level analysis. © 2009 Springer Berlin Heidelberg.

Dynamic variability in adaptive systems

One of the reasons for using variability in the software product line (SPL) approach (see Apel et al., 2006; Figueiredo et al., 2008; Kastner et al., 2007; Mezini & Ostermann, 2004) is to delay a design decision (Svahnberg et al., 2005). Instead of deciding on what system to develop in advance, with the SPL approach a set of components and a reference architecture are specified and implemented (during domain engineering, see Czarnecki & Eisenecker, 2000) out of which individual systems are composed at a later stage (during application engineering, see Czarnecki & Eisenecker, 2000). By postponing the design decisions in such a manner, it is possible to better fit the resultant system in its intended environment, for instance, to allow selection of the system interaction mode to be made after the customers have purchased particular hardware, such as a PDA vs. a laptop. Such variability is expressed through variation points which are locations in a software-based system where choices are available for defining a specific instance of a system (Svahnberg et al., 2005). Until recently it had sufficed to postpone committing to a specific system instance till before the system runtime. However, in the recent years the use and expectations of software systems in human society has undergone significant changes.Today's software systems need to be always available, highly interactive, and able to continuously adapt according to the varying environment conditions, user characteristics and characteristics of other systems that interact with them. Such systems, called adaptive systems, are expected to be long-lived and able to undertake adaptations with little or no human intervention (Cheng et al., 2009). Therefore, the variability now needs to be present also at system runtime, which leads to the emergence of a new type of system: adaptive systems with dynamic variability.

A goal-based modeling approach to develop requirements of an adaptive system with environmental uncertainty

Dynamically adaptive systems (DASs) are intended to monitor the execution environment and then dynamically adapt their behavior in response to changing environmental conditions. The uncertainty of the execution environment is a major motivation for dynamic adaptation; it is impossible to know at development time all of the possible combinations of environmental conditions that will be encountered. To date, the work performed in requirements engineering for a DAS includes requirements monitoring and reasoning about the correctness of adaptations, where the DAS requirements are assumed to exist. This paper introduces a goal-based modeling approach to develop the requirements for a DAS, while explicitly factoring uncertainty into the process and resulting requirements. We introduce a variation of threat modeling to identify sources of uncertainty and demonstrate how the RELAX specification language can be used to specify more flexible requirements within a goal model to handle the uncertainty. © 2009 Springer Berlin Heidelberg.

Models@run.time:foundations, applications, and roadmaps

Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia. However, software models also have the potential to be used at runtime, to monitor and verify particular aspects of runtime behavior, and to implement self-* capabilities (e.g., adaptation technologies used in self-healing, self-managing, self-optimizing systems). A key benefit of using models at runtime is that they can provide a richer semantic base for runtime decision-making related to runtime system concerns associated with autonomic and adaptive systems. This book is one of the outcomes of the Dagstuhl Seminar 11481 on models@run.time held in November/December 2011, discussing foundations, techniques, mechanisms, state of the art, research challenges, and applications for the use of runtime models. The book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area. The roadmap papers provide insights to key features of the use of runtime models and identify the following research challenges: the need for a reference architecture, uncertainty tackled by runtime models, mechanisms for leveraging runtime models for self-adaptive software, and the use of models at runtime to address assurance for self-adaptive systems.

A low mortality, high morbidity Reduced Intensity Status Epilepticus (RISE) model of epilepsy and epileptogenesis in the rat

Animal models of acquired epilepsies aim to provide researchers with tools for use in understanding the processes underlying the acquisition, development and establishment of the disorder. Typically, following a systemic or local insult, vulnerable brain regions undergo a process leading to the development, over time, of spontaneous recurrent seizures. Many such models make use of a period of intense seizure activity or status epilepticus, and this may be associated with high mortality and/or global damage to large areas of the brain. These undesirable elements have driven improvements in the design of chronic epilepsy models, for example the lithium-pilocarpine epileptogenesis model. Here, we present an optimised model of chronic epilepsy that reduces mortality to 1% whilst retaining features of high epileptogenicity and development of spontaneous seizures. Using local field potential recordings from hippocampus in vitro as a probe, we show that the model does not result in significant loss of neuronal network function in area CA3 and, instead, subtle alterations in network dynamics appear during a process of epileptogenesis, which eventually leads to a chronic seizure state. The model’s features of very low mortality and high morbidity in the absence of global neuronal damage offer the chance to explore the processes underlying epileptogenesis in detail, in a population of animals not defined by their resistance to seizures, whilst acknowledging and being driven by the 3Rs (Replacement, Refinement and Reduction of animal use in scientific procedures) principles.