Browsing by Author "Ylikotila, Henri"

Self-aware computing is an emerging research area, which aims to solve issues stemming from a combination of increasingly more complex systems and diverse operating conditions. It adapts the key concepts of human self-awareness to the computational context. These concepts are well established in cognitive science, psychology, social psychology and philosophy, but novel in the software engineering context. Self-aware systems are able to acquire information about their environment and their internal state. The obtained information is used to build knowledge through reasoning. This increasing knowledge is used for building an internal learning model, which enables the system to adapt its actions. Self-aware systems can autonomously navigate runtime changes in their goals and environmental conditions, thus enabling a high degree of adaptivity to changing conditions that are difficult to predict at design time. In contrast, traditional systems have to operate with a set of predefined rules that are reliant on the design time knowledge of the designer. This study aims to identify proposed software architecture solutions that are of value for both practitioners and researchers. The study was conducted as a systematic literature review, for which we have developed a repeatable review protocol in order to cover all relevant literature in the area. The review protocol was explicitly defined and applied rigorously. In our review we managed to extract several proposed architecture designs from the reviewed 9 primary studies. These solutions propose several solutions, such as reference architectures, architecture frameworks, and architectural patterns for designing self-aware systems. This study can be used to get an overview of state of the art software architecture designs for self-aware systems. Additionally, this study can provide support for finding future research direction regarding self-aware systems.