K8-Scalar: a workbench to compare autoscalers for container-orchestrated services (Artifact)

This artifact is an easy-to-use and extensible workbench exemplar, named K8-Scalar, which allows researchers to implement and evaluate different self-adaptive approaches to autoscaling container-orchestrated services. The workbench is based on Docker, a popular technology for easing the deployment of containerized software that also has been positioned as an enabler for reproducible research. The workbench also relies on a container orchestration framework: Kubernetes (K8s), the de-facto industry standard for orchestration and monitoring of elastically scalable container-based services. Finally, it integrates and extends Scalar, a generic testbed for evaluating the scalability of large-scale systems with support for evaluating the performance of autoscalers for database clusters. The associated scholarly paper presents (i) the architecture and implementation of K8-Scalar and how a particular autoscaler can be plugged in, (ii) sketches the design of a Riemann-based autoscaler for database clusters, (iii) illustrates how to design, setup and analyze a series of experiments to configure and evaluate the performance of this autoscaler for a particular database (i.e., Cassandra) and a particular workload type, (iv) and validates the effectiveness of K8-scalar as a workbench for accurately comparing the performance of different auto-scaling strategies. Future work includes extending K8-Scalar with an improved research data management repository

The OCareCloudS project: toward organizing care through trusted cloud services

The increasing elderly population and the shift from acute to chronic illness makes it difficult to care for people in hospitals and rest homes. Moreover, elderly people, if given a choice, want to stay at home as long as possible. In this article, the methodologies to develop a cloud-based semantic system, offering valuable information and knowledge-based services, are presented. The information and services are related to the different personal living hemispheres of the patient, namely the daily care-related needs, the social needs and the daily life assistance. Ontologies are used to facilitate the integration, analysis, aggregation and efficient use of all the available data in the cloud. By using an interdisciplinary research approach, where user researchers, (ontology) engineers, researchers and domain stakeholders are at the forefront, a platform can be developed of great added value for the patients that want to grow old in their own home and for their caregivers

Towards Managing Variability in the Safety Design of an Automotive Hall Effect Sensor

ABSTRACT This paper discusses the merits and challenges of adopting software product line engineering (SPLE) as the main development process for an automotive Hall Effect sensor. This versatile component is integrated into a number of automotive applications with varying safety requirements (e.g., windshield wipers and brake pedals). This paper provides a detailed explanation as to why the process of safety assessment and verification of the Hall Effect sensor is currently cumbersome and repetitive: it must be repeated entirely for every automotive application in which the sensor is to be used. In addition, no support is given to the engineer to select and configure the appropriate safety solutions and to explain the safety implications of his decisions. To address these problems, we present a tailored SPLEbased approach that combines model-driven development with advanced model composition techniques for applying and reasoning about specific safety solutions. In addition, we provide insights about how this approach can reduce the overall complexity, improve reusability, and facilitate safety assessment of the Hall Effect sensor

Towards Managing Variability in the Safety Design of an Automotive Hall Effect Sensor

International audienceThis paper discusses the merits and challenges of adopting software product line engineering (SPLE) as the main development process for an automotive Hall Effect sensor. This versatile component is integrated into a number of automotive applications with varying safety requirements (e.g., windshield wipers and brake pedals). This paper provides a detailed explanation as to why the process of safety assessment and verification of the Hall Effect sensor is currently cumbersome and repetitive:~it must be repeated entirely for every automotive application in which the sensor is to be used. In addition, no support is given to the engineer to select and configure the appropriate safety solutions and to explain the safety implications of his decisions. To address these problems, we present a tailored SPLE-based approach that combines model-driven development with advanced model composition techniques for applying and reasoning about specific safety solutions. In addition, we provide insights about how this approach can reduce the overall complexity, improve reusability, and facilitate safety assessment of the Hall Effect sensor

Modularizing early architectural assumptions in scenario-based requirements

Abstract. Early architectural assumptions (EAAs) are initial assumptions about the architectural solution that are made already during requirements elicitation. Such EAAs are inherently present when applying requirements engineering methods and techniques situated at the transition to architecture, for example those adhering to the Twin Peaks model to software engineering. Abstract. In the current state-of-the-art, early architectural assumptions (EAAs) are documented implicitly, and they are tangled within and scattered across heterogeneous requirement artifacts. This makes it hard to distinguish EAAs from actual requirements, analyze their relevance, and bring them in relation to architectural decisions taken in later development stages. As a consequence, early development activities in the transition to architecture are hindered by the lack of explicit support for EAAs. Abstract. In this paper, we address this problem in the context of scenario-based requirements (use cases and quality attribute scenarios). We present a system meta-model for EAAs, and provide an aspect-oriented requirements language that allows the instantiation of EAAs in terms of use case-level pointcuts. We employ our prototype implementation of above-mentioned techniques to evaluate and illustrate the benefits of making EAAs explicit in the early stages of development, specifically in terms of modularity and requirements navigability.status: accepte

On the role of early architectural assumptions in quality attribute scenarios: a qualitative and quantitative study

Architectural assumptions are fundamentally different from architectural decisions because they can not be traced directly to requirements, nor to domain, technical or environmental constraints; they represent conditions under which the designed solution is expected to be valid. Early architectural assumptions are similar in nature, with the key difference that they are not made during architectural design but during requirement elicitation, not by the software architect (a solution-oriented stakeholder), but by the requirements engineer (a problem-oriented stakeholder). They represent initial assumptions about the system’s architecture, and allow the requirements engineer to be more precise in documenting the requirements of the system. The role of early architectural assumptions in the current practice of quality attribute scenario elicitation and related development activities in the transition to architecture is unknown and under-investigated. In this paper, we present the results of an exploratory study that focuses on the role and nature of these assumptions in the early development stages. We studied a reasonably large set of quality attribute scenarios for a realistic industrial case, a smart metering system. Our study (i) confirms that quality attribute scenario elicitation in practice does rely heavily on early architectural assumptions, and (ii) shows that they do influence the perceived quality of the requirements body as a whole, in some cases positively, in other cases negatively. These findings provide empirical arguments in favor of making such assumptions explicit already during the requirements elicitation activities. Especially in the context of iterative software development methodologies such as the Twin Peaks model, a well-defined and -documented set of assumptions could smoothen the transition between successive development iterations.status: publishe

A descriptive study of assumptions made in LINDDUN privacy threat elicitation

status: publishe