Service management has been a hot topic in the research community for the last couple of years. However, due to the complexity of this research area, no commonly accepted definition of the terms service, service management, and the associated management tasks has evolved yet. This paper contributes to the ongoing process of defining these terms by proposing a top-down oriented and systematic methodology that is used to analyze and identify the necessary actors and the corresponding inter-and intra-organizational relationships. Then, a generic service model is introduced that defines commonly needed servicerelated terms, concepts and structuring rules in a general and unambiguous way. Since most of the work that is being presented here is still in flux, the service model is finally used to identify and structure open research questions.
Abstract. The promise of policy-based management is lessened by the risk of conflicts between policies. Even with careful conception of the policies it is difficult if not impossible to avoid conflicts completely. However, it is in principle possible to detect and resolve conflicts either statically or at runtime. Taking advantage of existing managed systems models it is even possible to detect and resolve policy conflicts not addressed until now. In this paper we present a generic approach to automated policy conflict detection based on existing knowledge about a managed system. We describe a methodology to derive conflict definitions from invariants of managed systems models, and show how these can be used to detect and resolve policy conflicts automatically.
Systems are becoming increasingly more adaptive, using techniques like machine learning to enhance their behavior on their own rather than only through human developers programming them. We analyze the impact the advent of these new techniques has on the discipline of rigorous software engineering, especially on the issue of quality assurance. To this end, we provide a general description of the processes related to machine learning and embed them into a formal framework for the analysis of adaptivity, recognizing that to test an adaptive system a new approach to adaptive testing is necessary. We introduce scenario coevolution as a design pattern describing how system and test can work as antagonists in the process of software evolution. While the general pattern applies to large-scale processes (including human developers further augmenting the system), we show all techniques on a smaller-scale example of an agent navigating a simple smart factory. We point out new aspects in software engineering for adaptive systems that may be tackled naturally using scenario coevolution. This work is a substantially extended take on Gabor et al. (International symposium on leveraging applications of formal methods, Springer, pp 137-154, 2018).
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